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: Zoltan's Linkedin Profile: https://www.linkedin.com/in/preko/ RapidMiner: https://rapidminer.com/ Prezi: https://prezi.com/ Netflix Competition: https://en.wikipedia.org/wiki/Netflix_Prize Kaggle: https://kaggle.com/ StackOverFlow: https://stackoverflow.com/ Secret Sauce Partners: https://secretsaucepartners.com/ -------- Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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. Youtube video format: https://youtu.be/XfiN6OGEfVY Original article format here: https://data36.com/find-data-science-mentor/ LINKS MENTIONED IN THE EPISODE: Meetup.com Newsletter: https://data36.com/newsletter Free mini-course: https://data36.com/how-to-become-a-data-scientist/ IMAGE SOURCES: wikipedia Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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? Youtube video: https://youtu.be/t4KnjpbRzos Original article format here: https://data36.com/is-r-required-for-data-science/ LINKS MENTIONED IN THE EPISODE: https://data36.com/learn-sql-for-data-analysis-from-scratch/ https://data36.com/learn-python-for-data-science-from-scratch/ https://trends.google.com/trends/explore?date=2010-09-01%202020-09-28&q=python%20for%20data,r%20for%20data https://businessoverbroadway.com/2019/01/13/programming-languages-most-used-and-recommended-by-data-scientists/ https://www.kdnuggets.com/2018/11/most-demand-skills-data-scientists.html https://bestbet.data36.com/ Newsletter: https://data36.com/newsletter Free mini-course: https://data36.com/how-to-become-a-data-scientist/ Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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.  Youtube version: https://youtu.be/yHfRQwOkhJY Original article format here: https://data36.com/machine-learning-algorithms-for-juniors/  LINKS MENTIONED IN THE EPISODE: https://data36.com/linear-regression-in-python-numpy-polyfit/ https://data36.com/jds/ http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ Newsletter: https://data36.com/newsletter Free mini-course: https://data36.com/how-to-become-a-data-scientist/ Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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!   Youtube version: https://youtu.be/cv0a6X5ioNs Original article format here: https://data36.com/soft-skills-data-scientist/  LINKS MENTIONED IN THE EPISODE:  https://data36.com/presentation-tips-for-data-professionals/  https://www.16personalities.com/  https://data36.com/productive-data-scientist-not-more-smarter/  Newsletter: https://data36.com/newsletter  Free mini-course: https://data36.com/how-to-become-a-data-scientist/  IMAGE SOURCES:  - Photo by NeONBRAND on Unsplash   MORE: Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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. Youtube version: https://youtu.be/aUgo988Ssl4 Original article format here: https://data36.com/domain-knowledge-data-scientist/   LINKS MENTIONED IN THE EPISODE:  - How to Become a DS: https://data36.com/how-to-become-a-data-scientist/  - 7 Books to Get Started with Data Science: https://medium.com/@datalab/wannabe-data-scientists-learn-the-basics-with-these-7-books-1a41cfbbdd34  - https://prezi.com/  - https://www.izettle.com/  - 6-week data science course: https://data36.com/jds/   Check my website: https://data36.com  Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle  Find me on Twitter: https://twitter.com/data36_com

Should an Aspiring Data Scientist Learn More About Big Data? No. And in this episode, I'll tell you why.   Youtube version: https://www.youtube.com/watch?v=StpXg0frfGE Article format here: https://data36.com/big-data-junior-data-scientist/  LINKS MENTIONED IN THE EPISODE: Image, here: https://data36.com/big-data-junior-data-scientist/  Apache Hadoop: https://hadoop.apache.org/  Apache Spark: https://spark.apache.org/ Python API (Spark): https://spark.apache.org/docs/latest/quick-start.html#self-contained-applications SparkSQL: https://spark.apache.org/docs/latest/sql-programming-guide.html  PySpark with Pandas: https://spark.apache.org/docs/latest/sql-pyspark-pandas-with-arrow.html  Newsletter: https://data36.com/newsletter  Free mini-course: https://data36.com/how-to-become-a-data-scientist/  IMAGE SOURCES: - own presentations   Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

In this episode, I'll answer a frequently asked question - which is: "What does a data scientist's day look like?"   Youtube version: https://youtu.be/B7Cr5AMkWVQ Original article format here: https://data36.com/data-scientists-day/  LINKS MENTIONED IN THE EPISODE: Mindmapping tool: https://miro.com Newsletter: https://data36.com/newsletter  Free mini-course: https://data36.com/how-to-become-a-data-scientist/   IMAGE SOURCES:  Photo by Sincerely Media on unsplash.com   Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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.  Original article format here: https://data36.com/learning-data-science/  Youtube video here: https://youtu.be/44xhV7PJB7g LINKS MENTIONED IN THE EPISODE: Data server tutorial: https://data36.com/data-coding-101-install-python-sql-r-bash/ LinkedIn Workforce Report 2018: https://economicgraph.linkedin.com/resources/linkedin-workforce-report-august-2018 Glassdoor.com -- best jobs in the US 2019: https://www.glassdoor.com/blog/best-jobs-in-america-2019/  Glassdoor.com -- best jobs in the US 2018: https://www.glassdoor.com/blog/best-jobs-in-america-2018/ Shift Happens 2018 Data36 Newsletter: https://data36.com/newsletter Free mini-course: https://data36.com/how-to-become-a-data-scientist **** Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com

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: Expected Value Formula + Calculations: https://data36.com/expected-value-formula/ Statistical Bias Types: https://data36.com/statistical-bias-types-explained/ Newsletter: https://data36.com/newsletter Free mini-course: https://data36.com/how-to-become-a-data-scientist/  ********** Check my website: https://data36.com Get access to more data science tutorials, join the inner circle: https://data36.com/inner-circle Find me on Twitter: https://twitter.com/data36_com **********