With the emergence of Covid – 19 at the end of the year 2019, a lot has changed till today. Many people and companies are forced to find new ways to carry on with their business. Every company or businessman is looking at ways to acquire more information and reduce cost.
Before we indulge in the discussion of the best data science books, we would briefly look into what is data science and where it is being applied? When we study different types of data, we devise ways to record, store and analyze data in such a way that we extract fruitful information from it.
Why Data Science?
The objective of data science is attaining knowledge and understanding from any type of data, whether the data is structured or unstructured. It is an independent field and is related to computer science. Computer science requires generating programs and algorithms to record and analyze data.
While data science covers all types of data analysis which may or may not apply the skills of computer programs. Data science is closer to statistics which is a field of mathematics. Statistics requires gathering, re-arranging, analyzing, and presenting data.
Nowadays a large amount of data is normal for big organizations and corporations, to store, manage and analyze this data the IT services are utilized.
Below we will look into the top 20 books on data science.
The Elements of Data Analytic Style
This book is written by Jeffery Tullis Leek (Jeff Leek). Jeff Leek is a Professor at John Hopkins Bloomberg School of Public Health. He is a biostatistician and a data scientist.
The Elements of Data Analytic Style is for people who are regularly working on data analyses. The objective of this book is to present a short synopsis of the main practices, pitfalls, and ideas of modern data analysis. It can also be the lighthouse to associate reviewers who can refer to exact sections when analyzing documents.
This book is available in PDF format as well as e-book and kindle format. It has a total of 15 chapters. It is available on the Leanpub website and is published by them as well.
The Art of Data Science
The Art of Data Science is written by Roger D. Peng and Elizabeth Matsui. This book has 10 chapters and is published by bookdown.
Roger D. Peng is the Co-Founder of Johns Hopkins Data Science Specialization, there are over 1.5 million students enrolled with it. He is also a blog writer and a Pod Caster. He is a Professor at the Johns Hopkins Bloomberg School of Public Health. He is a Professor of Biostatistics.
Elizabeth Matsui is Pediatrics, Epidemiology, and Environmental Health Sciences Professor. She is also at Johns Hopkins University. She is also a pediatric allergist and immunologist.
Introduction to Data Science.
This book provides vital information in layman’s terms to those who are not well versed with data science. It gives the readers a mild introduction to the essential concepts and activities about data science. For those who are knowledgeable about the subject, it offers code for a variety of interesting applications using R language which is open source.
This book suits more to introductory-level students who have a diverse background. The book is authored by Jeffery M. Stanton Ph.D. He is a professor of Information Studies at Syracuse University.
The Data Science Handbook.
This book is co-authored by four persons. The co-authors are Carl Shan, Henry Wang, William Chen, and Max Song. This book has in it 25 detailed interviews of amazing and outstanding data scientists. In these interviews, these data scientists share their adventures, observations, and opinions.
These interviewees belong to an expansive background, different industries, and varied disciplines. For example, Clare Corthell has carved her path in data science by coming up with an Open-Source Data Science Masters curriculum, which is assembled by collecting freely available resources from the internet.
Others like DJ Patil and Hilary Mason captured the attention of the nation through their extraordinary work.
Doing Data Science.
This book is published by O’Reilly Media, Inc. This book is written by Cathy O’Neil and Rachel Schutt. It is available in PDF format as well as e-book format. This book discusses how new joiners can start in this field since it has come into focus very quickly in recent months.
This book is on the pattern of Columbia University’s data science introductory class. It provides you case studies by many data scientists who are working with companies like Google, Microsoft, and eBay. They present methods, models, case studies, algorithms, and codes used by these data scientists.
Data Science for Dummies
This book is the 3rd Edition. This book is the perfect stepping stone for those who wish to start their career in Data Science, it is also equally good for IT professionals who need basic information on the subject. This book discusses topics on big data, data engineering, and data sciences and how the combination of these three areas produces value.
The author of this book is Lillian Pierson, she is the CEO of Data-Mania. She has trained over a million people in data science and Artificial Intelligence. Lillian Pierson has worked with governmental, non-governmental entities, global IT leaders, and famous media corporations.
Data Jujitsu: The Art of Turning Data into Product.
What is data jujitsu? Using various data types in smart ways to solve insistent problems. A combination of these solutions is used to solve larger data problem which otherwise would not seem solvable. It is the art of using the adversary’s force against himself instead of using your force to overpower the opponent.
This book is written by the famous mathematician and computer scientist Dhanurjay “DJ” Patil. He has rendered his services as the Chief Data Scientist of United States Office of Science and Technology policy from 2015 to 2017.
Mining of Massive Datasets.
This book is devised for computer science undergraduates who have no previous training in data science. This book further explores the topics by providing more reading references. The 3rd edition of this book was published, it contains chapters on TensorFlow, minhashing, algorithms, and decision trees. A new chapter of deep learning has also been added in the 3rd edition.
This book is written Jure Leskovec, Anand Rajaraman, Jeff Ullman. Jure Leskovec is an Assistant Professor of Computer Science at Stanford University. Anand Rajaraman is an academic, venture capitalist, and serial entrepreneur who is positioned in Silicon Valley. Jeffery David Ullman is a Professor of Computer Science (Emeritus) at Stanford W. Ascherman.
Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems.
This book is a hands-on comprehensive and pragmatic guide that is designed to assist you to navigate the data-intensive application landscape. It guides you by investigating the plus and minus of various mechanics for analyzing and storing data. The computer programs and their way of response are always changing but the basic ideas and core regulations remain constant.
This book teaches software experts and programmers how to take maximum benefit by using data in modern applications.
This book is written by Martin Klepmann. He is a software engineer, an entrepreneur, a musician, a Senior Research Associate, and Affiliated Lecturer.
Data Science from Scratch: First Principles with Python.
It is necessary that you understand the ideas and principles behind data science tools, like frameworks, toolkits, data science libraries, and modules. You need to master these tools if you want to fully understand data science itself. This book is designed on Python 3.6 and it teaches algorithms and tools starting from scratch.
This book is packed with the latest material on statistics, natural language processing, and deep learning. With a little background knowledge of mathematics and statistics, a little knack for programming you can begin your journey as a data scientist.
R Programming for Data Science
Wherever you go data science is the hottest topic now. This is the most discussed topic and is connected to almost all types of businesses and fields. It is all about extracting the correct information from the heaps of data that is out there. Only a trained professional can do that extraction and make sense of it.
The programming language R has become the actual language for data science. Because of its sophistication, fluency, pliability, and the potential it has become a priceless tool for data science experts. This book is authored by Roger D. Peng.
Python Data Science Handbook.
A python is a premium tool primarily because of its libraries for manipulating, analyzing, and storage of data. Multiple books are available which study these tools individually. This book Python Data Science Handbook discusses all of them in one place. For example, Pandas, Matplotlib, NumPy, IPython, Scikit-Learn, and many others.
This reference guide as well as a comprehensive desk encyclopedia is a must-have for all data scientists and data analysts who are working with Python code. It assists data crunchers and scientists in dealing with day-to-day matters like transforming, cleaning, and manipulating data.
In simple terms, it is a must-have Quick Reference Guide for scientific computation in Python.
Data Mining and Analysis: Fundamental Concepts and Algorithms
This book studies the automated methods to manipulate patterns and models for all types of data, with its utility ranging from analytics to business intelligence to scientific discovery. This book teaches and gives an expansive overview of data mining, interrelating, and combining concepts from machine learning and statistics.
This book discusses in great detail classification, pattern mining, data analysis, and clustering. This book establishes the building blocks of these tasks and also discusses other topics like high-dimensional data analysis, kernel methods, and complex graphs and networks.
Mathematical Foundations of Data Science Using R.
This book lays down a solid foundation of mathematical nature to handle data accurately and efficiently. Since data is being generated in enormous quantities and is also generated in a vast spectrum of areas. Because of this, data science has gained international acceptance. Overtime R programming language has become an indispensable tool while working with data sciences.
This book is useful for a multilevel audience, from professionals to undergraduates, and graduate students. This book has something for everyone. It comprises many illustrations that are essential for grabbing the concepts of data science.
Machine Learning and Big Data.
This book discusses multiple programming languages. The reason behind it is to ensure a balance is achieved between theory and application for the software engineer to apply machine learning models comfortably without depending too much on libraries. It often happens that the idea behind a technique and model is simple or innate but it gets lost in intricacies or jargons.
Another reason would be that existing libraries would solve the iterating issues, but libraries are treated as black boxes. These black boxes have their own considerations and planning, which conceal the underlying concepts. This book is an attempt to highlight these concepts.
Essentials of Metaheuristics.
This book is about metaheuristics algorithms, it is a set of open lecture notes which are presented for non-experts, programmers, practitioners, and undergraduate students. These are lecture notes delivered by the author at George Mason University. The chapters of this book are arranged in such a way that they can be easily printed separately.
This book is written by Sean Luke who is a professor in the Computer Science Department at George Mason University. The best way to utilize this book is to complement it with other texts. This book has short topics and sparse examples.
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Data based analysis is reshaping the forecasting, design, and management of complex systems. This book combines engineering mathematics, mathematical physics, and machine learning to assimilate modeling and management of dynamical systems with a modern mechanism in data science.
It features many of the latest advances in scientific computing that enable the data-driven mechanism to be applied to a varied range of complex systems, epidemiology, finance, turbulence, climate, the brain, robotics, and autonomy.
This book is directed towards advanced undergraduate and novice graduate students in the engineering and physical sciences, the text demonstrates a range of topics and mechanism from introductory to advanced level.
Social Media Mining: An Introduction.
Social media has grown by leaps and bounds in the previous years and it is still growing. This growth has changed the way we interact with each other. It has revolutionized the way industries conduct their business. A huge amount of data is produced when humans interact through social media by sharing, chatting, interacting, and viewing content.
This has given an opportunity to researchers to collate and analyze data for interdisciplinary research. It also helps in developing tools, enhance algorithms, and study patterns. This book looks into data mining through social media and helps all types of students to improve their understanding of social media mining.
Thinks Stats: Probability and Statistics for Programmers.
This book as the name states introduced probability and statistics to Python language programmers. It places its emphasis on simple and easy techniques used to analyze real data sets and answer intriguing questions. This book also offers a dive into a case study using real data from the National Institute of Health.
It requires the reader to have basic Python skills. Applying these basic skills, the reader can understand concepts of statistics and probability. This book offers many examples and exercises, these exercises use short programs to perform operations for the reader’s understanding.
This book offers extensive insight into the study of convex optimization. It teaches in detail how a problem can be resolved efficiently by using numerical methods. The book studies beginners’ elements of convex sets and functions. Later on, it sheds light on various classes of convex optimization problems.
This book is available in multiple formats. For example, e-textbook, digital and paperback.
Above we have listed the top 20 books which are available in e-book format, PDF format, and traditional paperback format. These books give valuable insight and training on data sciences. As we have already discussed data science has now taken center stage. Huge amounts of data are being compiled and research is being carried out on them. It is extremely important that the data analyzed by man and machine should be presented in an understandable manner. This can only be done by data science experts. The above list offers a glimpse into the number of books available to learn and practice data science.
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