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Top 5 Data Science Books You Should Read in 2022

Data science is a constantly-evolving discipline that requires ongoing education to keep up with the latest tools, techniques, and trends. While there are many excellent resources available online, many people prefer to read books rather than spend their free time on the Internet.

To help you become an expert in this field, here are five great books for learning about data science:

1. Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett

Data Science for business is a book written by known data science experts Foster Provost and Tom Fawcett. This Data science study book introduces the fundamental principles of data science. This study book for data science projects helps you understand many data-mining techniques in use today.

You will also learn how to improve communication between business stakeholders and data scientists. It also helps you understand the data-analytical process and how data science methods able to support business decision-making.

2. Machine Learning for Dummies by John Paul Mueller and Luca Massaron

This book provides a comprehensive introduction to machine learning from the ground up, making it ideal for those who want to learn more about the subject but are unsure where to begin.

3. “Data Science at the Command Line” by JJ Allaire and Julia Silge

This book teaches you how to use command-line tools like grep or sed using code examples based on real-world data sets so that you can apply what you learn directly to your projects. It also includes exercises in R and Python so that you can get a broad overview of all major languages used in data science today!

4. “Practical Data Analysis with R” by John Mount

5. “Data Science from Scratch” by Joel Grus

This book teaches you how to do data science using Python, which is one of the most popular languages used in data science today. This book takes you through the basics of programming and statistics so that you can learn how to find insights in datasets and make predictions based on those insights. It also covers topics like machine learning and natural language processing (NLP) k-nearest neighbours, Naïve Bayes, linear and logistic regression, decision trees, clustering models etc.

Reading data science books is a valuable resource for anyone who wishes to learn the field, from beginners to experts. These five books rank among the best of their kind and are a must-read for anyone looking to kickstart their career in data science.

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