Best Books for Data Science Beginners

Hey! Are you looking for some best books to learn Data Science from scratch? If yes, you are at the right place. Today, we’ll be discussing the 10 best books for Data Science Beginners.

There are plenty of resources available to learn data science in the form of online tutorials, blogs, etc. But, nothing can be as reliable as a book. Learning from books is probably the best way. That’s why, today, we’ll be discussing some awesome books that will make you a master of Data Science. These books will not just help you in programming but will also give you a deep intuition behind each and every concept. When you have both theoretical and practical understanding behind every concept, nothing can stop you from acing the data science interviews.

Without data, you’re just another person with an opinion

W. Edwards Deming

Why books for Data Science?

There is no friend as loyal as a book

Ernest Hemingway

So, why wait? Let’s hop onto the 10 Best Books for Data Science Beginner now!

1. Data Science from Scratch: First Principles with Python

Author: Joel Grus

Best for: Beginners

Why to read this book?

It is a great book for Data Science enthusiasts. The prose is solid and easy to follow. You’ll learn many fundamental data science tools and algorithms and implement them from scratch.

The book very clearly explains how to collect, explore, clean, munge and manipulate data using various statistical and mathematical techniques and also implement them using Python.

The unique thing about the book is that it enables you to build algorithms without using any module or library. But need not worry, the author explains each concept in a very lucid manner. You don’t need to have any statistical or programming background for that.

Major Topics covered:

  • Linear Algebra
  • Statistics
  • Probability
  • Exploratory Data Analysis
  • Fundamentals of Machine Learning
  • Machine Learning Models Intuition with implementation

Programming Language: Python

2. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Author: Aurélien Géron

Best for: Beginners with familiarity with Python and Mathematics

Why to read this book?

Absolute goldmine! Without a doubt, this book is highly recommended for Machine Learning. It is an exceptionally poignant introduction to applied Machine Learning. It successfully connects the dots between theory, techniques, and implementation in the real world.

The book delivers practical knowledge of Machine learning with scikit-learn and TensorFlow. It covers the intuition behind all the machine learning models along with their implementation in Python. It contains many practical datasets to explain the concepts. The author also maintains a public GitHub repository where all of the code and datasets used in the book are there.

Indeed one of the best books for data science beginners.

Major Topics covered:

  • Fundamentals of Machine Learning
  • Feature Engineering Techniques
  • Machine Learning Models
  • Problems associated with models
  • Neural Networks
  • Tensorflow

Programming Langauge: Python

3. Practical Statistics for Data Scientists

Authors: Peter Bruce & Andrew Bruce

Best for: Beginners

Why to read this book?

If you are only ever going to buy one statistics book, or if you are thinking of updating your library, this book would be an excellent choice.

Not every statistical concept is required for data science. This book covers each and every concept associated with data science in a very elucidated manner.

Not just that, the book is completely practical focussed. The book along with an explanation also contains code written in R. So, if you are the one who is looking for implementing the code as well, this book is for you.

The book will enable you to gain proficiency in the basics of statistics for Data Science.

Major Topics Covered

  • Descriptive statistics
  • Probability
  • Randomization
  • Sampling
  • Types of distributions

Programming Language used: R

4. An Introduction to Statistical Learning with Applications in R

Authors: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

Best for: Beginners

Why to read this book?

Precisely what it sounds like! This book is an absolute goldmine when it comes to the way of explanation. This book provides a solid introduction to Machine Learning Algorithms based on statistical principles. It walks through various forms of statistical learning, regression, classification, SVM, tree-based methods along with code written in R. The book puts more emphasis on the practical applications of the concepts.

This book is suitable for individuals in quantitative fields who wish to use statistical tools to analyze the data.

Not just algorithms, it also includes statistical techniques behind resampling techniques like cross-validation.

This book is completely practical focussed. You can practice along with understanding the concepts.

Programming Language used: R

5. Python for Data Analysis

Authors: Wes McKinney

Best for: Beginners

Why to read this book?

This book is a practical introduction to scientific computing in Python and is tailored for data-intensive applications. It explains the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. This book explains about analyzing the data step by step using NumPy and Pandas and also visualizing them using Matplotlib, Seaborn.

This book also hosts all the datasets and Jupyter notebooks on a public GitHub repository which proves to be very handy for the learners.

Major Topics Covered

  • IPython
  • Numpy basics
  • Introduction to Pandas
  • Data Loading
  • Data Wrangling
  • Plotting and Visualization
  • Time Series

Programming Language used: Python

6. Mathematics for Machine Learning

Authors: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong

Best for: Beginners

Why to read this book?

This book bridges the gap between mathematical and machine learning texts. The book covers the mathematics required for Machine Learning extremely well. It gives a strong mathematical and Bayesian perspective, to understand how well one model works better than others.

It is very important that you have a mathematical understanding of the algorithms because that helps you to optimize the algorithm further and explain your observations mathematically. Programming tutorials are available on the book’s website.

Major Topics Covered

  • Linear Algebra
  • Analytic Geometry
  • Matrix Decomposition
  • Vector Calculus
  • Probability
  • Linear Regression
  • Support Vector Machines

Programming Language used: Python

7. Artificial Intelligence: A Modern Approach

Authors: Stuart J. Russell, Peter Norvig

Best for: Beginners

Why to read this book?

Do you want to get a great and most comprehensive introduction to Artificial Intelligence? Also, have you heard about AphaGo, the AI Agent developed by Google Deepmind that beat the World’s Go Champion ‘Lee Sedol’? This book is a bible for learning Artificial Intelligence.

From the traditional Search problems to Natural Language Processing, this book has it all. It brims with a lot of detail and is suited to anyone with an interest in AI.

Major Topics Covered

  • Intelligent Agents
  • Solving problems by searching
  • Probabilistic Reasoning
  • Probability
  • Natural Language Processing
  • Reinforcement Learning

8. Think Python: How to think like a Computer Scientist

Authors: Allen B. Downey

Best for: Beginners

Why to read this book?

The book is a wonderful and concise introduction to Python, and computer programming. It provides a lot of practical examples and self-study exercises. It is more targeted towards people who want to become software developers.

The text builds a great progression for learning to program in Python in an easy-to-follow manner and using non-trivial and insightful examples that will teach you to think about the language itself and consolidate the knowledge retrieved from the book deep into your brain.

Major Topics Covered

  • Variables, Expressions and Statements
  • Functions
  • Recursion
  • Strings
  • Exception Handling
  • Object Oriented Programming
  • Stacks, Queues, Linked List

Programming Language Used: Python

9. Deep Learning with Python

Authors: Francois Chollet

Best for: Beginners with knowledge of Python

Why to read this book?

What better than reading a book written by the creator of the Framework itself! Yes, the book is written by none other than the creator of Keras Framework “Francois Chollet”. Rather than scavenging the internet trying to piece together the ideas behind machine learning, get this book for a coherent package.

The book introduces the field of Deep Learning and the powerful Keras library. It builds your understanding through intuitive explanations and practical examples. It also discusses the applications of Deep Learning in Computer Vision, Natural Language Processing, and Generative models.

Major Topics Covered

  • Neural Networks
  • Deep Learning for Computer Vision
  • Deep Learning for Texts and Sequences
  • Generative Deep Learning
  • Variational Autoencoders

Programming Language Used: Python

10. Natural Language Processing with Python

Authors: Steven Bird, Ewan Klein, Edward Loper

Best for: Beginners with knowledge of Python

Why to read this book?

If you’re interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages — or if you’re simply curious to have a programmer’s perspective on how human language works, you’ll find Natural Language Processing with Python both fascinating and immensely useful.

This book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open-source library. It strikes an excellent balance between theory and applications. It provides compelling use cases along with the actual code needed to resolve those use cases. 

Major Topics Covered

  • Processing raw text
  • Feature Extraction
  • Categorizing and tagging words
  • Access popular linguistic databases, including WordNet and treebanks

Programming Language Used: Python

Conclusion

So, in this blog, you learn about the best books for Data Science beginners. You can refer to these books to gain mastery and delve deep into the field of Data Science.

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