Learning about data science

With the increasing popularity of data science, there are now countless online tutorials on machine learningdata science and statisticsPython 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

I believe that the best way to learn data science is to blend different modes of learning. That’s why I decided to create the Datalyst Academy based on my many years of experience in data science education. What are the benefits of Datalyst?


  1. It is flexible: It can fit around anyone’s schedule, and can be done in as few as 3 months, or as long as up to 1 year or more.
  2. Topics which are best for solo-learning, like coding, are being taught online.
  3. Lectures are online, but are supported by additional content, so they can fit around anyone’s schedule.
  4. There are offline face-to-face workshops in order to facilitate social interaction.
  5. There is 24/7 mentoring and support, which helps speed up learning.
  6. The students get the chance to work on projects of their own choice, which enables them to do things they like, or related to their work.

This is why I believe Datalyst Academy is currently the best data science next generation bootcamp in the market. You want to learn more? Make sure to watch my on-demand webinar!