14 Best Data Science BooksNovember 17, 2020
Data science is applicable to almost every industry and is the hottest career in the industry today. Data Science subject book is grounded on three core disciplines; math and statistics, computer science and programming, as well as domain knowledge.
Professionals are expected to have some knowledge in each of these domains. However, building knowledge, skills, and competence is a process that goes way beyond earning the coveted data science certification and practicing. There are several types of resources available including books, videos and tutorials, journals, expert opinion, and more. It takes a personal initiative to take advantage of them.
Thinking about books, here are handy Data Science subject book packed with valuable information on data science tools and technologies, concepts, and principles, as well as illustrations and use cases.
Best Data Science Subject Book for students
Data Science for Dummies
Author: Lillian Pierson
This book brings out general big data and data science concepts in a simple way that is easy for IT beginners and learners to understand. It covers disciplines like big data, Data Science subject book, data engineering alongside other related technologies like R and Python programming languages, mathematical and statistical techniques, and others from a rather general perspective and will lay a sound foundation for those interested in venturing into the field.
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Best Data Science Book for beginners
Data Science from Scratch
Author: Joel Gurus
Data science from scratch features introductory data science content for beginners who have already stepped into the field and are ready to start building algorithms under the different subsets like AI and machine learning by first laying foundations in maths and statistics which are core to data science subjects book. Algorithms are written in Python language.
Best Data Science book for entry-level data scientists
The data science handbook
Author: Field Cady
This book is a great guide for those who want to hone their data science skills through practice. You probably have completed course work and want a data science project in your name or you have already plunged into the field and are seeking guidance on how to handle data analysis tasks. It offers great insight into analysis techniques and big data tools.
Best Data Science Subject Book for Developers and Software Engineers
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems
Author: Martin Kleppman
This book gives an overview of data systems. It covers the fundamentals and ideal properties of simple database systems and how these systems can work together with data-intensive applications. It also analyzes the suitability of various database tools and technologies and highlights common database design challenges and solutions. Ideal for system architects and software engineers who are into database design.
Best Data Science Subject Books for business owners
Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking
Author: Foster Provost and Tom Fawcett
This is a non-technical data science book written from a business perspective. With real-world illustrations, this book talks about the basic principles of data science, data mining techniques, and helps you to think data-analytically to be able to get the most out of data science for your business.
Big Data – A Revolution That Will Transform How We Live, Work, and Think
Author: Kenneth Cukier and Viktor Mayer-Schönberger
This book takes you through the fundamentals of big data, data science subject book, and AI without packing in the overly technical jargon. It takes you through the entire process that data passes through from the time it is sourced to how it influences business profitability, helps businesses develop solutions, explore opportunities, mitigate risks, and comply with security and data regulations.
Best Data Science book for visualization professionals
Storytelling with Data: A data visualization guide for business professionals
Author: Cole Nussbaumer Knaflic
This book introduces you to the other side of crunching numbers in data science; data visualization and communication. The theory in this book lets you in on the very purpose of data science which is the interpretation and communication of findings after the data analysis process. Simply put, data stories need to be told for informed decisions to be made. Practical illustrations are certainly not lacking in this book.
Best for Data Science book for data mining
Mining of Massive Datasets
Author: Jure Leskovec, Anand Rajaraman, and Jeff Ullman
Data mining is one thing and large-scale data mining quite another. This book delves into the less trodden space of data mining with large datasets. It brings to your knowledge of various data mining algorithms that are used to solve problems related to mining extremely large datasets and also touches on network analysis.
Best Data Science book for statistics
Naked Statistics: Stripping the Dread from the Data
Author: Charles Wheelan
Data science cannot be mentioned without mentioning statistics. This book explains concepts of statistics like regressions and probability simply but thoroughly and rather humorously. Wheelan highlights
Best Data Science book for probability
Probability: For the Enthusiastic Beginner
Author: David Morin
Another book authored for the beginner or the student who wishes to delve into data science by first laying the foundation on core data science concepts like probability. It covers probability basics like variance, probability density, expectation value, correlation, regressions, combinatorics, rules of probability, Bayes theorem, and more.
Best Data Science book for Machine Learning
The Hundred-Page Machine Learning Book
Author: Andriy Burkov
Andriy Burkov concisely rounds up machine learning topics like supervised and unsupervised learning, gradient descent, cluster analysis, and dimensionality reduction, support vector machines, and many more in just 100 pages. This would also include equations that are not often covered in other books. This book presents an interesting easy-read for just anyone who wants to gain some knowledge and not so much about the in-depth study.
Best Data Science Book for Deep Learning
Grokking Deep Learning
Author: Andrew W. Trask Grokking Deep Learning delivers a well-grounded introduction to the science of deep learning. It features a detailed and practical guide on building and training deep learning neural networks using Python and its supporting tools like NumPy plus much more deep learning concepts including deep learning frameworks and privacy concepts. To get the most out of this valuable resource, some high-school level math and python programming knowledge come in handy.
Best for Data Science book for R
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Author: Garrett Grolemund and Hadley Wickham
A good read for beginners who wish to get on board data science with R coding. R is big in data science and is widely used through the data science cycle. Therefore, a background in R together with familiarity with math will help you make sense of this book. R for data science explores the data science cycle from importing, transforming, visualizing, and modeling data.
Best Data Science book for Python
Mastering Python for Data Science
Author: Samir Madhavan
The book Mastering Python for Data Science ushers you to the world of data science with python. Curious to learn about python and its popular data science libraries, create visualizations and mine for patterns, and about fundamentals of advanced data science in data mining, visualization, and machine learning? This is the book to read particularly for developers.
Data science is a broad field that encompasses several disciplines. Self-study is an important aspect of building knowledge. However, you need to have laid the foundation by taking an online certification course to be able to get the most out of these books. These are just a few books that we mentioned. There are many more that you can read to build knowledge around your domain and specialty.