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Analytics Insight has listed the top Data Science books you must read in 2021.
Job opportunities in Data Science are thriving in the global market with lucrative salary packages from reputed organizations. Eminent educational institutes are offering exclusive curriculum including online certificate courses for aspiring data scientists across the world. Thus, we can say that there is ample scope in the field of Data Science to deal with data management and machine learning algorithms if one has sufficient knowledge about it. There are multiple sources such as blogs, journals, classes, and videos to learn about different aspects of Data Science and its models. Yes, it is an overwhelming and strenuous field as well as time-consuming to explore certain areas. But if you are an avid book-reader, this article is just for you! Analytics Insight has listed some of the top Data Science books that you must read in 2021 before entering into the data-centric world. You can find the following books and many more on Amazon at a budget-friendly price.
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Top Data Science books you must read in 2021
Essential Math for Data Science by Hadrien Jean
Publisher: O’Reilly (30 September 2020) with 250 pages. ISBN-10: 1098115562
Hadrien Jean has written this book, ‘Essential Math for Data Science’ for aspiring data scientists who need to take control of data with fundamental calculus, linear algebra, probability, and statistics. It does not matter if some aspiring data scientists lack expertise in mathematics, this book will provide the fundamentals of mathematics needed for Data Science, machine learning, and data management. It will teach the methods to use mathematical notation to understand new developments as well as Python and Jupyter notebooks to plot data and represent equations. Aspiring data scientists can perform dataset manipulative vectors, matrices, and tensors with the use of TensorFlow and Keras.
A Common-Sense Guide to Data Structures and Algorithms by Jay Wengrow
Publisher: O’Reilly (30 June 2020) with 250 pages. ISBN-10: 1680507222
The author, Jay Wengrow, wants aspiring data scientists to take a practical approach to data structures and machine learning algorithms with modern techniques in JavaScript, Python, and Ruby. This second edition includes special chapters on recursion and dynamic programming by using Big O notations in daily work. The readers can learn to solve tricky problems and create fast-pacing machine learning algorithms. They can also gain sufficient knowledge of advanced data structures like binary trees, hash tables, and graphs to scale social networks and mapping software through this Data Science book. It includes chapters on data structures, the importance of algorithms, an in-depth description of Big O, Recursive, and many more.
The Art of Data Science: A Guide for Anyone Who Works with Data by Roger D. Peng and Elizabeth Matsui
Publisher: Lulu.com (8 June 2016) with 170 pages. ISBN-10: 1365061469
This is one of the most popular Data Science books that describes the process of data analysis in simple terms for aspiring data scientists. Data analysis is, indeed, a difficult process for beginners to understand. Thus, this book shows that Data Science is an art and has multiple tools such as linear regression, classification trees, random forests, and many more. It takes a keen data scientist to assemble all the available tools and apply these to transform data into meaningful in-depth insights. The authors have written down the process of data analysis with minimal technical details to produce coherent results and types of failures to be faced in these processes.
Data Science from Scratch: First Principles with Python by Joel Grus
Publisher: O’Reilly (12 April 2019) with 408 pages. ISBN-13: 9781492041139
Joel Grus considers that aspiring data scientists should understand the ideas and principles before mastering the tools and modules through this Data Science book. This book shows how the tools and machine learning algorithms work by implementing the principles from scratch. The author has packed new chapters on deep learning, statistics, recommender systems, network analysis, MapReduce, database and NLP in this second version. It also includes some hacking skills to be professional data scientists with the knowledge of mathematics and statistics at the core of Data Science. The readers can also learn about the fundamentals of machine learning models like decision trees, neural networks clustering as well as linear and logistic regression.
Data Smart: Using Data Science to Transform Information into Insight by John W. Foreman
Publisher: Wiley (22 November 2013) with 432 pages. ISBN-10: 111866146X
There are multiple concerns about what is Data Science in the minds of aspiring data scientists as well as business leaders. One can have a better understanding of Data Science through this amazing book. It will show the process of transforming relevant information into in-depth insight within a familiar environment of a spreadsheet. It will boost confidence in the reader’s mind by teaching the tricks of the trade through spreadsheets. It consists of several chapters that include mathematical optimization, clustering through k-means, data mining in graphs, supervised AI through logistic regression, and shifting from spreadsheet to R programming language. It has readily applicable topics with a tinge of humor from the author to make it more interesting.
Data Science for Dummies by Lillian Pierson
Publisher: For Dummies (31 March 2017) with 384 pages. ISBN-10: 9781119327639
This is one of the most popular Data Science books for working professionals and students who are aspiring to be data scientists in their careers. This book acts as a guide to them to transform all structured, semi-structured, and unstructured data into in-depth business insights efficiently and effectively. It also provides a head start in turning messy data into meaningful outcomes for an organization by including chapters like Data Science basics, Big Data, Python, R, SQL, data visualization, real-time analytics, IoT, and many more. This book ensures to enhance the Data Science skills to kick-start new career or projects with sufficient knowledge of modern technologies, programming languages, and mathematical methods.
