Data Science from Scratch: First Principles with Python, Joel Grus

2019 Re-release, updated for Python 3.6. The author is an AI researcher at the Allen Institute for AI and a former Google engineer.

This book covers the basics of linear algebra, statistics, and probability and how and when they are used in data science with examples in Python. In addition, the basics of machine learning and the most popular Data Science models, including neural networks, are given.

Reviews note that the author focuses specifically on fundamental principles, rather than studying Python libraries.

Data Science for Beginners: 4 books in 1, Andrew Park

This book is also for beginners, but the focus here is more on the tools. The manual includes four books:

1. Python for Beginners. Basics of the language, from installation and variables to working with files. There are exercises and code examples.
2. Python for Data Analysis. Basics of Python libraries used for data processing: PyTorch, Pandas and others.
3. Python Machine Learning. How to work with machine learning in Python. Here: neural networks.
4. Python Data Science. In-depth study of Data Science algorithms and examples of real-life applications.

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, Wes McKinney

The book is from the author of the Pandas Python library, although the manual covers not only it, but also NumPy and IPython. Here are examples of how these tools can be used to process, analyze, and visualize data.

All examples and data files that the author of the book works with are available on GitHub.

The manual has two editions: the latest, 2017, is relevant for Python 3.6.

R for Data Science: Import, Tidy, Transform, Visualize, and Model Data, Hadley Wickham, Garrett Grolemund

Data Science is not just about Python. This is confirmed by the fact that this book is in the top 6 best data science books on Amazon and ranks second in the Mathematical & Statistical Software category.

Both authors of the book are actively involved in the development of the R language. In this book, they talk about how to work with RStudio and tidyverse – respectively, an IDE and a set of R packages for Data Science.

The manual is suitable even for those who have never programmed.

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, Peter Bruce, Andrew Bruce, Peter Gedeck

This is a book for those who have already learned the basics of Python and R and now want to improve in certain areas of Data Science. The focus is on statistics. According to the authors, this is a key part of data science, but very few data scientists study it separately.

The tutorial describes how to obtain high-quality data sets, analyze them, and work even with untagged data. Statistical methods of machine learning are also discussed.

Code examples are first written in R and then duplicated in Python.

Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines, Chris Fregly, Antje Barth

It is important not only to successfully develop real Data Science projects, but also to deploy them. This book is just a practical guide on how to work with Amazon Web Services. The authors teach you how to work quickly and efficiently in the cloud. Topics covered include:

• automated machine learning with SageMaker Autopilot;
• use of BERT-based NLP;
• streaming analytics using Amazon Kinesis and Managed Streaming for Apache Kafka;
• ensuring project security.

How to Lead in Data Science, Jike Chong, Yue Cathy Chang

Is your goal to become the leader of a Data Science team? This book is for you. The authors, ex-data team leaders at LinkedIn, share tips for managing a small number of people and even building strategies for an entire company.

The book is completely new – 2021. The reviews note that this is an excellent guide to building a career, even if you are still at the very beginning of your journey as a data scientist.

Ace the Data Science Interview, Nick Singh, Kevin Huo

The final book in this collection is an Amazon bestseller that will also help you build your career in Data Science. It contains 201 questions asked in FAANG interviews.

The authors are former Facebook employees. In the book, they share not only the questions, but also detailed answers, explaining the most important concepts and solutions.