Data36 Data Science Podcast

In this episode, I had a guest on the podcast: Zoltan Prekopcsak (VP of Data Science and Analytics at Prezi, previously VP of Data Science at RapidMiner). I asked Zoltan to share his story focusing on one question: How did he get his first data science job -- or in other words: how did he become a data scientist?

It's a pretty interesting and exciting interview, where you can hear about the first steps of a now-senior data professional. Zoltan talks about his side projects, about the data science competition he enrolled in with his friends (back in 2007!), about his first internship and eventually his first junior data scientist position.

MENTIONED IN THE EPISODE:

Finding a data science mentor is tricky… Well, finding a mentor for any profession is tricky!

You know, the best minds that you would love to learn from are quite often very busy and hard to get in touch with. In this article, I’ll show you a few tips and tricks that helped me find a data science mentor back in the day — and I think you can take advantage of these, too.

There are three popular languages for data scientists: SQL, Python and R. I create a lot of tutorials about SQL and Python on my blog… But I never talk about R. So the question provides itself: Is R required for data science?

I get way too many questions from aspiring data scientists regarding machine learning. Like what parts of machine learning learning they should learn more about to get a job. And I don't want to disappoint you -- but the thing is that when you get started as a junior, ninety five percent of your projects won't be about Machine Learning. At least, that's a rough average. So what parts of machine learning should you learn more about when preparing for your first job?

Well. None? :-)

Okay, that's not true. There are some parts that you'll have to know about. I'll talk more about that in this episode. 

I’ve talked a lot already about the three primary skills you need to know for data science: coding, statistics and business thinking… But it’s worth listing those secondary soft skills that you might need to be efficient and successful in day-to-day work as a data scientist. In this episode, I'll talk about these!  

Is it important for data scientists to have domain knowledge in a specific field? Sure it is! You can't come up with meaningful conclusions, and drive results from your data science projects, if you don't know the business you are in. That's sort of self-evident. But how important it is exactly and how much domain knowledge should you have before you apply for a specific data position? In this podcast episode I'll explain everything.

Should an Aspiring Data Scientist Learn More About Big Data? No. And in this episode, I'll tell you why.  

In this episode, I'll answer a frequently asked question - which is: "What does a data scientist's day look like?"  

Did you flirt with the idea of learning data science? You are not alone. This has been a really hot topic in the last few years and it will be one in the upcoming few, for sure. Yet, very few people actually become data scientists. 

Why?

 Well, part of the problem is that many aspiring data scientists don’t know what to expect from this field. Or even worse, based on the many misleading (sometimes scammy) “how to become a data scientist” articles, they have false expectations. And when they hit the wall, they get demotivated and quit.

In this podcast episode, I want to show you four untold truths that you should know about learning data science – and I have never seen them written down anywhere else before. 

In this podcast episode, I'll tell you why you shouldn't go to casinos. 

The house always wins. We all know this phrase. But this is more than a phrase. This is a simple, mathematically proven fact. And you'll only have to know three statistical concepts to see why the house always wins.  (These three statistical concepts come up often in data science projects, too. So if you are wondering why I’m talking about gambling on a data science channel, rest assured, you'll be able to take advantage of this knowledge in your data science career, too.) 

Anyways, three statistical concepts.

These are: 

• Survivorship Bias
• Expected Value
• Hot-Hand Fallacy  

Youtube vid version: https://youtu.be/MkfPALtnDG8 

FUN GAME TO TEST YOURSELF:

• https://bestbet.data36.com

LINKS MENTIONED IN THE EPISODE:

In this podcast episode I’ll answer a very simple question: What is data science?

Well, the question is simple, sure… But the answer is rather complex.

The problem is that there are no generally accepted definitions. But in this article, I’ll show you many important aspects of data science. And by the end, you’ll have a pretty clear mental picture about what it is exactly!

You'll learn:

• What data science is
• What skills you need to do it
• The real meaning of all the "buzzwords"... (Machine Learning, Artificial Intelligence, Deep Learning, Predictive Analytics…)
• Data science project examples
• and more...