Wanna know more about data science? Make sure to check out my events and my webinar What it's like to be a data scientist and What’s the best way to become a data scientist !

Learning about data science

With the increasing popularity of data science, there are now countless online tutorials on machine learning, data science and statistics, Python and R. Most of these tutorials follow the same pattern: learn some basic commands, go through a simple use case, apply some algorithms and discuss the results, which raises the question “what is the best way to learn about data science”?

The first thing we would have to ask would, what does the job of data scientist look like on a daily basis?

A data scientist will have to do (amongst other things):

  • Discuss the business problem with the stakeholders and convert it into a data science problem.
  • Understand the different metrics and how they relate to business outcomes.
  • Understand the algorithms, and how the various trade-offs in terms of explanatory power/predictive power/implementation.
  • Present and communicate results.

So, data science is a lot more than just loading data and playing around with algorithms in scikit-learn and R. It is easy to become very good in one of those skills, while missing the rest.

Many others resort to competitions, and that is a fine way to learn how to use the tools properly, but you are not going to face the same challenges you face in real life. Competitions is a great way to learn how to code pipelines and experiment with different algorithms. However, in a machine learning competition, the metric of the problem is given to you. You won’t have to present outcomes. You can create a solution of arbitrary complexity, as long as it drives you up the leaderboard, leading to monster ensembles of many models mixed together.


Learn data science the right way

This is why in my course I took a different turn in teaching data science. Not only I am teaching the most popular tools (R, Python and Weka), and the basic principles behind machine learning and statistics, but I do that through 3 different real-world use cases which came form my experience working on the field of sports.

For example, in one of the lectures (“injury prediction based on exposure records”), I teach you how to get a dataset and transform it so that you can answer a particular problem, while at the same time taking into account the uncertainty and data issues associated with a problem. I another lecture (“predicting the recovery time”) I teach about some of the issues you might meet when presenting sensitive results to a non-technical audience.

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Also, the course is not just for people who want to become data scientists. While R and Python and the most popular choices of languages for data scientists, Weka is an amazing tool that can be used to do machine learning through a graphical user interface. You don’t have to know how to code to analyse data. Clearly very useful for people outside of tech who want to use data science, without having to go deep into it.

So, just go to the course and join! If you want a special discount just e-mail me. Also, make sure to check out some of my other courses. Finally, if you are a decision maker within your organisation (executive, manager, founder, product manager, or otherwise), make sure to check the Tesseract Academy. The Tesseract Academy provides executive education in deep technical topics for decision makers, and it can be the fastest way to turn your organisation into a data-driven powerhouse.

Wanna know more about data science? Besides my events, you should check out my webinars:
  1. If you want to learn data science: What it's like to be a data scientist and What’s the best way to become a data scientist
  2. If you are a CEO: The importance of data strategy

Dr. Stylianos Kampakis is the owner and author of The Data Scientist.

1 Comment

Proquotient · July 6, 2017 at 7:36 am

There can be many approaches which many experts follow to teach data science, the basic intention should be to make aspirants understand the concepts by giving practical examples rather flooding them with tons of theory.

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