10 Best Data Science Books

Data is one of the most crucial aspects of any company since it allows executives to make choices based on evidence, statistics, and trends. Data science, a diverse field, emerged as a result of the expanding breadth of data. It extracts information and insight from massive amounts of data using scientific methodologies, processes, algorithms, and structures.

Consider the rise of data generated by IoT or social data at the edge. Looking farther forward, it is estimated that there will be 11.5 million employment opportunities in data science and machine learning by 2026.

The best way to start learning data science is through books. Here is a list of the 10 best data science books that you can read to learn it.

10 Best Data Science Books

1. Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Practical Statistics for Data Scientists 50+ Essential Concepts Using R and Python

Author: Peter Bruce, Andrew Bruce
Publisher: O’ Reilley
Edition: 2nd
Available in: Kindle, Paperback

Although only a tiny number of data scientists have undergone formal statistical expertise, statistical approaches are an essential part of data science. The subject of basic statistics is rarely approached from a data science perspective in courses or publications.

This popular guide’s second edition includes advice on what’s essential and what’s not, thorough Python examples, practical help on utilizing statistical approaches to data science, and information on how to avoid their misuse.

Many data science resources include statistical approaches but often lack a comprehensive statistical viewpoint. This fast reference bridges the gap in an easy, readable way if you’re acquainted with the R or Python computer languages and have some experience with statistics.

What you’ll learn

  • Why is exploratory data analysis such an important first step in data science?
  • How random sampling may eliminate bias and produce a higher-quality dataset even with large amounts of data
  • How experimental design concepts offer conclusive solutions to questions
  • How to Estimate Outcomes and Detect Inconsistencies Using Regression
  • The most important categorization strategies for determining which category a document belongs to
  • Methods of statistical machine learning that “learn” from data
  • Techniques for deriving information from unlabeled data via unsupervised learning

You can buy this book from here.

2. Python Data Science Handbook: Essential Tools for Working with Data

Python Data Science Handbook Essential Tools for Working with Data

Author: Jake Vanderplas
Publisher: O’ Reilley
Edition: 2nd
Available in: Kindle, Paperback

Python is a first-class tool for many academics because of its packages for saving, manipulating, and extracting information from data. Individual components of this data science stack are available elsewhere, but the Python Data Science Handbook is the only place to get them all: Python, Matplotlib, Pandas, NumPy, Scikit-Learn, and other associated tools.

This holistic desk guide is perfect for full working researchers and data analysts who are accustomed to writing Python code for dealing with day-to-day problems such as converting, modifying, and summarizing data; visualizing various types of data; and utilizing the information to build statistical or machine learning prototypes. Simply stated, this is the essential guide to Python scientific computing.

What you’ll learn

  • IPython and Jupyter are Python-based computing environments for data scientists
  • For efficient storing and handling of dense data arrays in Python, NumPy contains the ndarray module
  • Pandas includes the DataFrame for storing and manipulating labeled/columnar data efficiently in Python
  • Matplotlib is a Python library that allows for a wide range of data displays
  • Scikit-Learn is a Python library for implementing the most significant and well-known machine learning algorithms in a fast and clean manner

You can buy this book from here.

3. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Python for Data Analysis Data Wrangling with Pandas, NumPy, and IPython

Author: Wes McKinsey
Publisher: O’ Reilley
Edition: 2nd
Available in: Kindle, Paperback

In this tutorial, you’ll learn how to manipulate, analyze, clean, and crunch datasets in Python. The second edition of this hands-on tutorial, which has been revised for Python 3.6, is jam-packed with real-world case studies that teach you how to successfully solve a variety of data analysis challenges. You’ll master the most latest versions of IPython, NumPy, pandas, and Jupiter in the process.

This book, published by Wes McKinney, the creator of the Python pandas project, gives a comprehensive, up-to-date introduction to data science techniques in Python. It’s ideal for Python developers who are fresh to data science and scientific programming, as well as new Python analysts. You may access records and related stuff on GitHub.

What you’ll learn

  • Begin with the pandas library’s data analysis tools.
  • Load, filter, transform, combine, and restructure data using adaptable tools.
  • Matplotlib may be used to create useful visuals.
  • To slice, dice, and summarize datasets, use the pandas group by facility.
  • Analyze and alter time series data, both regular and erratic.
  • With complete, detailed examples, learn how to address real-world data analysis challenges.

You can buy this book from here.

4. R for Data Science

R for Data Science Import, Tidy, Transform, Visualize, and Model Data

Author: Hadley Wickham
Publisher: O’ Reilley
Edition: 2nd
Available in: Kindle, Paperback

Discover how to use R to transform raw data into insight, understanding, and comprehension. This book introduces you to R, RStudio, and the tidyverse, a set of R programs that work together to make data science quick, fluent, and enjoyable. R for Data Science is written for readers who have no prior programming knowledge and is intended to get you started with data science as soon as possible.

You’ll acquire an exhaustive overview of the data science cycle, as well as the fundamental tools you’ll need to handle the details. Each section of the book is accompanied by exercises that will allow you to put your newfound knowledge into practice.

What you’ll learn

  • Wrangle—transform your datasets into an analysis-friendly format.
  • Learn to use sophisticated R tools to solve data challenges with better clarity and convenience.
  • Examine your data, make hypotheses, and test them rapidly.
  • Model—create a low-dimensional description of your dataset’s true “signals.”
  • Learn R Markdown to integrate prose, coding, and results while communicating.

You can buy this book from here.

5. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Data Science for Business What You Need to Know about Data Mining and Data-Analytic Thinking

Author: Foster Provose
Publisher: O’ Reilley
Edition: 2nd
Available in: Kindle, Paperback

Data Science for Business, written by renowned data science professionals Foster Provost and Tom Fawcett, leads you through the “data-analytic thinking” required to extract usable knowledge and business value from the data you collect. This book also explains the various data-mining strategies that are now in use.

Data Science for Business is based on a ten-year MBA course taught by Provost at New York University that uses real-world business challenges to teach these principles. You’ll not only learn how to increase interaction between business partners and data scientists but also how to participate effectively in data science projects at your firm.

Additionally, you’ll also learn how to think analytically about data and understand how data science works.

You can buy this book from here.

6. Data Science For Dummies

Data Science For Dummies (For Dummies (Computer Tech))

Author: Lilian Pearson
Publisher: For dummies
Edition: 3rd
Available in: Kindle, Paperback

Make the most of your company’s data and data science capabilities without having to pay a fortune for independent strategic consultants.

What if there was a single, easy-to-follow approach for ensuring that all of your company’s data science initiatives yield a high return on investment? What if you could test your data science project ideas and pick the one that’s most likely to make money while simultaneously getting your firm closer to its goals? It is true.

Lillian Pierson, a well-known data science consultant, explains the STAR Framework, which she created. A straightforward, tried-and-true method for directing profitable data science initiatives

Are you unfamiliar with the term “data science”? You need not be concerned. Parts 1 and 2 of Data Science For Dummies will cover all you need to know about data science.

What you’ll learn

  • Developing a career in data science
  • Using data to make money
  • Bettering your business decisions
  • Insights from big data visualized
  • Choosing the best data science use case
  • Putting together a data science approach
  • Data and data skills may be monetized.

You can buy this book from here.

7. Naked Statistics: Stripping the Dread from the Data

Naked Statistics Stripping the Dread from the Data

Author: Charles Wheelan
Publisher: W W Norton and Company
Edition: 1st
Available in: Kindle, Paperback

Statistics is swiftly turning into a science that Hal Varian, Google’s senior economist, has dubbed “sexy.” The real-world use of statistics continues to increase by leaps and bounds, from batting statistics and presidential polls to quiz shows and medical studies.

What is the best way to catch institutions that cheat on standardized test scores? Netflix has no way of knowing what movies you’ll enjoy. What’s behind the rise in autism cases? The correct data and a few well-chosen statistical methods, as best-selling author Charles Wheelan demonstrates in Naked Statistics, can help us answer these issues and more.

You can buy this book from here.

8. The Art of Data Science: A Guide for Anyone Who Works with Data

The Art of Data Science

Author: Elizabeth Matsui, Roger Peng
Publisher: lulu.com,
Edition: Null
Available in: Kindle, Paperback

This book explains the process of data analysis in simple and general words. The writers have vast expertise supervising data analysts as well as doing their own data studies, and they have carefully examined what provides consistent findings and what does not. This book is a compilation of their experience in a way that can be used by both data scientists and managers.

You can buy this book from here.

9. The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists

The Data Science Handbook Advice and Insights from 25 Amazing Data Scientists

Author: Carl Shan, Willian Shen
Publisher: Data science bookshelf
Edition: 1st
Available in: Kindle, Paperback

The Data Science Handbook includes honest interviews with 25 of the world’s most accomplished data scientists.

They sat down with the authors and had in-depth discussions about their jobs, individual anecdotes, data science viewpoints, and career advice.

DJ Patil, the US Chief Data Officer and one of the field’s founders share his combat stories in The Data Science Handbook. Industry experts Kevin Novak and Riley Newman, who lead the data science departments at Uber and Airbnb, respectively, will share their knowledge. You’ll also learn about up-and-coming data scientists like Clare Corthell, who created her own open-source data science master’s degree.

This book is ideal for aspiring or practicing data scientists who want to learn from the finest in the field.

You can buy this book from here.

10. Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing, and Presenting Data

Data Science and Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data

Author: EMC education services
Publisher: Wiley
Edition: 1st
Available in: Kindle, Paperback

The goal of data science & big data analytics is to use the power of data to gain new insights. The book covers a wide range of activities, methodologies, and tools used by Data Scientists. The training is reinforced and illustrated using examples that you can recreate using open-source software, and the content concentrates on concepts, principles, and practical implementations that can be applied to any industry and technology context.

What you’ll learn

  • Become a member of a data science team
  • Use a structured lifecycle strategy to solving data analytics concerns
  • Analyze huge data using proper analytic techniques and tools
  • Learn how to use statistics to make a compelling tale that will motivate company action
  • Prepare to take the EMC Proven Professional Data Science Certification Exam

You can buy this book from here.

Conclusion

Data science has become a much sought-after career option. Learning it will not only open ways to a great job but also be great for your bank account, (considering how well it pays!) Hopefully, the books mentioned in this list will help you learn data science. If there is a book that you think deserves a mention o this list, let us know via the comments.

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