In addition to being one of the highest-paid and most popular subjects, Data Science will continue to be so for another decade or more. There will be enough work in data science to get you an excellent salary and growth opportunities.
That being said, there is nothing better than to read some books on this subject, to get the ball rolling. Data science is not only about computation. It also involves statistics, mathematics, probability, machine learning, programming, and more. To better understand data science principles, here are some of the best books on data science that you can read.
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10 Best Books On Data Science For Beginners and Experts
Without further ado, let’s explore these data science books that you can refer to begin your career or even if you want to enhance your skills to climb up the corporate ladder. If we miss listing any good books, do let us know in the comments section.
1. Build a Career in Data Science
Preparing for a Data Science career is not the same as studying to learn the basics. ‘Build a Career in Data Science ‘is more of a career guide than a traditional data science book.
The writers have set out to fill in the gaps between academia, get your first job, and advance your data science career. This book explores topics such as a traditional data science project’s lifecycle, how to adjust to market needs, how to plan for a leadership position, and even tips to help you handle demanding stakeholders.
Table of contents
Part 1: Getting started with Data Science
Part 2: Finding your data science job
Part 3: Settling into Data Science
Part 4: Growing in your Data Science role
2. Head First Statistics: A Brain-Friendly Guide
Like other Headfirst books, the tone of this book is friendly and conversational. The book covers a lot of statistics. It starts with descriptive statistics, mean, median, mode, and standard deviation then moves on to probability and inferential statistics such as correlation, regression, etc.
If you were a student of science or commerce in school, you might have studied all of it, and the book is a great start to refresh in depth what you already know. There are a lot of quick-to-recall images and graphics on the sides.
You will find some good real-life examples to keep you hooked on this book. Overall, this is a fantastic book to launch your journey in data science.
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Table of contents
1. Visualizing Information: First Impressions
2. Measuring Central Tendency: The Middle Way
3. Measuring Variability and Spread: Power Ranges
4. Calculating Probabilities: Taking Chances
5. Using Discrete Probability Distributions: Manage Your Expectations
6. Permutations and Combinations: Making Arrangements
7. Geometric, Binomial, and Poisson Distributions: Keeping Things Discrete
8. Using the Normal Distribution: Being Normal
9. Using the Normal Distribution ii: Beyond Normal
10. Using Statistical Sampling: Taking Samples
11. Estimating Populations and Samples: Making Predictions
12. Constructing Confidence Intervals: Guessing with Confidence
13. Using Hypothesis Tests: Look At The Evidence
14. The X2 Distribution
15. Correlation and Regression: What’s My Line?
3. Introduction to Machine Learning with Python
This is a book that will kick-start your Python Machine Learning journey. The concepts are well explained in detail, with sufficient examples, even for a layperson. The sound is compassionate and easy to understand.
ML is complex, but you should create your ML models after practicing with the book. You’ll get a good understanding of ML definitions. The book has examples in Python, but you do not need any prior knowledge of either Maths or Programming to read this book.
This book is intended for beginners and discusses essential topics in depth. However, reading this book alone would not be enough to get deeper into ML and coding.
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4. Practical Statistics for Data Scientists
Practical Statistics for Data Scientists is a book that will give you a thorough overview of all the concepts you need to understand to master data science. The book is not exhaustive but offers adequate details on all the high-level concepts, such as randomization, sampling, distribution, sample bias, etc.
Each of these concepts is well defined, and there are examples and an overview of how data science concepts are applicable. The novel also surprises us with a review of ML designs. This book covers all the topics that data science needs.
It is a quick and easy guide, but as the descriptions and examples are not comprehensive, this book alone cannot master the concepts in depth.
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5. Python Machine Learning By Example
This book is the best way to get into machine learning. The book interestingly gets you started with Python and machine learning with some classy examples, such as spam email detection using Bayes and predictions using regression and tree-based algorithms.
The author shares his insights into the different areas of ML, such as ad optimization, conversion rate estimation, identification of click fraud, etc. This beautifully adds to the experience of a reader. You might want to acquire some basic Python knowledge before starting this book.
The book will assist you in setting up the appropriate software before models are developed, revised, and monitored—all in all, a perfect book for beginners and experienced users alike.
6. Pattern recognition and machine learning
This book is something for all, whether you are an undergraduate, graduate, or advanced-level scholar. This book will cost you nothing if you have a Kindle subscription. This is one book that covers machine learning from the inside out.
It is detailed and clearly describes the concept of ML, along with examples. Few readers can find some words difficult to understand, but you should use other free tools like web articles or videos to get through.
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If you are serious about getting into machine learning, the book is a must-have, particularly detailed mathematical data analytics. Though you can use the book for self-learning, reading it alongside machine-learning courses would be a better idea.
7. Python for data analysis
True to its title, the book covers all possible data analysis approaches. It is an excellent start for beginners and covers Python’s fundamentals before moving on to Python’s role in data analysis and statistics.
The book describes everything in a super-easy way. You can create real applications within a week of reading the book. This book gives you a good idea of what you can expect from being a data analyst or scientist when you start working.
The author also offers many references to helpful resources in the book, which you will enjoy going through. All in all, it is a well-organized book with a detailed overview of the principles of data analysis.
8. Data Science and Big Data Analytics
This book gently introduces big data and its relevance to a reader in today’s digitally competitive world. Along with a case study and attractive graphics, the whole data analytics lifecycle is illustrated in-depth so you can see the fundamental workings of the entire system.
The book’s structure and flow are robust and well-structured. In addition to primary and everyday examples, one can relate to; the book also includes clustering, regression, association rules, and much more. The reader is also introduced to advanced analytics using MapReduce, Hadoop, and SQL.
9. R for data science
This book covers the principles of statistics and explains the kind of data you can see in real life, how to convert it using concepts such as median, average, standard deviation, etc., and how to map, filter, and clean the data.
The book will help you understand how raw and messy fundamental data is and how to process it. Data transformation is one of the most time-consuming activities. This book will help you learn various processing data methods to provide valuable insights.
10. Data Science for Beginners: 4 Books in 1
If you’re new to data science, this beginner’s four-book collection is for you. Together, these books provide an excellent basic understanding of data processing, Python, data science, and machine learning.
Each book offers step-by-step instructions and tutorials on how to build neural networks, manipulate data, and master the basics by leveraging the popular Python programming language.
Following are the four books: Python for Beginners, Python for Data Analysis, Python Machine Learning, and Python Data Science.
With this, we summarize our list of the ten best books on data science for beginners and experts. Please let us know which you liked the most from this list in the comments section below. If we missed any of your favorite data science books, write to us at [email protected] and add them to this list.
Hello Friends! I am Himanshu, a hobbyist programmer, tech enthusiast, and digital content creator.
With CodeItBro, my mission is to promote coding and help people from non-tech backgrounds to learn this modern-age skill!