Data science sources to learn

If you want to know everyhing about data science, here are a list of blogs, books and youtube chanels which can help you.


The list:

  • Go practice. The author of the resource, Oleg Yakubenkov, is one of the most famous specialists in the field of product analytics in the segment. In his articles, the essence is always very accurately conveyed.
  • Small data analysis. Alexander Dyakonov’s blog about ML. There is not only useful theory and practice here, but also many features, details, analysis of delusions. For this, I really appreciate the author. Every data science agency would recomend it.

It is also worth subscribing to his telegram channel. There he writes about Machine Learning, Deep Learning, Data Analysis, and Data Science.

  • Uber Blog. This is where Uber talks about its data solutions. You can read about Michelangelo – Uber’s machine learning platform – or how the company has shaped a better data culture.


The list:

  1. “Moneyball. How mathematics changed the world’s most popular sports league ”, Michael Lewis

It is an inspiring story of transforming the sports industry with a data-driven approach. There is a film adaptation of the book – “The Man Who Changed Everything.”

From the Editor: The methods described in the book have changed the way we work in European football. English clubs have adopted this experience in order to use mathematics and algorithms to find more suitable players and coaches for themselves. We wrote about this here.

  1. “Analytical culture. From data collection to business results ”, Karl Anderson

A practical guide to implementing data-driven governance.

Editor’s Note: Carl Anderson talks about building predictive business models. The book is based on the experience of data analysts and Scientists from various industries.

  1. All of Statistics, Larry Wasserman

A compact book on statistics for those who already have an understanding of the topic.

From the Editor: The book is suitable for those who want to quickly learn about probability and statistics.

  1. Learning from Data

A book and course of the same name for the university level. Covers machine learning fundamentals that are often not covered in other courses.

From the Editor: Readers receive 100 problems and exercises in order to master the material and deal with more complex topics.

Telegram channels

The list:

  •  BigQuery Insights.

Analytics in Google BigQuery, examples of solutions and SQL queries, insights, life hacks and tips for working with data.

Editor’s Note: Channel author Alexander Osiyuk is an analyst at MacPaw. The channel has information about working with geodata and examples of game analytics using Data Studio.

  •  All about AV tests.

The best A / B testing materials in one channel.

From the Editor: Another channel of Alexander Osiyuk. He explains, for example, what the sample mismatch ratio is and how to work with it, or how to fix the 4 major mistakes in A / B testing.


Data expert Nikolay Valiotti runs a Telegram Business channel about analytics, visualization, Data Science and BI. Here you can learn about SQL and working with databases, building analytical metrics and reports, interesting libraries for Python, working with API (from Google Docs to social networks for beer lovers), BI and SQL tools, data visualization and dashboards.


3Blue1Brown. Awesome YouTube channel that helps you understand the basics of mathematics through visualization.

From the Editor: Topics that can be found on the channel include epidemic modeling, advice on how best to solve problems, or what the derivative paradox is.

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