The 5 Best Data Science Books You Should Read in 2022

The 5 Best Data Science Books You Should Read in 2022

2022年你应该阅读的5本最佳数据科学书籍

Die 5 besten Bücher über Datenwissenschaft, die Sie 2022 lesen sollten

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Hello, and welcome back to the site. It's been a while since I've posted here, but I hope to see you again  . One of the most common questions I get from many of my followers is "What is the best data science book to read?". That's a good question.

This is a subjective question, but I will try to answer it objectively and systematically. When I judge whether a non-fiction book is good or bad, I look at four criteria

Depth: It is important that the interesting book delves into the details and complexity of the subject being discussed. If you can learn (almost) everything you need to know from a book, then it is a book with depth.

It should be comprehensive. At the same time, it should be concise and to the point. If the book contains unnecessary details or does not explain complex concepts well enough, it is not a complete book.

Readability. In general, a well-written book should be easy to read. For example, a book for beginners in machine learning should explain what a decision tree is before a random forest.

Scope. The last criterion is perhaps the most important to me, namely "applicability." If the book can relate the raw knowledge to its application in the real world, its value increases exponentially.

Summary: These are the five best data science books to read in 2022!

1. machine learning simplified

Depth: 4/5

Scalability: 4.5/5

Readability: 4.5/5

Applicability: 5/5

The first book is called "Machine Learning Simplified". It was just published this year and covers the basics of machine learning, but I was impressed with how unique it is compared to other books! Whether you are new to data science or need a refresher, this book stands out in several ways.

It provides comprehensive coverage of all core concepts related to data preparation and data modeling, explaining the mathematics behind the concepts in depth. As a result, it answers not only the "what" but also the "how".

Complex concepts and algorithms are explained with applicable and intuitive examples, making the thinking less conceptual and more practical.

Most importantly, the book is complemented by a GitHub repository containing real Python code implementations for each of the topics discussed.

2. Practical Statistics for Data Scientists

Depth: 4.5/5
Completeness: 4/5
Readability: 3.5/5
Coverage: 5/5

Practical Statistics for Data Scientists is similar to the first book in that it is comprehensive and detailed, but differs in that the focus of the book is on statistics rather than machine learning.

It covers all the core concepts you need to know in statistics, including descriptive statistics, sampling distributions, hypothesis and A/B testing, and prediction.

The book also includes code snippets in R and Python, ultimately linking the theoretical concepts to practice.

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3. Doing data science

Description.
Introduction: what is data science? -- Statistical inference, exploratory data analysis, and data science processes -- Algorithms -- Spam filtering, Naive Bayes, and Dabbling -- Logistic regression -- Timestamping and financial modeling -- Extracting meaning from data -- Recommendation engines. Building user-centric products with big data -- Data visualization and fraud detection -- Social networks and data journalism -- Causality -- Epidemiology -- Lessons from the data race. Data leakage and model evaluation -- Data science: MapReduce, Pregel and Hadoop -- Student presentations -- The next generation of data scientists, arrogance and ethics.