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Best Data Science Books for Beginners and Experienced [2024]
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26th Dec, 2023
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In recent years, with the advent of technology, data has been considered to be a valuable asset in both large-scale and small-scale organizations. Data as a resource requires skilled professionals to be collected, interpreted, and stored safely. The demand for data scientists and career prospects has skyrocketed in the job market. Budding aspirants and students are constantly looking for reliable data science s, research material, and the top data science books to kickstart their careers in this field.
With dozens of online data science courses available, it is essential to know not only specific details such as syllabus, Data Science course fees, but also the relevant books you will be referring to. Be it as a beginner or an experienced learner; you need to know which book is a reliable source of knowledge and is suited to your personal level of understanding.
We have compiled a list of the best books for learning data science, including free data science books for beginner-level, intermediate and experienced learners.
Top Data Science Books to Learn [Beginners]
As a beginner or a student starting out fresh in the field of data science, along with courses, you need beginner-level books. These specialized books will help you gain a comprehensive understanding of the basics and fundamentals of data science to get started. Here you will find a consolidated list of the best books to learn data science.
1. Practical Statistics for Data Scientists By Peter Bruce and Andrew Bruce
Source: Amazon.com
As an absolute beginner, this book will be your best friend when it comes to an understanding the basics of statistics in Data Science. Some of the reasons why this book is ideal for beginner-level students are listed below:
- It covers topics that are fundamental in the field of data science
- The language is easy to comprehend
- You will learn the basics of statistics in data science
- Important topics like distribution, randomization, sampling, and the like are covered in depth.
2. Introduction to Machine Learning with Python: A Guide for Data Scientists By Andreas C. Müller and Sarah Guido
Having in-depth knowledge of Machine Learning is integral to becoming a data science professional. Some of the reasons why this is one of the best Python for data science books are listed below:-
- You will learn about the basics of Machine Learning.
- This book has detailed and easily comprehensible knowledge about the programming language Python which is crucial in ML.
- This book will prepare you to step into a more advanced level in order to learn more about Python and Machine Learning.
3. Python for Data Analysis By Wes McKinney
Online Along with Machine Learning, you also need to learn about Python, a widely used programming language in the field of Data Analytics. Some of the reasons why this book is ideal for a beginner-level data science student are listed below:-
- This book can provide a holistic guide for beginner-level students in data science with detailed information about Data Analytics with Python.
- The book might be fast-paced but is easy to understand and simple.
- After reading this book thoroughly, you can build real applications in a week or two maximum.
- Its well-organized structure helps readers take a step ahead in the world of data analysis and know about what falls under the role of a data scientist is expected to do.
Top Data Science Books [Intermediate]
As an intermediate learner, you will need books that provide you with higher-level knowledge about this subject to prepare you for the next stage. Here is a list of some of the best books for data scientists, ideal for intermediate learners.
1. Python Data Science Handbook By Jake VanderPlas
This is the best book for data science with python if you have already covered the basics of Data Science and Python. The reasons why this book is popular amongst intermediate-level data science students are as follows:-
- It will help you explore Python libraries to work with them efficiently.
- You will have a wholesome understanding of standardized Python libraries like Pandas, Matplotlib, Numpy, Scikit-learn, and many more.
2. R for Data Science – By Hadley Wickham and Garret Grolemund
Source: amazon.com
R is a programming language also used in many Data Science applications. This book is ideal for those who have already worked with Python. This book will help you in the following ways:-
- You will learn the basics of coding with the R programming language.
- You will learn about in-depth concepts like data exploration, wrangling, programming, communication, and modeling.
Top Data Science Books To Learn [Experienced]
Advanced learners are more likely to have a strong understanding of the fundamentals of data science. In this list, you will find the best data scientist books to take you further in your career as a data scientist.
1. Deep Learning By Ian Goodfellow, Yoshua Bengio, and Aaron Courville
As an advanced learner, this book should be your Bible for learning about deep learning algorithms. You will find the reasons behind this book being a success amongst advanced students below:-
- This book is not packed with complicated codes.
- It offers an in-depth explanation of finding solutions to deep learning problems.
- The easy layout of this book consists of bullets and helpful images that make following it easy.
- This book offers a detailed introduction to why deep learning is integral in Data Science.
- You will get to learn more about backpropagation algorithms, recurrent neural nets, convnets, and even other topics like attention mechanisms, unsupervised deep learning, etc.
2. Mining of Massive Datasets By Jure Leskovec, Anand Rajaraman, Jeff Ullma
This book will provide a comprehensive understanding of large-scale data mining and network analysis. It is a highly recommended book developed based on numerous Stanford courses. Some other features that make it a book ideal for advanced learners are as follows:-
- This book will help you learn how to mine significantly large datasets.
- You will also learn about the development of large-scale production-level models in this book.
- Some of the integral topics that this book covers are MapReduce, mining data streams, link analysis, building recommendation systems, dimensionality reduction, and many more.
The Complete Collection of Data Science Books
With the ever-growing demand for professionals with expert knowledge in handling large sets of data, data science as a career is a lucrative option. It has indeed become a prospective career choice especially in 2024 and in the future years to come. If you are looking for a wholesome list of books, including one like statistics for data science book or even the best python data science books, you can refer to the below list:-
1. Programming
Programming is crucial to have an in-depth understanding of data science. It includes various programming languages like Python, R, Julia, SQL, and others. If you are pursuing a career in data science, you can refer to the list of excellent books related to programming in data science:-
A) Python
Python is a widely used programming language in both development and data science. Along with online courses that teach you what is Data Science with Python, you can refer to the following Python books listed below:-
- Python Crash Course, 2nd Edition: A Hands-On, Project-Based Introduction to Programming (Author: Eric Matthes)
- Introducing Python: Modern Computing in Simple Packages (Author: Bill Lubanovic)
- Fluent Python, 2nd Edition (Author: Luciano Ramalho)
- High-Performance Python: Practical Performant Programming for Humans (Author: Ian Ozsvald and Micha Gorelick)
B) R Programming Language
R is a programming language used mainly for statistical computing and other data science-related operations. Some of the best books that will guide you in R are as follows:_
- R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (Author: J. D. Long and Paul Teetor)
- Beginning R: The Statistical Programming Language (Author: Mark Gardener)
- Efficient R Programming: A Practical Guide to Smarter Programming (Author: Colin Gillespie and Robin Lovelace)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (Author: Garrett Grolemund and Hadley Wickham)
C) Julia
Julia is a dynamic programming language that is both easy to use like Python and as high-level as C/C++. Some of the best books that offer a detailed guide to Julia are as follows:-
- Think Julia: How to Think Like a Computer Scientist (Author: Allen B. Downey and Ben Lauwens)
- Beginning Julia Programming: For Engineers and Scientists (Author: Sandeep Nagar)
- Hands-On Design Patterns and Best Practices with Julia: Proven solutions to common problems in software design for Julia 1.x (Author: Tom Kwong)
D) SQL
Structured Query Language, also known as SQL is a standard programming language used in various functions related to data. The following books will help you master SQL very fast:-
- SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights (Author: Cathy Tanimura)
- Learning SQL: Generate, Manipulate, and Retrieve Data (Author: Alan Beaulieu)
- Practical SQL, 2nd Edition: A Beginner's Guide to Storytelling with Data (Author: Anthony DeBarros)
E) Scala
Scala is an object-oriented high-level programming language used widely in the development of ML functions. Some of the best books that will guide you in Scala are:-
- Scala Cookbook: Recipes for Object-Oriented and Functional Programming (Author: Alvin Alexander)
- Scala for the Impatient (Author: Cay S. Horstmann)
- Programming Scala: Scalability = Functional Programming + Objects (Author: Alex Payne and Dean Wampler)
2. Statistics
Statistics is the heart of developing high-level machine learning algorithms and is also involved in gathering and translating data sets and patterns for actionable use by organizations. Therefore, it is imperative that you have a comprehensive knowledge of statistics which the following books listed below will offer you in a holistic and simplistic way:-
- Think Stats: Exploratory Data Analysis (Author: Allen B. Downey)
- Think Bayes: Bayesian Statistics in Python (Author: Allen B. Downey)
- Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python (Author: Andrew Bruce, Peter C. Bruce, and Peter Gedeck)
- Naked Statistics: Stripping the Dread from the Data (Author: Charles Wheelan)
3. Data Analytics
Data Science is more than just writing code for generating the visualization of data. Here are some books that will help you understand data using visual representation and graphs:-
- Data Analytics Made Accessible: 2022 edition eBook (Author: Anil Maheshwari)
- Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (Author: Wes McKinney)
- Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures (Author: Claus O. Wilke)
- Advancing into Analytics: From Excel to Python and R (Author: George Mount)
4. Business Intelligence
Business Intelligence is an intrinsic element of modern business. By referring to the following books, you will learn about various BI tools and operations like creating reports, tracking performance, managing data sources, etc.
- The Definitive Guide to DAX: Business Intelligence for Microsoft Power BI, SQL Server Analysis Services, and Excel (Author: Alberto Ferrari and Marco Russo)
- Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (Author: Tom Fawcett)
- Mastering Tableau 2021: Implement advanced business intelligence techniques and analytics with Tableau (Author: David Baldwin, Kate Strachnyi, and Marleen Meier)
- Business Intelligence, Analytics, and Data Science: A Managerial Perspective: Sharda, Ramesh, Delen, Dursun, Turban, Efraim (Author: Dursun Delen, Efraim Turban, and Ramesh Sharda)
5. Data Engineering
Data Engineering involves various operations like creating data pipelines, strategizing data management plans, processing data, and many more. The following books will guide you through the various facets of data engineering to help you dive into this field headfirst:-
- Fundamentals of Data Engineering: Plan and Build Robust Data Systems (Author: Joe Reis and Matt Housley)
- Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems (Author: Martin Kleppmann)
- Data Pipelines Pocket Reference: Moving and Processing Data for Analytics (Author: James Densmore)
- Data Engineering with Python: Work with massive datasets to design data models and automate data pipelines using Python (Author: Paul Crickard)
6. Web Scraping
Web scraping knowledge is one of the basic requirements to become a data scientist or analyst to develop completely automated systems. Down below, we have listed some books that are widely used to learn about web scraping:-
- Web Scraping with Python: Collecting More Data from the Modern Web (Author: Ryan Mitchell)
- Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale (Author: Jay M. Patel)
- Practical Web Scraping for Data Science: Best Practices and Examples with Python (Author: Bart Baesens and Seppe vanden Broucke)
- Hands-On Web Scraping with Python: Perform advanced scraping operations using various Python libraries and tools such as Selenium (Author: Anish Chapagain)
7. Data Applications
Once machine learning models are created, the next step is to develop a web application to share projects with different team members. The following books will guide you in developing API or web apps with the help of Flask, FastAPI, Django, and Streamlit.
- Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python (Author: Tyler Richards)
- Web Development with Django: Learn to build modern web applications with a Python-based framework (Author: Andrew Bird, Ben Shaw, and Saurabh Badhwar)
- Flask Web Development: Developing Web Applications with Python (Author: Miguel Grinberg)
- Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python (Author: Francois Voron)
8. Data Management
Data management is an integral part of high-scale organizations. As a data scientist, you need to learn about various tools that will help you scale modern data systems. Some books that will help you learn about them are listed below:-
- Data Management at Scale: Best Practices for Enterprise Architecture (Author: Piethein Strengholt)
- Database Internals: A Deep Dive into How Distributed Data Systems Work (Author: Alex Petrov)
- Database Administration: The Complete Guide to Practices and Procedures: Mullins, Craig S. (Author: Craig Mullins)
- MASTER DATA MANAGEMENT AND DATA GOVERNANCE (Author: Alex Berson and Larry Dubov)
9. Big Data
Large datasets require expert professionals who have the skills to make data scalable and easily comprehensible. You will learn these in detail by referring to the following books:-
- Big Data Demystified: How to use big data, data science, and AI to make better business decisions and gain a competitive advantage (Author: D. Stephenson)
- The Enterprise Big Data Lake: Delivering the Promise of Big Data and Data Science (Author: Alex Gorelik)
- Data Strategy: How to Profit from a World of Big Data, Analytics, and Artificial Intelligence: Marr, Bernard (Author: Bernard Marr)
- Big Data: Principles and best practices of scalable realtime data systems eBook (Author: Nathan Marz and James Warren)
10. Cloud Architecture
Skills in developing cloud architecture are a bonus for data scientists because it is recently coming into the limelight amongst data communities. Some reliable books you can refer to on this subject are listed below:-.
- Kubernetes: Up and Running: Dive into the Future of Infrastructure (Author: Kelsey Hightower, Brendan Burns and Joe Beda)
- Design Patterns for Cloud Native Applications: Patterns in Practice Using APIs, Data, Events, and Streams eBook (Author: Kasun Indrasiri and Sriskandarajah Suhothayan)
- Security and Microservice Architecture on AWS: Architecting and Implementing a Secured, Scalable Solution (Author: Gaurav Raje)
- Cloud Without Compromise: Hybrid Cloud for the Enterprise (Author: Paul C. Zikopoulos, Sai Vennam, Chris Backer, Chris Konarski and Christopher Bienko)
Get Started in Data Science
Books are relevant and reliable sources of knowledge if you want to delve deep into the world of data science. You can sign up for various courses and even participate in online Bootcamps for Data Science in KnowledgeHut, to develop a deep understanding of the world of data science. However, the aforementioned books will be your guide in your journey in this field. These books will not only help you kickstart your journey in this professional field but also be great sources of reference during practicals.
Frequently Asked Questions (FAQs)
1. What book should every data scientist read?
Learn the needed skills to become a data scientist are Programming with python, the Hadoop Platform, SQL Database/Coding, and Apache SparkData Visualization, Machine Learning and AI, Unstructured data Intellectual curiosity, and communication skills. After that you have to do practice on different platforms like Kaggle, It is the most famous website for practicing data science skills.
2. Which book is best for data science beginners?
Data science is not all around coding and programming; you will require some comfortable hands in statistics and mathematics and statistics too. For the beginning, I would suggest you start with "An introduction to statistical learning by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.
3. Is Python Data Science Handbook for Beginners?
Python is also the most growing programming language in Data Science. Also, data analytics is essential to data science. Hence this data science book for beginners is a comprehensive guide for beginners in data science to learn the concepts of Data Analytics with Python. And there are lots of repositories including the entire Python Data Science Handbook, in the form of Jupyter notebooks.
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