Data Futurology - Data Science, ML and AI From Top Industry Leaders

#44 Using Data Science to Actually Solve Problems with Caroline Worboys - Data Expert, Investor, Advisor, COO & Vice Chair

An episode of Data Futurology - Data Science, ML and AI From Top Industry Leaders

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By Felipe Flores
Data Futurology is data science from a human lens.
In Data Futurology, experienced Data Science Leaders from around the world tell us their stories, challenges and the lessons learned throughout their career.
We also ask them:
- What makes a great data scientist? What skills are required?
- How to become a great data science leader?
- How should I grow and get the most out of my team?
- What is a good data strategy? and how do I best implement it?
- What are interesting applications of ML/AI that I should be considering in my industry?
To find out more visit www.datafuturology.com
More places to listen

More places to listen

#73 Powering Change using AI with Alex Ermolaev – AI Leader
Alex Ermolaev has been involved in the software industry for 20 years, including AI-specific experience at Bell Labs, Microsoft, several startups and now Nvidia. He is currently a leading AI software developer and works with groundbreaking companies that are implementing incredible AI solutions across several domains. In this episode, Alex describes how he started in the data space. Early in his career, he got a chance to work on a lot of data and software products. Enjoy the show! We speak about: [01:50] How Alex started in the data space [04:55] Alex’s professional background [10:30] Working for the finance team at Microsoft [14:55] Business development skills [18:50] Challenges working with startups [22:10] Working at Nvidia [26:20] Successful and unsuccessful AI patterns [30:00] AI and collecting data [35:15] How to tackle data problems using AI [40:30] Exciting uses for AI [43:15] The execution of new AI programs [49:00] What Alex is most proud of [50:20] Be patient and invest in your knowledge Resources: Alex’s LinkedIn: https://www.linkedin.com/in/alexermolaev Quotes: “The best way to develop knowledge in any area is to experience it.” “It is easier to sit in an office and assume the world works in a certain way.” “Don’t be in startups because it’s cool, try and find a path that meets your own needs.” “Working with startups is a lot of broader outreach and helping the community understand what is possible.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
53:27
October 15, 2019
#72 Focusing on Simplification to Solve Data Science Roadblocks with Evan Shellshear – Head of Analytics
Evan Shellshear has been an entrepreneur for more than a decade, and throughout that time he has always loved getting his hands dirty with building products from scratch and then commercializing them. Evan has a passion for innovation and not just from a managerial perspective but also from a doing perspective. He has a Ph.D. in Game Theory, is published in fields computer graphics to politics, mathematics to manufacturing, and much more. Evan has founded or co-founded over half a dozen companies to commercialize different technologies. Enjoy the show! We speak about: [01:15] How Evan started in the world of data [09:45] Zoom out to solve technical roadblocks [12:10] Examples of how Evan zoomed out [14:25] Why is zooming out a challenge for data scientists? [18:00] Focus on simplification [22:45] Taking opportunities that present themselves [27:00] Measures of success during a project [31:00] The process of a case study [36:05] Getting users to adopt new technologies [40:00] Innovation Tools [47:20] Evan’s proudest moment [49:40] Challenges for the future of machine learning [51:30] Get soft skills Resources: Evan’s LinkedIn: https://www.linkedin.com/in/eshellshear/ Innovation Tools: https://amzn.to/2OrrAsj Quotes: “Take a step up and over to look at the problem in a new direction.” “It is in our human nature to overcomplicate things.” “I need to help the company understand what the true problem is.” “Take a low-risk approach to solve your client’s problem.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
53:56
October 8, 2019
#71 Creating Effective Data Science Presentations with Rachel Fojtik – Director of Analytics and Performance
Rachel Fojtik is an Experienced Senior leader in Analytics, influencing change in behaviour, company culture, and improvement with analytics. Managing high performing teams that deliver across a myriad of knowledge areas. She is passionate about delivering information that sees results, using collaborative design and development. A demonstrated history of setting up teams that deliver end to end business intelligence implementations. Cross-industry experience in healthcare, telecommunications, the financial services industry, travel and tourism, and energy. Enjoy the show! We speak about: [01:15] How Rachel started in the data space [08:40] The motivation behind Rachel’s trailblazing [11:30] The metrics Rachel was helping optimize [14:10] Working with the management director vs. operational work [16:45] Data matching at Diner’s Club [22:15] Using a minimalist view [24:45] Find the best way – don’t just stick with what you know [28:45] If something is well presented, it is more likely to be trusted [35:00] What is a product manager? [42:50] An organic governance in the workplace [46:15] Rachel’s role as Director of Analytics and Performance [53:00] Building and working on a network [54:10] Do what you’re passionate about Resources: Rachel’s LinkedIn: https://www.linkedin.com/in/rachel-fojtik-78321199/ Quotes: “I created an input tool where a user could design the layout of their input form.” “I’ve always tried to go with a minimalist view.” “If your presentation is way too busy, it is difficult to take a story from that information.” “Consider where the eye goes first when creating a presentation.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
56:30
October 1, 2019
#70 Making Black Box Models Explainable with Christoph Molnar – Interpretable Machine Learning Researcher
Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter. Enjoy the show! We speak about: [02:10] How Christoph started in the data space [09:25] Understanding what a researcher needs [15:15] Skills learned from software engineers [16:00] Statistical consulting [19:50] Labeling data [23:00] Christoph is pursuing his Ph.D. [29:00] Why is interpretable machine learning needed now? [31:00] Learning interpretability [33:50] Accumulated local effects (ALE) [37:00] Example-based explanations [39:15] Deep learning [43:35] The illustrations in Interpretable Machine Learning. [49:50] How Christoph maximizes the impact of his time Resources: Christoph’s LinkedIn: https://www.linkedin.com/in/christoph-molnar-63777189/ Christoph’s Website: https://christophm.github.io Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/ Quotes: “Always look at the process when labeling data.” “After each chapter of my book, I publish it and get feedback.” “I randomly read a lot of papers and structure the knowledge to fit them together.” “I express what I want easier with illustrations in my book.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
55:12
September 24, 2019
#69 Creating Cultural Transformations Using Data Science Leaders with Bülent Kiziltan – Head of Data Science & Analytics and Chief Data Scientist
Dr. Bülent Kiziltan is an AI executive and an accomplished scientist who uses artificial intelligence to create value in many business verticals and tackles diverse problems in disciplines ranging from the financial industry, healthcare, astrophysics, operations research, marketing, biology, engineering, hardware design, digital platforms, to art. He has worked at Harvard, NASA, and MIT in close collaboration with pioneers of their respective fields. In the past 15+ years, he has led data-driven efforts in R&D and built multifaceted strategies for the industry. He has been a data science leader at Harvard and the Head of Deep Learning at Aetna leading and mentoring more than 200 scientists. Enjoy the show! We speak about: [02:00] Bülent’s background [05:50] The transition from astrophysics to business [08:45] Data leaders need technical experience [12:45] Academics still need soft skills [19:20] What data science can offer organizations [23:50] Addressing causal inferences [25:30] Recommendations for implementing culture in the workplace [30:00] How a leader should balance priorities [36:10] Challenges Bülent currently faces in the industry [38:15] Hierarchy in the startup space [40:45] What Bülent loves about data science [42:45] Future data challenges Resources: Bülent’s Website: http://www.kiziltan.org/ Bülent’s LinkedIn: https://www.linkedin.com/in/bulentkiziltan/ Quotes: “Culturally, I was surprised by the mindset of business leaders.” “We asked individual members of the data science group to come up with their own ideas that can be implemented in the day-to-day business operations.” “A diverse team is critically important for the business.” “All companies will become AI companies in one way or another.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
48:19
September 17, 2019
#68 How To Build Award Winning Data Products with Nick Blewden – Head of Data Product Development
Nick loves playing with, analyzing and visualising data and gets a massive kick out of the change it can bring to people, businesses, and the world. At Lloyd's of London, Nick leads a team of great designers and developers helping people automate processes and get more insight from their data. He gets a buzz from saving others time and surprising them with what surprising insights lie in their data. Out of the office, Nick is keen to mentor or share his experiences and enjoys speaking at events or conferences. In this episode, Nick discusses some of his favorite projects and describes issues he has faced being part of various teams. When overcoming team obstacles, he listens to every person in the group. If you do not listen to people, then you cannot persuade someone that you are a good guy. Transparency is also essential; explain what knowledge you bring and the processes that you do. People can pick up on integrity, but they can also pick up on suspicion. Currently, Nick works at Lloyd's of London, the world's leading insurance market providing specialist insurance services to businesses in over 200 countries and territories. He has helped establish a vision and strategy for Business Intelligence within Lloyd's of London and external market published insight. He delivers automated online MI apps to a range of business functions through a roadmap of strategic change while creating a training structure to develop BI analysts across the business. Nick's data product development team has to work with other teams at Lloyd's of London to create the best products for their customers. They communicate with the innovation team to understand the research. They also work with all the different modelling teams to access their expertise and bounce ideas around with each other. Before working at Lloyd's of London, Nick was self-employed; it taught Nick a lot about customer service, collaboration, and teamwork. Later, Nick explains common mistakes in developing data products, winning global hackathons, and what excites him most about the future of data. Enjoy the show! We speak about: [01:10] How Nick started in the data space [07:00] The evolution of data warehousing [11:45] Nick’s favorite projects [14:45] Navigating team issues [15:30] Solving problems at Lloyd’s of London [20:00] Lloyd’s of London customers [25:40] Interaction with other teams [30:30] Working for yourself [35:45] Common mistakes in developing data products [38:50] Winning global hackathons [40:20] What excites Nick most about the future of data [45:45] Nick’s proudest moments Resources: Nick’s LinkedIn: https://www.linkedin.com/in/nicholasblewden Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
51:22
September 10, 2019
#67 12 Questions to Ask in Mentoring Sessions with Felipe Flores – Founder & Podcast Host
In this episode, Felipe talks about an aspect of leadership, the one-on-one mentorship and feedback session with people on your team. To start, ask your team member what is on their mind and in general, how have things been going? If they do not have anything pressing they want to discuss in the sessions, Felipe turns to set of twelve questions. The first question is what you are most proud of that you have done since we last caught up? These questions are designed as tools for the team member to recognize the need for self-assessment. What they say is not essential; really, the follow-up questions are more necessary to help them uncover themselves. Next, ask what the team member could have done better since the last time you talked. By allowing them to evaluate and think of improving continually, they can learn faster and become more efficient in their work. The next few questions require a broader perspective and ask about the team as a whole. It should be clear that everything is a team effort, and all member’s ideas are respected and heard by others on the team. After the team questions, head back to questions about the person and ask what they would like to work on or improve? Then, the next issue will take a lot of trust and rapport with your team member, ask what is one thing that is true that you think I do not want to hear? Question nine is how I can help you to do better? This question has taught Felipe that he is good at the big picture but needs to focus on the details and how the team might achieve the big picture. Finally, the last three questions are asking what they like best and least about the organization and if they are happy at the moment. Enjoy the show! We speak about: [01:35] Open-ended questions [02:15] What are you most proud of that you have done since we last caught up? [03:45] What could you have done better? [05:30] Questions about the team [10:00] What would you like to work on or improve? [11:40] What is one thing that is true that you think I do not want to hear? [13:10] How can I help you to do better? [15:00] What do you like the least about the team? [16:00] What do you like best about the team? [16:35] Are you happy at the moment? Resources: Saturday Night Live Quotes: “One of the best and quickest ways to learn is to evaluate your efforts continually.” “Understand the human behind the data scientist.” “You don’t need to be fixing every person’s problem, but everyone needs help every now and again.” Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
23:57
September 3, 2019
#66 How to Structure a Data Analytics Team with June Dershewitz – Director of Analytics
June Dershewitz has spent her career driving analytics strategies for major businesses. She's currently Director of Analytics at Twitch, the world's leading video platform and community for gamers (a subsidiary of Amazon). As an analytics practitioner, she builds and leads teams that focus on marketing analytics, product analytics, business intelligence, and data governance. In her prior life as a consultant, she was a member of the leadership team at Semphonic, a prominent analytics consultancy (now part of Ernst & Young). As a long-standing advocate of the analytics community, she was the co-founder of Web Analytics Wednesdays; she's also a Director Emeritus of the Digital Analytics Association and a current Advisory Board Member at Golden Gate University. She holds a BA in Mathematics from Reed College in Portland, Oregon. Enjoy the show! We speak about: [01:40] How June started in the data space [08:20] Solving problems in startups [09:45] Getting a holistic view in the workplace [11:20] Feeling unsure about owning a piece of work [15:30] Business intelligence skillsets for data scientists [19:35] Clear understanding of data roles in the workplace [20:55] An overview of June’s teams’ structures [27:10] Managing career transitions with the hub and spoke model [29:25] Assigning each person a technical buddy [32:10] The data quality journey [41:40] Evolution of data quality at Twitch [48:00] Becoming involved in the data science community [53:10] Other ways June stays involved in her communities [55:20] Advice for breaking into the data science field Resources: June’s LinkedIn: https://www.linkedin.com/in/jdersh Non-Invasive Data Governance: The Path of Least Resistance and Greatest Success Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program Quotes: “The thing about being a data person at that time was we just had to figure it out.” “I was the vice president of everything that needed to get done.” “At Twitch, we don’t have a clear definition of what a data engineer means.” “We chose to move to an organization model that is hub and spoke.” “Data governance can mean lots of things to lots of people.” Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
57:31
August 27, 2019
#65 Using the Love@Work Method to Improve Workplace Culture with Olivia Parr-Rud – Speaker, Award-Winning Author, and Data Scientist
Olivia is an internationally known thought-leader, speaker, best-selling and award-winning author, and a data scientist who focuses on the interplay between technology, corporate leadership, and personal growth and happiness.  Throughout her career, she has blended analytic tools and holistic organizational practices to deliver successful solutions for her clients. As a lifelong spiritual seeker, Olivia began to see patterns that revealed the importance of love as a driver of business success. In this episode, Olivia explains why she changed her major to statistics in grad school. Once she completed her degree, she joined a bank in San Francisco. Olivia built a model using logistic regression for the bank. It saved the company 17 million dollars a year in mail expense, making her an instant hero. Her desktop computer had a 500-megabyte hard drive when she was running SAS she couldn’t get into any other programs. Financial services had a vibrant climate for modelling because the behavioral data was so reliable. Behavioral data is so powerful because if a person has done something before, they are more likely to do it again. Enjoy the show! We speak about: [01:40] How Olivia started in the data space   [07:50] Data in the financial services industry  [08:50] Oliva’s career history   [13:35] Starting a consulting business  [17:10] Tips for explaining data science to non-technical people [18:30] Becoming a published author  [24:45] Learning about Holacracy   [29:00] Balancing Holacracy and teamwork  [31:40] Combing data and human skills  [40:20] The Love@Work Method [47:15] One of Oliva’s professional fails    [51:10] Using LEAP (love, energy, audacity, and proof) [54:30] Following our intuitions  Resources: Oliva’s Website: www.lovemakeityourbusiness.com Data Science Consulting: www.oliviagroup.com  My Big ‘Why’ - https://tinyurl.com/LOVENEWCOMPETITIVEEDGE LOVE@WORK now available at https://tinyurl.com/OLIVIAPRLOVEATWORK - A Silver Nautilus Book Award-Winner  The LOVE@WORK MethodTM now available at https://tinyurl.com/TheLOVE-WORKMethod What is your Corporate Love Quotient? Find out here www.corporatelovequotient.com  Oliva’s Social Media: Facebook: https://www.facebook.com/LoveMakeItYourBusiness/ LinkedIn: https://www.linkedin.com/in/oliviagroup/ Twitter handle: #OliviaParrRud   YouTube: www.OliviaOnYouTube.com Instagram: Love.MakeItYourBusiness Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology 
56:37
August 20, 2019
#64 Intersections of Analytics, AI, Linguistics and Culture with Prashant Natarajan – Principal, AI & Analytics
Prashant Natarajan has 18+ years’ experience in building EMRs, ERP, big data platforms, actionable analytics, and machine/deep learning applications. Before joining Deloitte, he served in hands-on global consulting and product leadership roles at H2O.ai, Oracle, McKesson Payer Solutions, Healthways, and Siemens. Prashant is Co-Faculty Instructor of Data Science and AI at Stanford University School of Medicine, Palo Alto, CA, USA. He volunteers as an industry expert and guest lecturer at leading Australian universities. Prashant serves as an industry advisor at the CIAPM computer vision project in University of California San Francisco, Council for Affordable Health Coverage, and Pistoia Alliance Center for Excellence in Artificial Intelligence.   In this episode, Prashant describes how essential human interaction is for success. In a technology-heavy space, human interaction and linguistics were not very common. Instead of complaining about it, Prashant went and got his masters to focus on English in the technology space. To have success, we need a clear understanding of culture. Culture is language, and language at its core is mathematics. How do we interact with people to figure out what their strengths are? Prashant considers himself the luckiest person on earth to have the experiences he has had in his career.  Enjoy the show! We speak about: [01:25] How Prashant started in the data space  [03:45] Studying communications and linguistics   [08:45] Mentoring young professionals   [11:45] Work with people who are smarter than you   [15:00] Merging business problems with data science   [19:45] The value business leaders see in data   [25:00] Advice for companies who are moving into data-driven products [29:45] What excites Prashant about the future of data  [34:05] Horizontal capabilities  [37:20] The use of machine learning in healthcare   [44:20] Improving product development  [48:40] Prashant’s proudest moment [50:15] The manufacturing industry  [52:20] We learn more from our failures than our successes  Resources: Prashant’s LinkedIn: https://www.linkedin.com/in/natarpr/ Demystifying Big Data and Machine Learning for Healthcare (Himss Book) Quotes: “Human interaction is the most key determiner of success or not.” “Today, we have the technology that has caught up with the human need.” “Data science is increasingly a horizontal capability that will impact all of us.” “I celebrate relationships because they allow me to learn.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
56:32
August 13, 2019
#63 Set Yourself Multi-Year Professional Challenges with Felipe Flores – Founder & Podcast Host
In this episode, Anthony Ugoni, one of Australia’s more prominent leaders in analytics interviews Felipe. Felipe came to Australia as a backpacker and ended up falling in love with the place. With Spanish as his first language, the only English he could say was the jacket is black. Then, Felipe explains some of his odd jobs and working freelance IT. At university, Felipe wanted to specialize in data, but all of his friends told him it was dead. So, he ended up specializing in hardware, even though all of his work was in data. When Felipe went to do his thesis, he happened to stumble into a project involving brain wave activity. The electrical engineer did all the research and design, the signals would be passed to Felipe’s computer, where he made his first application of machine learning.  Then, Felipe explains how he and a colleague of his made the decision to quit their jobs at a small consulting firm. They decided to start their own firm, despite knowing very little about business. The first year they almost went bankrupt about four times and made lots of mistakes. They wanted to be in analytics but were unsure how to sell their services. The two spent six months creating a piece of software. When they went to show prospects they found out people did not like the entire product. So they decided to focus on their consulting business.  Enjoy the show! We speak about: [02:40] Felipe’s background  [06:10] Education and specializations [14:30] Quick delivery of value   [17:20] A series of odd jobs and IT freelancing   [24:20] Setting up his own consulting company   [33:15] Highs and lows of Clear Blue Water [37:30] Executive Director & Head of Data Science at ANZ [47:55] Supportive and open culture at work  [52:40] Understanding the business at a new job [54:45] Inspiration behind Data Futurology [62:00] Explainable AI  Resources: Felipe’s LinkedIn: https://www.linkedin.com/in/felipefloresanalytics/?originalSubdomain=au Episode #21 Antony Ugoni: https://www.datafuturology.com/podcast/21 Quotes: “If I’m an engineer, people will think I’m smart.” “A colleague of mine and I decided to set up our own consulting company. Professionally, it was the best and worst thing I’ve ever done.” “Sales is built on trust and a human connection.” “I had not done a good job of being a leader and creating a culture.” “How can we make data scientists today, the CEOs of tomorrow?”  Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:04:15
August 6, 2019
#62 Full Stack Data Science with Gregory Hill – Global Head of Analytics
Dr. Gregory Hill leads the Analytics function at Brightstar's Global Services division, developing and delivering their data & analytics strategy, innovation programs, and product development initiatives. He works across their lines of business, including supply chain optimization, product portfolio management, financial services, buy-back and trade-in, leasing, and omnichannel solutions. He also manages Brightstar's analytics team in support of their key global accounts with pre-sales, solution design, and service delivery. His expertise is in the application of advanced analytics techniques (including machine learning, predictive modelling, mathematical optimization, econometrics, and operations research) to commercial problems. These applications span forecasting, pricing, fraud, market segmentation, customer satisfaction, and propensity modelling. In this episode, Gregory explains how he started in the data space. He was aware of all the theoretical work being done around data but did not know how it worked in an industry aspect. The real challenge of putting mathematical models to practice lies in the organizational and people elements of it. Computer science and electrical engineering do not teach you how to overcome organizational challenges and individual motivations and incentives. Going back to get his Ph.D., Greg wanted to do something requiring qualitative research. So he targeted informational systems and economics. His fieldwork leads him to interview executives of larger banks, publicly listed companies, and government agencies. He came up with an economic framework that improved customer data quality.  Enjoy the show! We speak about: [02:00] How Greg started in the data space [11:10] Leaving academics and getting involved in the industry   [13:20] Greg’s work background [18:25] The four P’s of marketing [20:40] Transiting from gut instinct to a data-driven approach [27:55] Thinking through cause and effect  [30:45] What Greg’s team looks like [39:00] Lessons learned from managing data scientists   [42:25] Active in local data science meetups + guest speaking   [44:25] Working globally + peeling back opportunities to use data science techniques Resources: Greg’s LinkedIn: https://www.linkedin.com/in/gregoryhill/?originalSubdomain=au Brightstar: https://www.brightstar.com Quotes: “My thesis was not a project; it was a lifestyle.” “I didn’t want to be an academic, I wanted to get back into the industry.” “It was a combination of arrogance and laziness.” “At the end of the day, it boils down to if I change X, will Y change?” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
49:43
July 30, 2019
#61 Data Science Strategy in the Military, Startups and Tech/Digital Leaders with Sveta Freidman - Data Analytics & Science Director/Mentor
Sveta Freidman is a data scientist and business intelligence leader with extensive experience in consulting and client-based environments. She has a vast experience working in different industries, including gambling, retail, health, and online businesses (startups). Sveta is a data strategist with a passion for connecting people to the data they need to make decisions, build better products, and execute marketing strategies.  In this episode, Sveta explains why she decided to study statistics, she had a passion for mathematics. During her time in Israel’s military, she collected data from different places and made sense from it. Her commercial experience comes from various startups she joined. When joining a startup, you have to wear many hats. Sometimes you have to be a data engineer, data scientist, or a data analyst. Then, Sveta moved to Australia and found a startup, Envato, where she built all the data from scratch. Enjoy the show! We speak about: [01:40] How Sveta started in the data space  [07:15] Sveta’s professional background   [17:10] Investing in local talent    [20:45] How to hire for a startup [24:30] Questions for hiring interviews  [29:25] Working for Carsales  [31:55] People not trusting the data   [35:20] Solving the issue of trust  [40:30] Finding bias in the data  [44:50] Make sure you look at the data every day  Resources: Sveta’s LinkedIn: https://www.linkedin.com/in/sveta-freidman-5981593 Carsales: https://www.carsales.com.au Quotes: “Statistics is everywhere.” “You can be a great data scientist, but you need to understand the culture.” “I give the candidate a business problem to see how they will react to it.” “Your algorithms are good as long as your data is good.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
50:47
July 23, 2019
#60 Building Self-Driving Cars in Silicon Valley with Vladimir Iglovikov, Ph.D. – Senior Computer Vision Engineer and Kaggle Grandmaster
Vladimir Iglovikov graduated from university with a degree in theoretical physics, he moved to Silicon Valley in search of a data science role in the industry. This led him to his current position in Lyft’s autonomous vehicle division where he works on computer vision related applications. In the past few years, he has invested a lot of time in Machine Learning competitions leading to his title of Kaggle Grandmaster. In this episode, Vladimir explains how difficult it was to find work in Silicon Valley. He had harsh requirements for a salary, no one looked at his resume. Companies in Silicon Valley are willing to pay big bucks, but at the same time, they require the person to be skilled in software engineering, machine learning, and statistics. His biggest issue when applying for jobs was assuming that all people are similar to the people in academics. At his interviews, he felt no connection with the interviewers. After sending his resume to over 200 different companies, someone finally bit just before his visa expired. Vladimir worked at Bidgely for 8 months then moved to TrueAccord and eventually got his job at Lyft.  Enjoy the show! We speak about: [02:00] How Vladimir started in the data space [12:30] Transferring from academia to industry  [21:40] Benefits of having soft skills    [25:45] How Vladimir manages stress  [31:30] Kaggle is like lifting weights  [35:30] The hiring process for data scientists   [40:45] Excitement for machine learning  [46:00] Autonomous driving    [47:55] Pursuing a startup [51:40] Aiming to maximize mistakes in a day  [61:00] Social life comes first  Resources: Vladimir’s LinkedIn: https://www.linkedin.com/in/iglovikov/ Kaggle: https://www.kaggle.com/iglovikov Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:03:00
July 16, 2019
#59 Creating the Link Between Business and Data with Tony Gruebner - GM Analytics, Insights and Modelling
Tony Gruebner is the GM Analytics of Insights and Modelling and the Exec Sponsor of Personalisation at Sportsbet. He established a department of 40+ skilled analysts and data scientists tasked with creating innovative data products focused at improving the experience for their customers and supporting the business by providing relevant and timely information and insights that steer decision making across all levels of the business. He has served on the Executive Leadership Team from 2016. In this episode, Tony explains how he started in data and what led him to get his job at Sportsbet. Tony got a call from a recruiter asking if he wanted to do work with analytics, in a company that does sports and is heavily digital. All of those factors checked the box for Tony, and he took the entry-level analyst role. Over time, the need for analytics has grown, so he has been able to develop some analytics teams.  Enjoy the show! We speak about: [01:20] How Tony got started in data [08:20] Tony’s skills come from the commercial side [11:10] Linking data science and the business [14:30] Communicating how data science works [17:00] Steps to getting others to understand data science [20:40] Getting the best talent for your team [24:00] Structuring teams and the department [28:10] Transiting from analytical roles to commercial roles  [35:30] Working on global expansion [38:10] Solving with artificial intelligence [42:30] Passionate about using numbers to reach an outcome [44:00] Modelling failures with Sportsbet  [47:50] Imposter syndrome in data science   [50:05] Data science is rapidly changing and exciting Resources: Tony’s LinkedIn: https://www.linkedin.com/in/gruebz/ Sportsbet: https://www.sportsbet.com.au Tony’s Twitter: https://twitter.com/gruebz?lang=en Quotes: “There is no one path that always works.” “There are literally thousands of things data scientists couldn’t potentially tackle in any business.” “If you’re not making mistakes, then you aren’t pushing the envelope hard enough.” “Not having imposter syndrome is a sign of lack of knowledge.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
53:43
July 9, 2019
#58 Explainable AI
Today we have a different type of episode, this is a presentation that Felipe did at the Chief Data and Analytics Officer Conference in Canberra, and it is on explainable AI. First, Felipe explains how Amazon used a secret AI recruiting tool that had a bias against women. Also, the U.S. government used an algorithm predicting how likely people in the criminal justice system would reoffend. What they found is that it targeted specific racial groups. The algorithm isn’t racist or sexist, the data is.  Regarding job applications, as your company scales up, the need to automate the process of looking at the applications becomes necessary. Sometimes, bias will creep into the automated decision-making algorithm. The bias can even be narrowed down to the person’s name. For example, somebody with name Felipe might get scored lower than somebody with the name Tyler. Lean into the inequality and predict the bias. You can plug in the CV information, and ask the algorithm to predict the person’s race and gender. Then, find out what key inputs they are flagging to determine this and remove them from the algorithm.  Then, Felipe explains how algorithms can tackle unstructured data approaches. When discussing images, an algorithm was able to correctly identify a wolf from a husky 5 out of 6 times. However, when uncovering how the algorithm determined which was which, it was merely looking at if the animal was in the snow or not. If the picture had snow in it, then it must be a wolf. To determine how this algorithm was functioning, Felipe used LIME - Local Interpretable Model-Agnostic Explanations. It works for classifications and came out of a study from MIT. Later, Felipe discusses using EL15 and how transparency is essential for the public to understand how the algorithms could affect them.  Enjoy the show! We speak about: [03:40] Large companies and their biases  [05:40] Racism and sexism is in our data [08:45] Uncovering inputs of the bias    [10:45] Unstructured data approaches  [14:30] Using ELI5  [19:20] The right to an explanation  Quotes: “We teach our algorithms on how to replicate our decisions.” “The algorithms show the inequality that we have in the world today.” “Explainable AI is more ethical in the sense that it is more transparent.” “Explainable AI helps us avoid blunders and informs us how the algorithm perceives the data.” Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
22:03
July 2, 2019
#57 From Academic to Data Science Leader with Yuval Marom - Analytics and Data Science Professional
Yuval is an Analytics and Data Science professional with extensive commercial and academic experience. His interests and goals are to be working on interesting and practical problems where there is a need to discover and act on meaningful patterns in data, through advanced analytics and data science. I'm the founder and co-organiser of two meetups: Data Science Melbourne and MelbURN, a user group for Melbourne-based users of the R statistical and data mining programming language.  In this episode, Yuval tells us about how both of his parents are statisticians and inspired him to fall in love with data science. Growing up, he used Pascal to build spaceship games, and it motivated his passion for programming. Eventually, Yuval went for his Ph.D. and focused on applying how animals learn and behave to robotics. Simulated and physical experiments were pretty basic because robotics were not as advanced as they are today. Later, Yuval realized academia was not necessarily his calling, he was more interested in applying solutions to interesting problems. However, in recent years, research innovation and solving problems are becoming much more intertwined.  Enjoy the show! We talk about: [01:40] How Yuval fell in love with data science [05:45] Social learning in biology [08:05] Lessons learned from completing a Ph.D. [13:10] Research innovation vs. solving problems [15:40] Embrace simplicity  [18:00] Small business advantages  [21:45] Skills to develop before management  [26:00] Results oriented work [30:45] Different flavors of management [32:50] Connection to community  [40:20] Learning to interact with stakeholders + managerial skills  [44:00] Benefits of building connections + education  [48:00] Assume people are at work with good intentions  [52:00] Allocate time for professional development  [59:30] Focus on retention Resources: Data Science Melbourne  MelbURN Yuval’s LinkedIn University of New South Wales Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:03:50
June 25, 2019
#56 Every Business is an AI Business with Dr. Eric Daimler – Serial Entrepreneur, Technology Executive, Investor and Policy Advisor
Dr. Eric Daimler is an authority in Artificial Intelligence & Robotics with over 20 years of experience in the field as an entrepreneur, executive, investor, technologist, and policy advisor. Daimler has co-founded six technology companies that have done pioneering work in fields ranging from software systems to statistical arbitrage. Daimler is the author of the forthcoming book Every Business is an AI Business, a guidebook for entrepreneurs, engineers, policymakers, and citizens on how to understand—and benefit from—the unfolding revolution in AI & Robotics. A frequent speaker, lecturer, and commentator, he works to empower communities and citizens to leverage AI & Robotics. For a more sustainable, secure, and prosperous future. In this episode, Eric explains how he has a vivid memory of getting a computer at the age of nine. He loves the machine, and even at such a young age saw the freedom a computer allows. Early in his career, Eric knew he wanted to work with brilliant and motivated people. When he was in New York, he saw the Netscape browser and instantly recognized the world was going to change. This inspired him to get out and find opportunities on the west coast.  Enjoy the show! We speak about: [02:10] How Eric started in the technology space [05:15] Moving from one career path to another [09:50] Eric’s most significant failure as an investor  [13:30] Picking the timing   [18:15] AI is larger than what currently exists   [21:30] Embracing the technology behind AI [29:45] Hurdles for companies who are adopting AI   [41:30] Reactions from people learning about AI  [48:40] Shortage of truck drivers + how technology is making driving easier  [54:00] AI in the medical field  [61:30] Using a categorical approach   Resources: Eric’s LinkedIn: https://www.linkedin.com/in/ericdaimler/ Eric’s Twitter: https://twitter.com/ead  Website: http://conexus.ai/ Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:04:28
June 19, 2019
#55 The Truth about AI from the People Building it with Martin Ford, Futurist and New York Times Bestselling Author Focused on Artificial Intelligence (AI), Robotics and the Future Economy
Martin Ford is a prominent futurist, New York Times bestselling author, and leading expert on artificial intelligence and robotics and their potential impact on the job market, economy and society. His 2015 book, "Rise of the Robots: Technology and the Threat of a Jobless Future" won the Financial Times and McKinsey Business Book of the Year Award and has been translated into more than 20 languages.  In this episode, Martin discusses his best-selling books and describes some of the themes he writes about. For instance, in Rise of the Robots he talks about “The Triple Revolution” which was a report presented to U.S. President Lyndon B. Johnson fifty years ago that argued this would be a dramatic change to the economy; however, it never really panned out. Martin’s argument for artificial intelligence started back in 2009 after writing his first book titled The Lights in the Tunnel. Ultimately, artificial intelligence will become so powerful that it can have a significant impact on employment that will compete with a large fraction of the workforce.  Enjoy the show! We speak about: [02:50] Martin’s background  [05:45] The themes behind Martin’s writing  [08:35] Machine learning is when algorithms can make decisions   [12:00] Amazon is susceptible to automation  [16:45] The most common occupation error is driving some kind of vehicle    [18:15] The type of work that will be left for humans   [21:45] Universal basic income   [28:55] Building explicit incentives to earn more income; paying people more to pursue education [33:25] Artificial intelligence will be the primary force shaping our futures  [38:35] The solution is not to teach everyone how to code  [41:30] Architects of Intelligence: The truth about AI from the people building it [46:00] Deep learning is the biggest thing to happen to artificial intelligence   [52:20] Controlling data and an entirely new industry called data banks  [53:15] Negative implications of artificial intelligence  [64:40] You do not want to be doing something predictable  Resources: Martin’s Website: https://mfordfuture.com/about/ Martin’s LinkedIn: https://www.linkedin.com/in/martin-ford-5a70428/ Martin’s Twitter: https://twitter.com/MFordFuture TED Talk: https://www.ted.com/talks/martin_ford Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:08:40
June 13, 2019
#54 How to build a Data Department from scratch with Annie South – General Manager of Data at ME Bank
Annie South is the General Manager of Data at ME Bank. She is an Information Management professional with twenty years’ experience of complex information environments spanning the full spectrum of structured data to unstructured information. Annie has in-depth technical knowledge of various specialisms, including metadata, data warehousing, data governance, data quality, enterprise architecture, data lineage, Big Data, data analytics, and regulatory requirements. In this episode, Annie explains the things she does to ensure her career is future ready because nobody can predict what jobs will look like years from now. Do not specialize in a particular technology but specialize in a capability. The technologies that you are using today will not be the technologies they are using tomorrow. If you specialize in a particular technology set, and it is decreasing in popularity, you will end up with fewer opportunities in the market. Annie tells people wanting career advice that when people look at your resume, they are looking for a consistent arc. That could mean staying consistent in an industry or constant engagement in the workforce. Another thing Annie looks for in applicants is kindness, this quality is something that cannot be taught.  Enjoy the show! We speak about: [01:20] How Annie got into the world of data [10:00] Insight for people starting in the data space [12:50] Organizations are not predictable   [14:50] Annie’s team at ME Bank  [27:50] Turning recruitment on its head [33:20] Transitioning from teaching to general manager  [39:05] Sort out your personality and experiment with leadership  [46:30] Imposter syndrome   [49:10] Experimenting with diversity in the workforce  [53:30] Challenges with discrimination in the workplace    [61:10] Define yourself; do not be defined by others  Resources: Annie’s LinkedIn: https://www.linkedin.com/in/annesouth/ ME Bank: https://www.mebank.com.au IT Jobs Watch: https://www.itjobswatch.co.uk Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:08:16
June 6, 2019
#53 Winning Data Science Competitions With Pavel Pleskov - Data Scientist & Kaggle Grandmaster, Top 3 In The World
Pavel Pleskov is a data scientist at Point API (NLP startup) and currently ranks number 3 out of 109,624 on Kaggle, making him a Grandmaster. Pavel has started companies in the past and has worked in many different industries before becoming a data scientist and Kaggle Grandmaster.  In this episode, Pavel explains his background and how he started in the data science space. When Pavel’s girlfriend went to pursue her master’s degree in London, Pavel began interviewing for a quantitative research job nearby. Turns out, the company was a rival of his current employer, causing him to get fired from his job the next day. Former employees of this job contacted Pavel to ask if they would join their new trading firm and be head of their research team. After doing his job for two years, Pavel knew he was capable of doing it on his own. The company works remotely, and after spending time in bitter Russian winters, Pavel looked to work elsewhere. The ideal country turned out to be Vietnam and was Pavel’s first time outside of Russia.  Enjoy the show! We speak about: [01:50] How Pavel started in the data space  [09:50] Vietnam is an ideal space for working remotely and teaching English  [21:40] The moment Pavel found Kaggle  [24:20] How Pavel became a data scientist  [28:00] Difference between machine learning engineers and researcher data scientists  [31:50] Why is it essential to be the very best? [34:20] Machine learning and mathematics  [36:45] The early days of Pavel’s Kaggle journey  [40:00] Pavel’s favorite part of Kaggle  [47:20] The role of automation in Kaggle    [49:40] The steps when approaching a new Kaggle competition  [52:55] Think twice before you commit to data science  Resources: Pavel’s LinkedIn: https://www.linkedin.com/in/ppleskov/?originalSubdomain=ru Pavel’s Kaggle: https://www.kaggle.com/ppleskov Pavel’s Twitter: https://twitter.com/ppleskov Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
56:56
May 30, 2019
#52 Transforming Marketing with Data Science with Danielle Timmins – Chief Data Analytics Officer
Danielle Timmins is the Chief Data Analytics Officer for Free Range Creatives. Free Range Creatives is a digital marketing agency that is deeply rooted in data and analytics. They have a different view on agency life and challenge the existing ways of working. They believe that work should be fun (well, at least most days) and that our work must be insightful, inspirational and effective. In this episode, Danielle tells us how she did not start in the data space but initially wanted to be a doctor. Danielle ended up getting a Master’s in Economic Psychology, during which she concentrated on the digital side of marketing. This is where Danielle got her exposure to data and started to understand it. Danielle got her first start at an NGO in a marketing position. She would shoot mini-documentaries for television and then moved into a more traditional marketing role. Danielle’s first job as a strategist was down in South Africa where she worked with several different clients. This is when she would start to work with data and incorporate it with strategy.  Enjoy the show! We speak about: •    [01:45] How Danielle started in the data space  •    [03:20] Background and career   •    [06:20] Deciding what problems to tackle first on the job  •    [08:35] Evolution of marketing    •    [13:35] Favorite failure •    [16:50] How to communicate data •    [18:30] Visual presentation style  •    [19:45] How Danielle creates a story  •    [21:30] How do you structure visuals for executives? •    [23:10] How do you think people can get better at this skill? •    [24:45] What is a strategist for data? •    [27:40] What is the role outside of data? •    [29:00] The main challenges for Danielle’s clients •    [32:30] Working with clients on case-by-case basis •    [33:30] Qualities of a great data scientist   •    [35:30] What do you think makes a good data leader?  •    [36:15] Current challenges in the data space •    [37:40] Future challenges for the data space •    [42:40] Advice for future data scientists and leaders Resources: Sexy Little Numbers Free Range Creatives: https://www.freerangecreatives.co.za/ Danielle’s LinkedIn: https://www.linkedin.com/in/danielletimmins/  Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
46:26
May 23, 2019
#51 AI-as-a-Service with Technology Executive & Serial Entrepreneur Peter Elger, Founder and CEO of fourTheorem
Peter Elger is the founder and CEO of fourTheorem; his focus is on delivering business value to his clients through the application of cutting edge serverless cloud architectures and machine learning technology. His experience covers everything from architecting large-scale distributed software systems, to leading the internationally-based teams that built them. In this episode, Peter tells us how his first real passion was in physics. After graduating with a BSc in Physics and a master’s degree in Computer Science, he worked for several years at the Joint European Torus (JET), the world's largest operational magnetically confined plasma physics / nuclear fusion experiment. They were doing big data, but at the time they did not refer to it as such; they dealt with around four to five terabytes of scientific data. Peter then transitioned to Indigo Stone as a Senior Technical Architect. Indigo Stone was a software disaster recovery firm which exited in 2007 to EMC.  Peter explains how it is essential to keep your technical skills up-to-date and why some of his favorite days are when he gets to code despite being the CEO of his company. If you can actually be the bridge between the business and the technology, you are an invaluable asset to any company. The freedom to innovate is what led Peter to his entrepreneurial ventures; previously, he had no real experience being his own boss. Peter says it is dangerous to think you can do everything; you have might a broad skill set, but you need to recognize that you have gaps. This is why Peter has always started businesses with co-founders. Currently, his co-founder is a world-class technologist and someone who understands the human dimension. All of his current co-founders and people Peter has worked with previously.  Enjoy the show! We speak about: [01:45] How Peter started in the data space  [06:50] Transition to disaster recovery   [08:55] Interactive radio and marketing applications  [13:40] Maintaining a grip with technical skills  [16:20] The entrepreneurial bug came organically to Peter [18:40] Transition to entrepreneurship   [21:45] What to look for in a co-founder  [26:00] Building analytics with machine learning  [29:00] A tale of two technologies  [33:00] Applying AI to existing platforms  [35:10] Knowledge of AI is not necessary to use AI as a service   [37:50] Capable team members are difficult to find  [40:10] Sharing management meetings with all staff members   [44:05] Experiences with handling politics in organizations  [48:50] Removing ego + allowing the team to do their best work  [50:30] Scheduling work to maximize the impact  Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
56:34
May 16, 2019
#50 Making CDOs more effective with Prakash Baskar – Founder and President
Prakash Baskar is the Founder and President of Khyanafi. He helps data leaders to rapidly transition and accelerate the success of data, analytics, and digital initiatives. Previously, Prakash was the Chief Data Officer at Santander Consumer USA where he led enterprise data governance, risk infrastructure & information (risk data aggregation), data quality, business data strategy & solutions, and business & reporting analysis functions.  In this episode, Prakash tells us how he started in the data space at his university. His role was to determine how students were performing. If they are not performing well, he needed to identify why. The graduation rates were low at the school, so Prakash was tasked with finding out what was the problem. Then, Prakash discusses starting a new job and having little direction about what to do. With everchanging technology, the description of your job will always be changing too. As a person going into any role, understand that you do not have to ask permission all the time. Have a clear idea of what you can do and what you cannot do, then do what you feel is right for the organization. Look for where the opportunities for expansion are and find a way to get results.  If you ask ten people what the role of a Chief Data Officer is, you will get ten different answers. Whatever the CDO does will ultimately be to enable others to receive real benefits out of the data. Just because something is not broken, does not mean it cannot be improved. There are many different routes a person can take to become a CDO; however, you need someone with knowledge in multiple aspects of business, technology, and people management. A CDO needs to create value for the organization; learn the company you are supporting to anticipate the problems they may run into. Later, Prakash explains how in business, any change is hard. How you embrace the change after it is made is what will differentiate yourself from others. If the change is too complicated, people will shut off. Start off by telling the client what the change will do for them rather than the steps it will take to get there. Some other tips when presenting a significant change is to be realistic with what it will take and make sure not to overpromise. It is imperative to select things that you can quickly do with minimal engagement from their people. Plus, make sure you have updates for the company each month, so they understand what is being revealed from the data. Finally, Prakash discusses how essential it is to move around the organization in order to understand different departments and he reveals the inspiration behind his latest business venture.  Enjoy the show! Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
59:55
May 10, 2019
#49 Becoming a Kaggle Competition Master with Valeriy Babushkin – Head of Data Science, Kaggle Competition Master (Top 60)
Valeriy Babushkin is the Head of Data Science at X5 Retail Group where he leads a team of 50+ people (4 departments: Machine Learning, Data Analysis, Computer Vision, R&D) and increases profit in a 25+ billion USD company. Also, Valeriy is a Kaggle competition master; ranking globally in the top 60. In this episode, Valeriy explains his background and how he started in the data science field. At one point, he received an offer for a senior position at a bank; it was the largest privately owned bank at that time in Russia. Valeriy did not find out that he was doing machine learning until working on it for two years. What someone is doing right now could be pretty close to machine learning, and they don't even know. Then, Valeriy speaks on how trust is essential to the job of a data scientist; not only between you and your boss but between you and other departments. Trust will make your job easier when explaining the data, the results, and how reliable they are for the company. However, if there is an existing data science department in the company, you will not have to work as hard to earn the trust of others because it already exists. Sometimes when data scientists join a company, they think their job will just be to code all day. That is not always the case, you will have to talk to many people and often be a business analyst.  Enjoy the show! We speak about: [01:45] How Valeriy started in the data space  [06:10] Transiting to working at a bank [11:30] Understanding the business process  [15:10] Gaining trust from clients  [20:20] Data scientists are business analysts  [24:10] Expectations from the job interview  [25:50] Starting data science teams [31:40] The type of mindsets to look for in a team member  [37:30] Different teams complement each other  [40:20] Valeriy’s journey with Kaggle [47:40] Ethical challenges in the industry  [51:20] Persistence is key Resources: Valeriy’s LinkedIn: https://www.linkedin.com/in/venheads/ Valeriy’s Kaggle: https://www.kaggle.com/venheads Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
54:30
May 4, 2019
#48 Leverage What You Know to Get Your Foot in the Door with Jay Liu - Chief Data Scientist at Digital-Dandelion
Jay Liu is the Chief Data Scientist at Digital-Dandelion specializing in helping insurance, and medical organizations innovate by integrating the latest in Artificial Intelligence (AI), machine learning and big data into their systems. Knowing the best way to learn is by putting your money where your mouth is, Digital-Dandelion launched an online brand and built a customer AI to promote it. There were numerous technical and modeling challenges that were overcome, but in the end, they sold all their stock within three months. They had proven to themselves that customer AI worked. Organizations can have great depth and breadth of customer data from their long-term relationships of selling high-value products and services.  In this episode, Jay explains how he found himself in advertising and started getting fat because of all the Michelin star restaurants his potential clients would treat him to. His data science career began with loyalty cards and being incredibility confident. When someone uses a loyalty card, the company is collecting data. They will know exactly what you purchased and how much you purchased of each item. The customer will be rewarded with monthly coupons. Jay was in charge of coming up with the coupons that were designed to make the customer spend more money in the store. Knowing at least one data programming language will leverage what you have and give you one foot in the door. The best way to get into data science is to know how it will improve the current industry or business you are working for. Later, Jay explains why QA is a lost skill and the idea that great data scientists have internal discipline. However, there is a race to push the boundaries and become more automated. For example, Facebook collects as much data as possible and thinks about the consequences later. Data is data and people are people. Understanding data is the starting point. Before Jay starts a job, he dives deep and analyzes what every number means to the business with their data collection. Also, Jay considers how to make his bosses job as easy as possible. Overall, the success of his boss will create the most significant impact on his business. If someone has been working at the same job for ten years, they are scared to grow and try something new. Finding a data scientist who has worked at multiple different sizes and types of organizations is the key to finding a well-rounded employee.  Enjoy the show! Resources: Jay’s LinkedIn: https://uk.linkedin.com/in/jay-liu-76ab2b8a Digital-Dandelion: https://www.digital-dandelion.com Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
43:28
April 26, 2019
#47 Transforming Government Organisations with Data Science with Marek Rucinski – Deputy Commissioner, Smarter Data Program
Marek Rucinski is the Deputy Commisioner leading the Smarter Data Program at the Australian Taxation Office (ATO). Marek has taken part and driven the evolution and transformation of Marketing, Analytics, Data and Digital capabilities for over 20 years. This has been done in both industry roles and consulting services capacity, across Australian, Asian and Global clients, across Retail, Telco, Consumer Goods, Financial Services, Mining & Utilities sectors. His passion centers on helping clients change the role of Marketing & Analytics capabilities in Digital and Data age, from activating the capability through acting on insights, to transforming customer experience and the whole business via delivering value across business functions. Prior to ATO & Accenture, Marek lead and created analytics functions and teams in a Retail industry, and developed global corporate strategy frameworks and analytics in a multinational organizations. In this episode, Marek tells us about how he was always interested in the science behind marketing. Marketing as a discipline has been completely transformed due to the emergence of data as a driver for engagement with the customer. Marek is not a classically trained data scientist; he is a data strategist and can dive deep into the organization’s needs in order to drive value to the customer. Marek tells us how some businesses can struggle with how to handle the findings of research from data scientists. It is essential to translate the potential into targets to create the prize. Leave the ego at the door and find the ability to be critiqued.  Later, Marek tells us how educating businesses on analytics as a mechanical process is essential for them to perceive how the whole thing works. He then explains his transition from consulting to government and how his excitement lies in the play with analytics at an enormous scale. Then, Marek describes how to have each section of the value chain working with purpose and precision. Data has to be trusted, organized, and accessible for the company. A data strategist must consider how the data is being delivered to their client. You want to create products and interactive experiences for the business as opposed to simple spreadsheets. Finally, Marek answers the audience’s questions including what makes a good data scientist and current challenges in the data science industry.  Resources: Marek’s LinkedIn: https://www.linkedin.com/in/rucinskimarek/ Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
38:59
April 19, 2019
#46 Today is the Best Time to be a Data Scientist with Jonny Bentwood – Global Head of Data & Analytics
Jonny Bentwood is the Global Head of Data & Analytics at Golin. Jonny is an innovative leader with 15+ years of experience in communications - winning, retaining and working for Fortune 100 clients such as Facebook, Unilever, Heineken, Barclays, HP and Microsoft. He has a proven record as a creator of pioneering solutions with ability to transform business to radically impact bottom line. Jonny presents complex information in an engaging and informative style and is a strategic consultant to executives using data to provide guidance on reputational and crisis issues and maximising marketing campaigns.  In this episode, Jonny tells a story about how MTV got in touch with him to apply data in figuring out who would most likely win The Apprentice. After being in the industry for over twenty years, he believes this is the best time to be in data. CMOS are spending more of their money than ever before on analytics. How do data scientist prove their value? People use data purely in a descriptive way. To succeed and bring value to clients, one needs to switch from describing the data to telling the customer what they need to do with the data. Set the goals of who, what, and why to figure out which message will be most useful before you even start. Take it a step further by using prescriptive data and make it predictive. This is where you study what will happen in the future. We are continually absorbing and understanding what things could happen and will happen. This opportunity is essential to identify issues before they occur and fix them. We speak about: [01:30]      How Jonny started in the data space  [04:50]      Public relations [06:00]      Descriptive, prescriptive, and predictive  [08:15]      Difference between interesting and useful [10:00]      Understanding the customer [15:25]      Cultural shift of data in organizations  [19:10]      Challenging the status quo  [22:40]      Shiny object syndrome  [26:45]      The twenty percent time [30:00]      Bringing data application to the masses [34:30]      Each stage of the customer journey  [39:30]      Getting value for money [42:45]      Return on investment  [44:15]      Data + creativity  Resources: Jonny’s LinkedIn: https://uk.linkedin.com/in/jonnybentwood Jonny’s Twitter https://twitter.com/jonnybentwood?lang=en Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
55:02
April 12, 2019
#45 Mastering the Domain of Your Work Before Becoming a Data Scientist with Warwick Graco - Senior Director Data Science
Warwick Graco is the Senior Director of Data Science at the Australian Taxation Office (ATO). He has worked in defence, health, and taxation and has been involved in analytics for 25 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a Ph.D. from the University of New England Australia. His professional interests include organisational innovation and learning, organisational decision making and analytics. In this episode, Warwick tells us how he got started in data research the skills gained that led him to his successes today. Warwick explains why transparency is a business requirement for software and tools in the data science field. People with more analytical backgrounds will be more willing to accept an opaque solution over a transparent solution. When analytics was in the early stages, some organisations pushed back from data science; feeling they were on top of their portfolio and did not need any outside resources. No matter what results Warwick would come up with for these organisations, they would continue to have the same attitudes. Since 2010, there has been a shift in attitudes because data science has shifted from the background to the foreground. Then, Warwick tells us the difference between good support and lousy support in the workplace. While Warwick was working with organisations, instead of providing results, he did the reverse. Ask the organisation what they want rather than telling them the findings. Providing the outputs clients wish to see led to incremental improvements built into their business intelligence reports. Warwick also explains why you can no longer be a data scientist; you will need to learn and master the domain of your work. For instance, Warwick learned everything about ophthalmology while working on data science with an ophthalmologist. Later, Warwick explains his process of publishing research, improving privacy concerns, and automated supports. Enjoy the show! Show Notes: • [02:20] How Warwick started in data science • [05:55] Aptitude for research • [08:40] Purpose-built software + decision trees • [12:20] Accepting opaque solutions vs. transparent solutions • [16:45] Pushback of data analytics • [21:15] Difference between good support and bad support on the job • [25:25] Necessity to learn the domain first • [29:00] How to learn on the job • [32:20] Process of publishing research • [41:50] Improving legal and privacy concerns • [44:25] Automated support + decision-making operations • [52:40] Developing an analytical + practical mindset • [58:10] Hyperspecialized • [64:30] Moving toward data + analytics as a service • [66:25] Advice from Warwick Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:09:52
April 5, 2019
#44 Using Data Science to Actually Solve Problems with Caroline Worboys - Data Expert, Investor, Advisor, COO & Vice Chair
Caroline Worboys is a data expert, investor, advisor, COO at Outra & Vice Chair at DMA Group. She has been working in the data industry for over 30 years. In this time, she’s had a fascinating journey. She has worked, created, mentored and consulted through many data driven organisations. She’s played all the different roles: technical lead, a business lead, a founder and investor. While Caroline doesn’t describe herself as a data scientist and didn’t go to university, she has always worked with data and has a wealth of experience. She started in the field by working with consumer data for direct marketing and progressed to the point where she founded and sold several successful data related start-ups. Currently, she is the founder and COO of Outra. In this episode, we talk about what it was like being a woman in technology in the 80’s, how the use of data has progressed over the years and how she keeps her team focused on the goal of doing things faster than other companies. Summary How Caroline got started in data (03:02) What she learnt from observing senior colleagues and what it was like being a woman in technology in the 80s (05:38) Using customer data in order to target people at the right time (07:46) The principles of working with consumer data hasn’t changed (10:04) How the care and attention required for direct mail has now been lost with email and digital marketing (11:09) The importance of being curious and learning (12:31) Starting her own business and finding a different way to charge customers (13:46) Advice for young people and why it’s important to seek people for advice (21:34) Personal drivers to start her business (23:35) How her business innovated as technology changed (25:10) The challenge of using data to actually solve problems (30:29) Considerations when choosing her team (35:48) The recruitment process is like for Caroline’s company (39:00) How Caroline keeps her team focused on the goal of doing things faster than other companies (41:40) The difficulties of work/ life balance (44:16) Considerations for being a leader in the data space (47:03) The importance of thinking about the type of data you want to work with (51:43) Quotes “Seek out people who have really, honestly read the book and seen the movie and been there. Because they can stop you from going down a whole bunch of dead ends.” “You can’t scale and have thousands of relationships with thousands of people. But you can create a culture, and processes below that culture, that are scalable.” Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
57:41
March 29, 2019
#43 Increasing Market Share and Improving Operations Using Data Science with Kevin Harrison - Chief Data Officer and Deputy Chief Information Officer
Kevin Harrison is working as Chief Data Officer and Deputy Chief Information Officer for the City of Oakland in California. Prior to this he worked as the first ever Chief Data Officer for the State of Illinois. During that time he designed the blueprint for the State Data Practice. Operating under the new Department of Innovation and Technology agency, he implemented an enterprise approach to Business Intelligence and Data Analytics, covering all 60 State Agencies to create a collaborative and sharing environment across the state. Having worked with multiple organisations, Kevin has been able to handle different types of challenges in our industry. In today’s episode, Kevin shares the strategies he applied to move from smaller projects to bigger ones. How he has been able to help organisations increase their market share and improve operations. Kevin also shares why he thinks changing the perception of organisations about data and educating them about tools in the space is so important. He further talks about data governance and possible changes in role of the data scientist role in future.  In This Episode: 01:55 Professional background of Kevin 06:30 Why data is important? 07:20 Evolution of Data warehousing 10:00 How organizations are utilizing the data? 11:39 As data officer, how to help organizations to improve their data capabilities? 13:00 Building trust is crucial for project success 13:30 Transition from small to bigger project 16:12 Challenges faced as data consultant 19:00 Educating about the change coming to data science 21:00 Process of data strategy for organizations 23:50 Why so many data warehousing failed? 26:00 Importance of data governance 27:10 Biggest problem in data governance 31:56 Role of data storage 35:15 Challenges faced from moving to another industry/sector 38:42 Qualities data scientist should have 41:43 Future of data science 42:30 Advice to the listeners Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
45:43
March 23, 2019
#42 Maintaining an Updated Skillset Despite Rapid Technological Advances with Michael Tamir - Head of Data Science & Data Science Lecturer
Mike serves as Head of Data Science at Uber ATG and lecturer for UC Berkeley iSchool Data Science master’s program.  Mike has led several teams of Data Scientists in the bay area as Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize he oversaw all data science product development and created the MS in Data Science program in partnership with UNH.  Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein’s general relativity theory and the convergence of “large N” Monte Carlo simulations in Statistical Mechanics’ universality models of criticality phenomena. In this episode, Michael talks about how he accidentally got into data and his work with simulation. Then, Michael discusses his background in data science product development and data science education. He reveals all the mistakes he made with his transition from academics to industry.  Later, Michael tells us what attracted him to data science education and how he balances industry projects with his teachings. Rapid growth is a challenge with technology management because your skillset will get rusty as the technology advances. Lastly, Michael talks fake news, bootstrapping, and Fake or Fact.  In This Episode: [00:20] Michael accidentally got into data [02:15] About Michael Tamir [03:40] Transition to industry [06:40] Software engineering challenges  [08:45] Data Science Education  [15:15] Adaptive learning  [17:15] Team management [19:05] Challenges with rapid growth [24:25] Fake news [27:25] Toughest challenge [28:50] Fake or Fact [31:20] Listener questions Mike's quotes from the episode: “You have to be really careful about what you do and what you do not teach in order to make sure students are successful in the long-term.” “Decisions are going to be best made by those who are closest to the ground.” “You’re not going to be the expert in every group you are managing.” “I take full responsibility for any failures with the algorithm.” “Most of my time is spent on my day job.”  “Find out what you enjoy about data science skills; find the role that is looking for those skills.” “I enjoy the science and making sure we are asking the questions in a scientifically sound way.” Connect: Twitter - https://twitter.com/MikeTamir LinkedIn – https://www.linkedin.com/in/miketamir/ Website - http://www.fakeorfact.org Now you can support Data Futurology on Patreon!   https://www.patreon.com/datafuturology  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
38:55
March 16, 2019
#41 Improving Learning on a Large Scale Through Data Science with David Niemi - VP Measurement and Evaluation
David Niemi is Vice President of Measurement and Evaluation at Kaplan, Inc., where he oversees efforts to improve the quality of measurement across all education units, evaluate the effectiveness of curricula and instruction, and study the impact of innovative products and strategies. Previously he was Vice President Evaluation and Research, at K12 Inc., where he directed assessment development and validation, evaluation of products and services, and research studies used to drive curriculum development. He has been a co-principal investigator for a number of large-scale assessment research projects funded by the U.S. Department of Education and the National Science Foundation and has collaborated on Department of Defence training studies. As a researcher and professor at UCLA and the University of Missouri, respectively, he has also managed assessment research and development studies in school districts across the U.S. and has trained thousands of teachers and other professionals to design and use assessments more effectively. David's new book is: Learning Analytics in Education: Experts Explain How To Use Data To Understand and Increase Learner Success New technologies, better measures and more data, all related to learning, hold the promise of helping educators increase their students’ success. The relatively new field of learning analytics has developed to help educators understand and use the increasing amounts of evidence from learners’ experiences. How can educators harness access to greater data to improve learning on a large scale? Learning Analytics in Education is a new book written by a broad range of experts who explain their methods, describe examples, and point out new underpinnings for the field. The collected essays show how learning analytics can improve the chances of success for all learners through deeper understanding of the academic, social-emotional, motivational, identity and meta-cognitive context each learner uniquely brings. The collection was edited by four noted educational experts including David Niemi, vice president of measurement and evaluation at Kaplan, Inc., the global educational services company well-known for using advanced learning science and learning engineering methods in its programs and products.   "At Kaplan, we've been invested in using learning science and data analytics for several years to help us design courses and refine instructional methods to help students achieve better outcomes," explains Niemi. "Educators today face accelerating change as education undergoes a fundamental transformation driven by the replacement of traditional analog tools by digital systems and expansive data inputs." He adds, "Understanding how to use these new streams of available data to best guide student learning is the essential point of the book." Now you can support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: UNSW Master of Data Science Online: studyonline.unsw.edu.au Datasource Services: http://www.datasourceservices.com.au/ or email Will Howard on will@datasourceservices.com.au And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:06:15
March 5, 2019
#40 How to Build a Diverse Data Science Team with Kjersten Moody - Chief Data and Analytics Officer
Kjersten Moody joined State Farm in July 2017 as Vice President and Chief Data & Analytics Officer in Bloomington, Illinois.  Previously, Kjersten led Data & Analytics and IT groups at global companies, such as FICO (Braun), Thomson Reuters and Unilever. She has a record of delivering tangible, positive business results, and a depth of experience in scaling operations, planning/executing mission-critical business initiatives, and achieving profitability objectives.  Kjersten is a graduate of the University of Chicago and has a proven track record in modernizing and scaling operations, executing mission-critical business initiatives, and achieving profitability objectives. An energetic leader with a focus on people development, diversity, and inclusion Kjersten demonstrates the ability to effectively lead and work in highly complex environments. In this episode, Kjersten talks about her love for data and how it compliments an understanding of human behavior. She is incredibly grateful for the chances others took on her to get her in the role she is today. Understanding how to thrive in stressful situations is one of the essential lessons Kjersten learned in her early roles.  Her leadership style is open, honest, and collaborative while always ensuring to take time out of her day to serve others. In the healthcare industry, Kjersten gets to see her work through and enjoys the process of continuous improvement. Building teams have not changed much, some methods of work differ and where the work is performed. For example, information security has grown significantly to evolve with the ever-changing advancements in technology. Later, Kjersten explains how she builds a team, what diversity means, data strategy, data governance, and financial impacts.  In This Episode: • [00:20] About Kjersten Moody • [04:45] Love for data • [06:40] Transition to technology consulting • [09:50] Lessons learned early on • [13:15] Leadership took the time • [14:40] Kjersten’s leadership style • [15:35] Transition to healthcare • [18:00] Lessons learned in consulting • [20:00] Building teams • [22:15] Qualifications for individuals • [29:10] Data strategy  • [33:00] Data governance • [38:00] Understanding the business aspects  • [45:20] Financial impacts • [48:20] Listener questions    Some of Kjersten's quotes from the episode: “Challenges are a constant in a domain such as data science.” “Diversity is an attribute of the team. It’s the diversity of experiences, culture, and thought.” “The process of matching price to risk is inherently done through data.” “Data strategy is interpreted in many different ways.”  “The leader needs to be able to work in a trusted way with business leaders and general managers.”  Now you can support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:00:18
February 27, 2019
#39 Communicating the Results of Advanced Analytics Projects with Matt Kuperholz - Partner and Chief Data Scientist
In this episode I talk to Matt Kuperholz. Matt currently works for PWC as a Partner in their Analytic Intelligence Area and is their Chief Data scientist. As a kid, Matt was fascinated by computers and while training to be an actuary started developing his computer science skills. This led to working as a data scientist and consulting with top tier companies. In this episode Matt and I talk about his career journey, why it’s important to focus on the real world and not just the data and how data science can be integrated into businesses. We discuss the concept of responsible AI and why the exponential growth of technology is making for an interesting world. With a background in both actuary and computer science, Matt has been working with data for over 20 years. He ran his own company in the early 2000s which included working with Deloitte Australia as they started to look at how to use data science in their business. He is now a is a partner and chief data scientist at PWC Australia. An expert in planning, executing and communicating the results of advanced analytics projects, Matt’s area of specialisation is the application of artificial intelligence and machine learning technologies to detailed and complex data. Summary · Matt’s love for computers and he he got to where he is now (00:12) · How Matt’s interest in computers led to a love for data (06:28) · Matt’s interest in martial arts and why a diversity of people matters (08:19) · Smell-testing the quality of a number, and the importance of attention to detail (09:40) · Working with limited time on a mainframe and how Matt coped with limited resources (12:09) · The early days of using AI and what it was like working in a start-up in the late 90s (15:04) · The importance of well prepared data (16:56) · How Matt keeps up to date with data and technology (21:17) · How Matt chooses what problems to tackle (23:26) · What it was like working with Deloitte (26:03) · How data can integrate into other areas of a business (28:32) · Starting with the real world problem before focusing on the data (30:26) · A recent project Matt has worked on exploring what trust looks like in a digital world (35:11) · The idea of responsible AI and how we develop checks and regulation (41:41) · How technologies are growing exponentially and causing a fast changing world (49:45) · How Matt follows his curiosity and how this has led to opportunities (52:05) · Why the data industry is worth getting into (54:48) · The importance of finding what you are into and staying true to yourself (55:53) Connect: Twitter - https://twitter.com/datafuturology Instagram - https://www.instagram.com/datafuturology/ Facebook - https://www.facebook.com/datafuturology Now you can support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
59:28
February 22, 2019
#38 How to build a world class data science team
In this episode, I talk about data scientists and ways you can attract the best talent to your team. Instead of telling your employees what they can do better, make them curious as to what they could do better. Then, I reveal the three things to look for when analyzing your pool of applicants. Once you have your team, now what? Once you have a decent pay settled, I explain the three things you will need to have for a capable team. Later, I tell you the elements, as a manager, you should be doing as rarely as possible. In This Episode: • [02:45] How to attract data scientists to your team? • [04:45] The three things to look for from your pool of applicants • [07:05] Adversity; test how they would react  • [11:00] Three things needed to run an effective team • [18:00] Managers should be doing this as rarely as possible Creating a Data Team Session Quotes: 1. “Create a learning environment and continually challenging projects to focus on their development.” 2. “People should be open-minded and willing to learn; I test this in two different ways.” 3. “A lot of people come with technical skills from other countries.” 4. “They had to code it live with about eight people watching them, no pressure!” 5. “You know the answer, and you want to tell them to get to the outcome quickly. That’s an urge you have to roll back and fight against.”  6. “Purpose is really what gets us out of bed every day.” 7. “Make yourself redundant as quickly as possible.” Resources Mentioned:  Drive: The Surprising Truth About What Motivates Us Connect: Twitter - https://twitter.com/datafuturology Instagram - https://www.instagram.com/datafuturology/ Facebook - https://www.facebook.com/datafuturology Support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz   And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
25:01
February 15, 2019
#37 Data Visualization Made Simple with Dr. Kristen Sosulski - Associate Professor of Data Visualization NYU Stern, Director of Learning Science Lab and Consultant
In this episode I talk to Kristen Sosulski who is the Data Visualization Professor at NYU Stern School of Business. She has just written the book Data Visualization Made Simple: Insights Into Becoming Visual. An interest in using technology to help students learn has led to helping people to understand how to use data visualizations to communicate insights to others. Kristen and I discuss guidelines on creating data visualizations, why presenting data visualizations is as important as creating them, and how the software needs to improve.  Dr Kristen Sosulski is an Associate Professor of Information Systems at New York University’s Stern School of Business. She teaches MBA, undergraduate, executive, and online courses in data visualization and computer programming. She is also the Director of the Learning Science Lab for the NYU Stern where she leads teams in design immersive learning environments for professional business school education.  Summary • Kristen’s journey from doing her undergraduate in Information Systems at NYU Stern School of Business to being a professor there teaching Data Visualization (00:17) • How Kristen’s love of technology led to an interest in using technology to help students learn (01:38) • The challenges of trying to create an immersive learning environment in the late 90s (02:41) • What led to Kristen working with data visualization (03:38) • How Kristen thinks about data visualization and designing data graphics (06:14) • Some guidelines and thoughts on presenting data to an audience (08:03) • How people learn to improve their data graphics (11:15) • The importance of showing your work and getting feedback (14:18) • The challenges Kristen finds when consulting for companies in data visualisation (17:08) • The value of data visualization in a data driven organisation (19:54) • Why Kristen wrote her book on data visualization and why she included case studies (21:14) • Some resources that Kristen created for the book (23:40) • Her work in building NYU’s online education and the use of learning analytics (27:11) • Why there needs to be more training in how to visualize data and to understand what it means (30:10) • Designing a dashboard for user driven storytelling (33:41) • How Kristen would like data visualization to evolve in the future (36:44) • Mistakes people make when creating visualizations (38:51) • How Kristen developed and improves her work and the value of sharing your mistakes (41:33) • The importance of understanding what your data means in the real world (42:49) Links Data Visualization Made Simple: Insights into Becoming Visual by Kristen Sosulski https://www.amazon.com/Data-Visualization-Made-Simple-Insights/dp1138503916 The Online Certificate in Visualizing Data Taught by Kristen Sosulski via NYU Stern School of Business https://www.stern.nyu.edu/programs-admissions/online-certificate-courses/visualizing-data Support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz
46:40
February 12, 2019
#36 Kickstarting 2019 With A Look Back at 2018 (Part 2): Episodes 19 to 34
A lot of listeners have asked what have been my takeaways from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 2 where I discuss episodes 19 to 34. I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways! Support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program Fyrebox - Make Your Own Quiz And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:09:24
February 5, 2019
#35 Kickstarting 2019 With A Look Back at 2018 (Part 1): Episodes 1 to 18
A lot of listeners have asked what have been my takeaway points from the 30+ discussions with the guests on this podcast so far. To launch 2019 I’ve done a look back at all episodes from 2018. This is part 1 where I discuss episodes 1 to 18. I hope you enjoy my recollection of these conversations. I’d love to hear what were your favourite takeaways! Support Data Futurology on Patreon!  https://www.patreon.com/datafuturology Thank you to our sponsors: JCU Master of Data Science - Online Program  Fyrebox - Make Your Own Quiz And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:06:27
February 2, 2019
#34 Sally Grove - General Manager of Insights
Sally is the General Manager of Insights at the Australian Motoring Services. She previously spent 10 years working in banking and today she shares her story. We speak about: * Fraud analytics in big banks * End to end analytics * Importance of fast feedback loops * Shocks of early working life * Balancing speed & accuracy * 80/20 vs 95/5 * Exposures in strategy & politics * Helping the business ask the right questions * Leading with the work * Career breaks: how to * Importance of working on yourself * Advantages of medium sized companies * Creating a data strategy * Balancing tactical solutions, strategic initiatives and team development * Self service analytics * Educating business stakeholders & getting their feedback * Ability to ask anything from everyone * Data science is like medicine * Leveraging multiple dimensions for career development * Knowledge sharing sessions * Getting analytics a seat at the table Show notes: www.datafuturology.com/podcast/34 Sally is based in Melbourne, Australia And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:03:47
December 20, 2018
#33 Graeme McDermott - Chief Data Officer
Graeme started in actuarial science and developed a love for algorithms and automation. He worked in data warehousing before moving into data analytics. He spent 16 years in several Head of Data roles at The Automobile Association (AA) before joining Addison Lee as their Chief Data Officer, where he is today. We speak about: * What is actuarial science * Data warehousing & GIS systems * Overview of the Chief Data Officer role * Automation in the data space * How to build a data warehouse * The difference between a data warehouse, data lake and virtual data warehouse * Starting data work with business problems/questions * How to deliver value to the business * Balancing tactical project delivery with strategic work * Enabling self service data analytics * Prioritising & sizing up work * Modern styles of work in data * Data governance: creating a plan * Creating a data strategy * How to get to a head of role * Team building * Networking Show notes: www.datafuturology.com/podcast/33 Graeme is based in London, Greater London, United Kingdom And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:03:05
December 12, 2018
#32 Carole Wai Hai - Head of Data Science & Analytics
Carole had an unusual path into data science. She's worked as a content project manager, in strategic planning and in sales before getting into data through Business Intelligence at Fyber where she eventually became their Head of Analytics. Today she is the Head of Data Science & Analytics at Tenjin. We speak about: * The strengths of being a generalist * Upskilling throughout your career * Focus on self service reporting * The skills needed in a BI team * Creating internal user groups to share knowledge * Convincing people to get training on the tools required to do their job better * The benefits of gaining a reputation internally * Setting a strategy for data teams * The importance of data modelling skills in data teams * Learning technology on the job when you're background is not technology * Monthly meeting with key departments to review all dashboards in the department * Working remotely in global companies * Metrics about user behaviour * Offering analytics for many customers with the same problem/need * How to develop consulting skills * The platinum rule - book on communication style * The leadership challenge - book recommendation * What it's like working in startups * How to recover from being a workaholic Show notes: www.datafuturology.com/podcast/32 Carole is based in Berlin Area, Germany And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:00:59
December 4, 2018
#31 Scott Wilson - Founder & CEO
Scott started his career pushing trolleys at Woolworths. In his career he rose to management levels in retail with Woolworths, consumer goods with Kraft Foods, Fonterra SPC and PZ Cussons, then in media with 21st Century Fox. He then became the CEO of iSelect, a role he left earlier this year to start his own AI company Wilson AI. We speak about: * Focus on customer needs * Digitising industries to access more data * Helping companies in multiple industries to begin their data analytics journey * How to differentiate your company when competitors have access to the same data * How to overcome being "data rich but insight poor" * Changing industry power dynamics through data * Creating new teams to create value from data * The importance of storytelling in data science * Defining objectives with your data analytics communication * Educating industries to use data more effectively * Understanding costs & priorities across the value chain to make better decisions * Eliminating your biases when dealing with customers * Process re-engineering & AI * How to think outside of the building * How to start an AI company * The importance of translating between business and technical * How to connect data science and the boardroom * The importance of data science education in an organisations journey * How to achieve a wider spread adoption of AI * Focusing on cost & revenue with data science for maximum impact * Resist the urge to boil the ocean * The role of a CEO in a publicly listed company * Focusing on the top 3 business priorities * Productionising AI & monitoring unintended consequences Show notes: www.datafuturology.com/podcast/31 Scott is based in Sandringham, Victoria, Australia And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:04:46
November 28, 2018
#30 Aaron Black - Chief Data Officer
Aaron started his career working in accounting and building management information systems (MIS). He had his own company, worked in multiple industries and then got into biology and genomics. Today he is the Chief Data Officer at the Inova Translational Medicine Institute. We speak about: * How to take research into scaled applications * The importance of sharing your knowledge and helping others understand * Why you're only as good as your team members * How to engage many different types of stakeholders * Challenges of data management in healthcare * Data governance & provenance in healthcare * Data monetization & it's stigma in healthcare * The benefits of data sharing consortiums * The potential of genomic & DNA data * Handling algorithm biases * Enabling reproducible research through data * Why "perfection is the enemy of good" * The importance of creating & sharing your mental models Show notes: www.datafuturology.com/podcast/30 Resources: Weapons of Math Destruction https://weaponsofmathdestructionbook.com Evernote https://evernote.com Real time board https://realtimeboard.com Mind jet - mind mapping https://www.mindjet.com Aaron is based in Washington DC Metro Area, USA And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:00:34
November 21, 2018
#29 Dr. Klaus Ifflander - Chief Analytics Officer
Klaus started his career doing internships at Yahoo! and the port of Hamburg. He worked as a consultant and completed a PhD in Quantitative Marketing. Today he is the Chief Analytics Officer at YAS.life We speak about: * The importance of getting applied experience as early as possible * Defining KPIs for businesses * Using data to change organisational behaviour and increase safety * How to navigate organisations to create data definitions * Realities of consulting: positives and negatives * Why large companies require so much custom work * How to help people and organisations that don't know what they want * Helping organisations in progressing through their analytics journey * How to overcome technical challenges with creative solutions in your projects * Why honesty within yourself and others is imperative in your work * How to provide customers what they need instead of what they want * The importance of hard and soft metrics when measuring value * Applying soft skills in data science * How to find what will be valuable for your customers * Expanding your interest with a postgraduate degree * How your social surroundings affect your purchase decisions * Using soft skills for data acquisition * What is eigenvector centrality and what is it used for? * How product reviews influence your buying decisions * How to create experiments in business * Pricing models in the steel business * Data science in fitness startups Show notes: www.datafuturology.com/podcast/29 Klaus is based in the Berlin Area, Germany. And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:01:12
November 12, 2018
#28 Jennifer Prendki - VP of Machine Learning & Data Strategist
Jennifer started her career as a particle physicist before becoming a data scientist. After gaining experience in many fields including high frequency algorithmic trading & advertising, she was Atlassian's first Chief Data Scientist. Today she is the VP of Machine Learning at Figure Eight and an Expert and Advisor at the International Institute for Analytics. We speak about: * How to see the results of your work sooner and faster * The importance of choosing your manager * Making data strategy decisions for companies that are very immature in their approach to data * Building data science teams from scratch * Combining impostor syndrome and leaps of faith for your benefit * The importance of making mistakes to be successful * What having a great data culture really means * How to convince peers and supervisors on the benefits and the path of data strategy * Differences between having a technical and non-technical manager * Combining technical abilities and business sense * The importance of customer contact for technical people * Focus on the impact and outcome of everything that you're building * How to keep the balance in teams * Pleasing customers vs product intuition * How to drive and create a data driven culture * How to create scale with your data science efforts * How to build your data science team * Data engineering vs Machine learning engineer * How to keep talent * How can data scientists learn the skills for business leadership * Active learning and building products for data scientists Show notes: www.datafuturology.com/podcast/28 Jennifer is based in Mountain View, California And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:08:01
November 5, 2018
#27 Dr. Mark Nasila - Chief Analytics Officer
Mark used to be a statistics lecturer at Nelson Mandela University in South Africa. He then joined First National Bank as a quantitative analyst where he climbed through the ranks to Head of Advanced Analytics and beyond. Today he is the Chief Analytics Officer at FNB. We speak about: * Predicting what the customer is calling about * Improving compliance in banking through analytics * Creating and driving a data strategy across an organisation * Using analytics to look after customers in better ways * How to create and measure economic value from data * How to find meaning in your work * Understanding your value across the entire value chain * Creating a culture of collaboration that's not afraid to fail * Working with tertiary institutions to identify talent * What to test when interviewing data scientists * How to structure your team & work with stakeholders * The importance of data governance * How to implement and socialise the solutions created by the team for maximum impact * The importance of mentoring and growing people * The difference between head of analytics and chief analytical officer Show notes: www.datafuturology.com/podcast/27 Mark is based in the Johannesburg Area, South Africa And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
54:22
October 29, 2018
#26 Sam Robertson - Head of Research & Innovation
Sam's background is in sports & exercise science. He has an accomplished career in sport analytics. Today, he is the Head of Research and Innovation at the Western Bulldogs and an Associate Professor at Victoria University. We speak about: * Using ML to help people see the non-linerarity in their problems * Common misconceptions of ML * Interpretability of ML * Using ML to improve athletes performance, measure their contribution & prevent injuries * Carving a data science job in an area you're interested in * How to choose projects to focus on * Mixing psychology, operations and data science in sport * Data collection & management in sport * How data can help off field & the mental side of the athletes * Similarities of data in sport and government/ corporate * How athletes change when fatigued * Applications of sports analytics * How data can help create drills to improve player performance & skills * Current modelling challenges in sport * Real time decision making in game by coaches: challenges and realities * Educating stakeholders Show notes: www.datafuturology.com/podcast/26 Sam is based in Melbourne, Australia And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
55:02
October 23, 2018
#25 Ben Taylor - Chief Al Officer & Cofounder
Ben started his career as a chemical engineer. He developed an interest for computer vision early on. He worked for Intel, then at a hedge fund and then became the Chief Data Scientist at HireVue. A couple of years ago he started his own AI startup called Ziff.ai where he's is building a Deep Learning platform for product visionaries and software engineers. We speak about: * How computers amplify us * What it looks like to start your own AI company * How to switch programming languages * Downsides of Google's tensorflow * What industry expects from data science * How to deliver value with ML * How to pick ML projects to tackle * Eliminating bias in AI applications * AI powered job interviews of the (near) future * Topic discovery with DL * AI warfare in business * What is a Hive Mind and how it works * Future health care assessments at home * AI is cute until it's scary * The importance of passion and obsession in data science Show notes: www.datafuturology.com/podcast/25a Articles by Ben on Linkedin: This is Why Your Data Scientist Sucks: https://www.linkedin.com/pulse/why-your-data-scientist-sucks-benjamin The Al War Machine: Our Darkest Day https://www.linkedin.com/pulse/ai-war-machine-our-darkest-day-ben-taylor-deeplearning-/ The Al War Machine: The Hive Mind https://www.linkedin.com/pulse/ai-war-machine-hive-mind-ben-taylor-deeplearning- Getting That Data Science Job https://www.linkedin.com/pulse/getting-data-science-job-ben-taylor-deeplearning-/ From 0 to $100K+ data science job in 6 months https://www.linkedin.com/pulse/from-0-100k-data-science-job-6-months-ben-taylor-ai-hacker/ Ben is based in the Provo, Utah Area And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:31:53
October 15, 2018
#24 Mistakes in Building a DS Capability by Felipe Flores
This is a different type of episode! This episode is a presentation I recently did at a large financial services institution. I presented on 5 Mistakes and Lessons Learned in Driving Business Value with Data Science and the Cloud. I talk about: - Using Lean Startup and Design Thinking principles in Data Science - The importance of staying close to your end customer and what that looks like in practice - The difference between machine learning for machines and for humans - What is the purpose of ML/AI and how you can bring that thinking into your organisation - What using ML for humans looks like - Using data from other areas - Leverage the flexibility of the cloud Show notes: www.datafuturology.com/podcast/24 Slides: http://bit.ly/df-5mistakes Felipe is based in Melbourne, Australia And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
26:48
October 9, 2018
#23 Mario Vinasco - Marketing Analytics and Data Science Mgr
Mario is an Electrical Engineer from Colombia. He went to Silicon Valley to do his Masters at Stanford University and stayed to build a career in Marketing Analytics. He has incredible experience and has worked at Intuit, Google, HP, Symantec and Facebook. He currently works at Uber as Marketing Analytics and Data Science Manager. We speak about: - Starting in marketing analytics without knowing anything about it - Data dictators and why multiple versions of the truth are necessary - The importance of data science education in organisations - How to pick the best predictive model for your applications - How to use people analytics - Google style - Why your job is to empower your stakeholders - How to stand out during interviews Show notes: www.datafuturology.com/podcast/23 Mario is based in the San Francisco Bay Area And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
1:11:39
October 2, 2018
#22 Kshira Saagar - Head of Analytics and Data Science
Kshira has been with the Analytics/Decision Sciences industry for almost a decade now having worked across Americas, Asia, Europe and Australia. He is the Head of Analytics and Data Science at The Iconic. We speak about: -Why he moved from analytics consulting to building data products -What a data driven product should do and how to prioritise your efforts -How to make analytics less intimidating and more accessible -How to take your stakeholders on the data-driven decision making journey in next the best way -How to structure your team for maximum impact in your organisation -Most common issues and roadblocks in creating a data driven culture and how to overcome them Show notes: https://www.datafuturology.com/podcast/22 Data Scientist job https://github.com/theiconic/datascientist Kshira is based in Sydney, Australia And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. It really helps new data scientists find us. Thank you so much, and enjoy the show!
59:48
September 25, 2018
#21 Antony Ugoni - Director, Global Matching and Analytics
Antony stumbled into his love of predictive modelling at the tender age of 10. He started his professional career in biostatistics and then made the switch to corporate Australia to work as Head of Analytics in banking before joining Seek; where today he is the Director of Global Matching and Analytics. We speak about: how analytical thinking adds value both in research and in corporate why you should read an intro to epidemiology textbook how analytics can take you to any field  why this is the most exciting time to be in analytics his transition from research to corporate surprises and rewards of moving into corporate how to use the constraints you have for your benefit and how to love what you do the rewards of moving to corporate what proper use of data looks like how you can help organisations find the value in their data how to present and explain the need for experiments in business the importance of educating your organisation on data science how to focus on value in your organisation how to take people on a humble, thought-provoking and non-intimidating journey into the use of data science how to understand the “grey” in business and appreciate people’s journeys why you should get close to the sales people in your organisation thoughts on the very high demand of data scientists data science for social good and why analytics professionals are like Batman how to stand out in data science interviews and much, much more! Antony's textbook recommendations: Statistical Models in Epidemiology Statistical Methods in Medical Research, 4th Edition Applied Logistic Regression - Wiley Series in Probability and Statistics Case Control Studies : Design, Conduct, Analysis Epidemiology Principles and methods  Thank you to our sponsors:  UNSW Master of Data Science Online: studyonline.unsw.edu.au  Datasource Services: datasourceservices.com.au or email Will Howard on will@datasourceservices.com.au  Fyrebox - Make Your Own Quiz! Antony is based in Melbourne, Australia And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!
1:11:15
September 18, 2018
#20 David Greenberg - Senior VP and Head of Data Analytics
David studied applied physics and began his career as a consultant. He’s had his own company where he created a video asset management & workflow software in the 90s!. Then worked in the education/not-for-profit sector and then went into the finance sector as VP of BI & Data Analysis. Today, he is the Senior Vice President and Head of Data, Analytics and Research at BankMobile. We discuss: - the insights into large companies from his early days in consulting - why technology provides the “guard rails” for the business - why our roles as data scientists is to make sense of the mess - what’s missing in today’s analytics education and how to learn what you need - what to look for when building a diverse team - the importance of creating a narrative in analytics - the mindset to maintain during your analysis - motivations behind problems with data definitions - how data is like a flashlight Show notes: www.datafuturology.com/podcast/20 David is based in Providence , Rhode Island, USA
1:15:28
September 10, 2018
#19 Vlad Kazantsev - Head of Data Science
Vlad started his career in visual effects & computer graphics. He worked on Hollywood blockbusters such as Avatar, Dark Knight, Happy Feet. He currently is Head of Data Science at Wooga which makes June’s Journey (Facebook Game of the Year 2017), Pearl’s Perils, Diamond Dash and many more. We speak about: - why it’s important to follow your curiosity and what that looks like how to keep learning and stay current in data science - uses of pytorch, the second deep learning python library after tensorflow which is backed by facebook - relevant metrics & analytics in the gaming industry - statistical modelling, machine learning & deep learning in gaming - considerations for deploying ML models to production - the importance of speed in delivery of work & reproducibility of data science - how to keep innovating for your customers - what is the semi-embedded model and much, much more! Show notes: https://www.datafuturology.com/podcast/19 Vlad is based in Berlin, Germany
58:17
September 5, 2018
#18 Dr. Ahmed Khamassi - Vice President of Data Science
Ahmed started his career at Siemens research, worked in startups, at Google, PayPal, SAS and JPMorgan and has his own machine learning company and now he runs data science at Equinor We speak about: - benefits of simulations in research and data science how he went from “equation-driven” to “data-driven” - the role of simulations in optimisation, decision-making and automation - uses of simulations and deep reinforcement learning models in the energy industry - how data is used 2-5kms underground below the sea to infer the properties of the ground underneath - lessons from startups and what to look for in people to work with - why it’s important for data science teams to own the engagement of value creation with the customer - how to ensure that your data science team is creating value in your organisation - how to prioritise the work done by your data science team and what to aim for; and much much more! Show notes: www.datafuturology.com/podcast/18 Ahmed is based in London, UK
58:02
August 28, 2018
#17 Naomi Clarke - Head of Data
Naomi Clarke started as a graduate in the oil business, since then she's worked in multiple industries, and now she is Head of Data in the finance sector. Naomi has a strong background on business data arch, business data modelling, data governance.  I loved the human-centred perspective that she has taken to her work. We talk about: - the Management Information Systems (MI or MIS) she created in her early days - the importance of business data models for analytics - the difference between a logical and physical data model and which one is more important - how to define the right meaning of the data in your data models - disruptions in the financial sector that happened overnight - the relationship between deregulation, dematerialisation and digitalisation; and how its affecting industries - the tight link between business, data and culture; and how each one affects the others and much, much more! Show notes: https://www.datafuturology.com/podcast/17 Naomi is based in London, UK
1:14:13
August 21, 2018
#16 Apollo Gerolymbos - Head of Data Analytics
In this episode we speak with Apollo Gerolymbos who is the Head of Data Analytics at the London Fire Brigade. We speak about: - the applications of data science in firefighting - how the London Fire Brigade (LFB) uses data to preempt and minimise fires - the end to end data science process at the LFB - how their data affects laws, policies and citizens’ lives - how Natural Language Processing (NLP) and text analysis is used on the reports of the most serious fires to identify new patterns of high risk factors - the importance of identifying bottlenecks and weak points in the availability of your service - why it’s important for data scientists to educate non-data people in their organisations and much, much more! Show notes: https://www.datafuturology.com/podcast/16 Apollo is based in London, UK ------------------ Also, catch me at the Chief Data & Analytics Officer Conference in Melbourne on September 3-5, 2018 https://chiefdataanalyticsofficermelbourne.com
1:12:27
August 14, 2018
#15 Tony Laing - General Manager Analytics & Data Services
In this episode we speak with Tony Laing who is the General Manager of Analytics & Data Services at Auto & General. We talk about: - what is the ‘nuts and bolts’ of analytics - what is the data supply chain required in organisations for the delivery of analytics/ML solutions - how to deliver quick wins and strategic projects concurrently - the journey to add significant value in an organisation through analytics - what questions to ask executives to kickstart their data science journey - why is there so much turnover in data science - why data preparation and model building is only 20% of the job - what type of model is the best to drive commercial outcomes - whether ML applied in specific domains is AI or not - the art of data preparation, increases in computing power and automation of data science - what is “the knife fight” of data science and what type of companies can benefit the most Show notes: https://www.datafuturology.com/podcast/15 Tony is based in Brisbane, Australia
1:21:41
August 7, 2018
#14 Dr Gabriel Maeztu -Medical Doctor & Chief Data Scientist
In this episode we speak with Dr Gabriel Maeztu who is the Co-Founder and & Chief Data Scientist at IOMED Medical Solutions. We talk about: - his background, how he went from medicine to data science and how he combines medicine, data science & entrepreneurship - how to start coding when everyone around you tell you you’re crazy - image processing in medicine, using scikit learn to classify patients - how to use data science to validate what you’re taught in medical school - economical Incentives of the medical system that is probably slowing down progress in the data space - GAFAs: Google Apple Facebook Amazon in medical data - value based care built on data science - NLP/text processing in medicine - current & future data challenges in medicine and much, much more! IOMED is hiring data scientists! https://angel.co/iomed/jobs/379740-data-scientist Show notes: https://www.datafuturology.com/podcast/14 Gabriel is based in Barcelona, Spain
1:15:57
August 1, 2018
#13 Ernesto Bernado - Chief Product & Marketing Officer
In this episode we speak with Ernesto Bernardo, he is the Chief Product & Marketing Officer at iContainers. We talk about: - whether leaders of tomorrow should be technical or not - how to create autonomous teams that focus on value (ROI) & pay for themselves - how creating a data science team that deliver value in the business forces you to go from a centralised to a decentralised team - why creating a data-driven culture in your organisation requires good marketing and great people skills - how the reporting lines of a data science team and significantly affect the teams’ impact in the business - the importance of networking within your company to drive adoption and change the culture - how to stand out in data science interviews & what managers look for - actionable metrics: focus on what you can control/influence to change the metrics you care about - i.e.: what’s the input/driver Show notes: www.datafuturology.com/podcast/13 Ernesto is based in Barcelona, Spain
59:06
July 25, 2018
#12 Alessandro Pregnolato - Director of Analytics
In this episode we speak with Alessandro Pregnolato, he is the Director of Analytics at Typeform.com. We talk about: - his journey to get where he is, - what is the optimal size of a data science team, - how to use data science in SaaS businesses/startups - the 4 pillars of a great data strategy - how to be an expert generalist in the data space and much more! Alessandro is a Business Analytics Leader with a love for Data Science. He has over fifteen years experience within the domain of BI, Analytics, Big Data and Machine Learning in international environments. He has strong management and communication skills with a demonstrated ability to work well under pressure with people from a variety of backgrounds. Show notes: www.datafuturology.com/podcast/12 Alessandro is based in: Barcelona, Spain
1:32:28
July 17, 2018
#11 Takaharu Tsuda -Practice Director & Head of Data Science
In this episode we speak with Takaharu Tsuda, Practice Director & Head of Data Science at Think Big Analytics. We talk about: - his journey to get where he is, - how applications of data science in many Japanese industries, - the translation of data science into Japanese and why it's hurt the industry! - the 3 layers of skills required in data science and much more! Show notes: www.datafuturology.com/podcast/11 Tak is based in: Tokyo, Japan
1:01:42
July 10, 2018
#10 Jonathan Hart - Head of Data Science & Analytics
In this episode we speak with Jonathan Hart, Head of Data Science & Analytics at MullenLowe Profero. We talk about: - his journey working in the US, UK, India and Japan, - how to create great Data Science teams and a great culture, - how to use data science in strategic decision-making - how to work with teams all over the globe and much more! Show notes: www.datafuturology.com/podcast/10 Jonathan is based in: Tokyo, Japan
1:12:42
July 4, 2018
#9 Matt McDevitt - Director of Data Engineering
In this episode we speak to Matt McDevitt, Director of Data Engineering at Think Big Analytics. We talk about: - his journey working in the US, UK, Europe and Japan as the company grew, - how big data, open source, data engineering and data science work together, - General Data Protection Regulation (GDPR), Personally Identifiable Information (PII), data lineage - business value, data products and much more! Matt is one of Think Big’s earliest team members playing many roles to help incubate and build Think Big over its 8-year history into the leading Big Data Analytics Global brand. He helped build from scratch and establish Think Big practices in the United States in Mountain View, Salt Lake City, New York, London and Toyko. Matt assisted in the development of Think Big’s innovative Velocity Delivery methodology, which integrates Data Engineering and Data Science in 6-week release cycles. Show notes: www.datafuturology.com/podcast/9 Matt is based in: Tokyo, Japan
1:03:08
June 26, 2018
#8 Agile Data Science by Felipe Flores
This is a different type episode! This is a recording of a presentation I did to about 300 data scientists in Melbourne, Australia. The theme of the night was Agile Data Science, a passion of mine. In this episode I cover: - the productivity gains an individual and a team can gain using agile methods - how agile is imperfect but very helpful - how I've tweaked agile to fit data science and deliver value with my teams - bust some of the main myths around agile, and much, much more! The show notes and presentation slides are in https://www.datafuturology.com/podcast/8 I hope you enjoy the episode! I am based in Melbourne, Australia and currently travelling for a few months!
52:04
June 20, 2018
#7 Sandra Hogan - Group Head of Customer Analytics
In this episode we speak to Sandra Hogan, Group Head of Customer Analytics at Origin Energy. We talk about: - how to successfully apply data science, - how to gauge your stakeholders ability to consume analytics, - what is analytics for good, the importance of mentorship in data science and much more! Sandra has extensive experience in the Marketing Sciences field, predominantly in re-engineering business processes to maximise customer relationship and customer experience outcomes. She's passionate about translating complex customer data into easy to use tools and processes. Her expertise spans embedding analytical capabilities and data driven decisions into business processes to achieve significant improvements across sales and marketing functions. Show notes: www.datafuturology.com/podcast/7 Sandra is based in: Melbourne, Australia
59:36
June 12, 2018
#6 Dr Mark Blakey - Ex-Managing Director
In this episode we speak to Mark Blakey, Ex-Managing Director at his own technology company Mainstream Consulting. We talk about: - how to combine entrepreneurship and data science, - how to create small teams that punch above their weight, - how to scale data-driven products using machine learning and much more! He founded and led Mainstream Consulting, a technology company, for 20 years. During that time he built a blue-chip client base of top tier high street banking customers including CBA, NAB, ANZ, Barclays Bank, Abbey National plc, Legal and General Bank, Woolwich plc, Mortgage Trust plc, Clydesdale Bank, Yorkshire Bank and many others. Mark is currently the founder and organiser of popular Melbourne meetup group on applications of machine learning to stock market prediction: https://www.meetup.com/Machine-Learning-Applied-to-Stock-Market-Predictions/ Show notes: www.datafuturology.com/podcast/6 Mark is based in: Melbourne, Australia
1:42:33
June 7, 2018
#5 Dr Anthony Rea - Chief Data Officer
In this episode we speak to Dr Anthony Rea, Chief Data Officer at The Bureau of Meteorology of Australia. We talk about what happens to over 30 petabytes of weather data in one of Australia's largest super computer, how to combine data governance and policies with culture and technology, details of the World Meteorological Organisation or WMO - an international data exchange program, how to create machine learning (ML) and data steward communities in your organisation and much more! Anthony has a background in remote sensing and physics. During his career he has worked on every aspect of data science and now is an executive leader at the Bureau of Meteorology of Australia. Show notes: https://www.datafuturology.com/podcast/5 Anthony is based in: Melbourne, Australia
58:06
May 29, 2018
#4 Dr Sam Kharazmi - Head of Data Science
In this episode we speak to Dr Sam Kharazmi, Head of Data Science at RedBubble. We talk about how to focus on product & users with your data science efforts, how data science can add value to company growth at different stages of the company's journey, how to combine people leadership & tech leadership to better drive business outcomes and much more! Sam is a Data Science, Analytics and Engineering leader with extensive experience in building teams and data product on small and large scale organisation and data science strategy. Show notes: https://www.datafuturology.com/podcast/4 Sam is based in: Melbourne, Australia
1:28:39
May 23, 2018
#3 Dr Jacek Kowalski - Chief Data Scientist
In this episode we speak to Dr Jacek Kowalski, Chief Data Scientist at Australian Unity. We talk about pragmatic and realistic data science, data science for startups and corporates, leadership in analytics, what it takes to get analytics projects through in large corporates and much more! Jacek is a highly experienced ICT manager and technical expert. His areas of expertise include Data Science, Network Analytics, Security, Virtualisation, Mobility, and Identity Management. His technical leadership and significant input into the business strategy contributed to the success of Azure Wireless and its acquisition by Docomo NTT. He's worked at a number of research institutions, major corporations and technology startups. He's published scientific papers in the area of Statistics, Network Analytics and holds technology patents. Show notes: https://www.datafuturology.com/podcast/3 Jacek is based in: Melbourne, Australia
1:33:27
May 15, 2018
#2 Ben Pattison - Head of Customer Data Science
In this episode we speak to Ben Pattison, Head of Customer Data Science at Medibank. We talk about getting buy-in from stakeholders, how to develop data scientists, the relationship with technology and business, how to define strategic priorities for your data science team and much more! Ben has over 20 years experience, in the UK and Australia, as an analytical and strategic leader, passionate about driving business and customer value from data and analytics. He has experience of leading large teams to design and integrate Credit Risk, Data Science, Decision Services and Analytical Marketing solutions into Personal, Consumer Finance, Insurance, Small Business and Wealth operations. Show notes: https://www.datafuturology.com/podcast/2 Ben is based in: Melbourne, Australia
1:27:56
May 15, 2018
#1 Dr Eugene Dubossarsky - Chief Data Scientist & Principal Trainer
In this episode we speak to Dr Eugene Dubossarsky, Chief Data Scientist at AlphaZetta and Principal Trainer at Presciient.com We talk about data literacy, questions to ask your potential employer, his definition of actionable insights and much more! For upcoming Data Science, Machine Learning and R courses go to: http://presciient.com/current-courses/ Eugene is a Strategic Advisor in all aspects Data Analytics - Capability building, winning support, management and operations. Community Builder. Creator of a number of data science and analytics communities, interest groups and professional associations He's the creator of ggraptR, an interactive data visualisation package in R Show notes: https://www.datafuturology.com/podcast/1 Eugene is based in: Sydney, Australia
2:15:11
May 15, 2018
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