Data science has taken over all the industries without leaving any stones unturned. Every firm is trying to leverage a panoply of data at each and every step of its operations to obtain the ultimate efficiency. It only makes sense for people to familiarize themselves with at least the basic algorithms and tools to analyze the data in their respective domains to better understand the trends and in turn, make better decisions.
And if you are already into your data science journey, you must have realized how important it was to upgrade yourself and practically implement complex algorithms for better results.
You wonder where to begin. That’s where I come to the rescue. In this article I am going to share 12 of the top free Data Science Books people must add to their list by the end of 2022.
1. Introduction to Machine Learning with Python: A Guide for Data Scientists
Author: Andreas C. Müller and Sarah
Guido
Publisher — O′Reilly
Difficulty Level: Beginners
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This book covers a variety
of Machine Learning topics in a style that is suited for beginners by
showing them how easily they can get started with building their own Machine Learning solutions.
It also goes into detail about the best practices for learning and applying
Machine Learning to solve common problems without undertaking advanced
mathematical courses.
This introductory book covers the fundamentals concepts, along with the algorithms and a few advanced methods for model evaluation and scikit-learn, a tried and tested Python tool that complements this book for a more hands-on experience of the implementation of Machine Learning.
2. Data Science and Machine Learning with R
Author: Reema Thareja
Publisher —
Difficulty Level: Beginners
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R is a crucial tool for
making sense of the vast amount of siloed data, and this book aims to guide the
readers on how to make the most out of R for Data Science. The topics of the
book covered follow the core steps in Data Science including, importing,
tidying, transforming, visualizing, and modeling of data using the R
programming language.
The book demands a level of prior knowledge of R, its packages such as tidyverse accompanied by a degree of sufficient numerical literacy. Although it doesn’t cover the entirety of the Data Science domain, the author has offered plenty of additional resources that can provide extensive coverage on the included topics.
3. Naked Statistics
Author: Charles Wheelan
Publisher — W. W. Norton & Company;
Reprint edition
Difficulty Level: Beginners
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An interesting and funny take
on the topic of Data Science, this book explains the core notions of the
subject by linking them with real-world scenarios. The book aims to
deliver the mind-boggling contents from the world of Statistics in a
comedic style, and at the same time, inspires the reader to go even deeper
into the subject.
Some of the concepts covered by the author include inference, regression analysis, central limit theorem, reverse causality, positive publication bias. Although it requires some degree of prior experience with Statistics, it succeeds at delivering the intended knowledge in a manner that is highly unique.
4. Practical Statistics for Data Scientists
Author: Andrew Bruce, Peter C. Bruce,
and Peter Gedeck
Publisher — O′Reilly
Difficulty Level: Intermediate
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Preferably aimed at Data
Science professionals with prior experience with the programming language R
and Statistics, this book presents the essential notions of the subject in
a handy way to facilitate learning. It also emphasizes the usefulness of the
various concepts from the Data Science and Statistics world along with its
purpose.
Practical Statistics for Data Scientists explains the core notions from the subject by relating them with practical examples from the past and the more recent years that are relevant to the Data Science industry. Even though it does cover a majority of the concepts, if not all, the book recommends additional reading.
5. Python for Data Analysis
Author: Avinash Navlani , Armando Fandango, Ivan Idris
Publisher —
Difficulty Level: Intermediate
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As the title of the book
suggests, it focusses heavily on the practical implementations of
Python for Data Analysis, to primarily analyze structured data stored in a
variety of forms. It goes into the details about the role of Python, its broad
collection of libraries for Data Analysis related tasks, and the benefits it
provides for Data Science.
Essential
Python libraries covered in this
book include NumPy, pandas, matplotlib, IPython, and SciPy. The
author starts with IPython and includes the rest of the libraries along the
way.
It also covers the fundamentals of Python programming as a quick refresher for readers with little to no Python programming experience.
6. Deep Learning
Author: Ian Goodfellow, Yoshua
Bengio, and Aaron Courville
Publisher — The MIT Press
Difficulty Level: Beginners
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Essentially targeted towards university students learning about Machine Learning, Deep Learning, and Artificial Intelligence and those programmers who rapidly want to learn about Machine Learning. The book covers all the introductory sections for Machine Learning, including the mathematical sections and moves on to Deep Networks, covers Deep Learning, and Deep Generative Models. The author has mentioned loads of insights to understand what Machine Learning is and how one can implement it for solving modern-day problems.
7. Hands-On Machine Learning with Scikit-Learn and TensorFlow
Author: By Aurélien Géron
Publisher — O’Reilly Media
Difficulty Level: Beginners
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If you have zero knowledge
about Machine Learning, this book will be the right choice for you as it takes
on the task of equipping you with the right tools, concepts, knowledge, and the
mindset to understand what Machine Learning is. The author has covered the
various techniques included in the subject and explained it with the help of
many production-ready tools and environments, such as Python’s
TensorFlow, Scikit-Learn, and Keras.
The book aims to deliver a more hands-on experience on the topics with a wide range of examples while giving less attention to theoretical content and encourages its readers to dive deeper into the practical implementation.
8. Introduction to Statistical Learning
Author: Gareth James, Daniela Witten,
Trevor Hastie, Robert Tibshirani
Publisher — Springer
Difficulty Level: Beginners
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This book serves as a guide
to Statistical Learning, which essentially translates to a set of
tools for modeling and understanding data. Covering the various techniques in
the subject, the book puts more emphasis on the practical applications of
the several concepts instead of its mathematical implementation.
It successfully delivers several complicated topics in a more simplistic and hands-on style to facilitate the learning process by including the R programming language. It does require an understanding of the statistical terms and concepts to make full use of this book.
9. Python Data Science Handbook
Author: Jake VanderPlas
Publisher — O’Reilly Media
Difficulty Level: Intermediate
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The goal behind this handy
book is to present the various concepts in Data Science not as an entirely new
domain, but merely as a new skill. According to the author, Data Science can be
best explained as the intersection between hacking skills, substantial
expertise of a domain, and the know-how of the maths and statistics in
the said domain.
The book assumes that the reader has basic experience of Python to create and manage the flow of a Python program, and therefore, focusses primarily on teaching the implementation of Python and its stack of noteworthy libraries in Data Science.
10. Data Science from Scratch
Author: Joel Grus
Publisher — O’Reilly Media
Difficulty Level: Beginners
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If you’re curious to learn about how the various algorithms, libraries, frameworks, and other toolkits in general, work in Data Science, then this is the right book for you. Instead of teaching you about the core aspects of Data Science first, this book takes the opposite route and starts with the very fundamentals of the tools that make Data Science possible and gradually touches upon the various concepts of Data Science along the way. The prerequisites for the book include a prior understanding of mathematics and programming skills.
11. Think Stats
Author: Allen B. Downey
Publisher — O’Reilly Media
Difficulty Level: Beginners
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Think Stats offers an introduction
to practical tools for exploratory data analysis and follows the
author’s style of data processing. The book follows the computational approach
rather than the traditional mathematical approach for the primary reason for
encouraging the readers to use Python code for better readability and clarity.
The idea behind this book is
to present a project-based approach where the readers can pick
a statistical question, a dataset and apply every technique they learn to that
dataset.
The author has also mentioned numerous freely available external references for the topics that require them, such as Wikipedia.
12. Deep Learning with Python
Author: François Chollet
Publisher — Manning Publications
Difficulty Level: Expert
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Deep Learning with Python
talks about making Machine Learning and Deep Learning available to a vast
audience by using Python and its library Keras. Covering the
essential background on Artificial Intelligence, Machine Learning and Deep
Learning, the book then focusses on Keras’ implementation for Deep
Learning.
The author then moves on to
cover the practical applications of Deep Learning and its
related notions with a healthy amount of code examples. It will be a suitable
choice for a majority of technically capable readers, such as data scientists,
deep-learning experts, and graduate students, as it requires proficiency in
Python.
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