For hands-on experience, Kaggle is a good place to go, a website that has a regular data science competition that everyone can take part in. There are also many open source datasets - you can analyze them and publish your results. Also study the work of other participants on Kaggle and learn from other people`s experience.


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After you learn the basics and complete all kinds of Data Science courses, try your hand at open source projects or competitions, and then start looking for work.

What do you need to be able to do?

Python programming

Knowing the basics of programming is a big advantage. But this is a rather large and complex area, and to make it a little easier to learn it, you can focus on one language. Python is ideal for beginners - it has a relatively simple syntax, is feature rich, and is often used to manipulate data.

Related books:

  • “Python for complex tasks. Data Science and Machine Learning, J. Vander Plas - A Guide to Statistical and Analytical Methods for Data Processing;
  • Ace the data science interview Kevin Huo (2021 edition);
  • Python and Data Analysis by Wes McKinney - A Tutorial on Using Python in Data Science;
  • Automating Routine Tasks with Python by Al Swigart is a good, practical book for beginners.
  • Learning Python by M. Lutz is a hands-on tutorial suitable for both novice and experienced developers.

After you learn the basics of Python, you can check out the Data Science Libraries.

Main libraries:

  • Numpy
  • Scipy
  • Pandas


  • Matplotlib
  • Seaborn

Machine Learning and Deep Learning:

  • SciKit-Learn
  • TensorFlow
  • Theano
  • Keras
  • Natural Language Processing:

Web scraping:

  • BeautifulSoup 4
  • Collect data

Data Mining is an important analytical process for data mining. It allows you to find hidden patterns in order to obtain previously unknown useful information necessary for making any decisions. This also includes data visualization - presenting information in an understandable graphical form.

Related books:

  • "Data analysis technologies: Data Mining, Visual Mining, Text Mining, OLAP" V.V. Stepanenko, I.I. Cold - description of data processing methods with examples;
  • “Data mining. Extracting information from Facebook, Twitter, LinkedIn, Instagram, GitHub ”, M. Russell. M. Klassen is a book that teaches practical techniques for data analysis using the example of popular social networks.

A good strategy is to get a base in Data Science at an online university, and then solve more complex practical problems during an internship at the company.