The 10 Must-Read Free eBooks for Data Science

by Vivek Kumar

November 10, 2020

Data science is recognized as the sexiest job of the 21st century. As organizations seek to sustain themselves through data-driven insights, the demand for data science professionals is ever-increasing. Numerous reports show that the demand for data scientists is increasing year on year and continues to rise sharply. So, to leverage the insights in their data to personalize experiences at scale, companies need to acquire the best data professionals. On the other hand, before entering a career in data science, candidates must acquire knowledge about the world of data and analytics.

Here is an overview of the top 10 free ebooks that you must read for learning data science.

Top Programming Languages ​​for a Data Scientist

Whether a candidate wants to develop a mobile application, get a certification in programming knowledge, or learn new skills, they must choose the right programming language to learn. This eBook will provide ten popular and important programming languages ​​that are in demand today. For each, learners will discover a little about the language and the complexity of learning it. They will also learn how it is used and which language to start with.

Get your copy here.

Python Data Science Handbook: Essential tools for working with data

Python is a top-notch tool for many researchers, mostly credited with its libraries for storing, manipulating, and getting insights from data. Written by Jake VanderPlas, this manual will allow the learner to learn how to use IPython and Jupyter; NumPy, includes the ndarray for efficient storage and manipulation of dense arrays of data in Python; Pandas which includes the DataFrame for efficient storage and manipulation of labeled/column data in Python; Matplotlib; and Scikit-Learn.

Get your copy here.

Python 101

This eBook will offer you to learn to program with Python 3 from beginning to end. Python 101 starts with the fundamentals of Python and then builds on what learners have learned from there. Written by Michael Driscoll, the book will be divided into five parts. In the first part, learners will learn all the basics of Python. The second part will be an organized tour of the Python standard library. The third part is all mid-level material, covering lamda, decorators, properties, debugging, testing, and profiling. The fourth part explains how to install third-party libraries from Python Package Index and other locations. The last section of the book will explain how to share your code with your friends and the world.

Get your copy here.

Aspiring to machine learning

Machine Learning Yearning is a free ebook by Andrew Ng. It teaches learners how to structure ML projects. This ebook is also focused on teaching ML algorithms and how to make them work. After reading this book, learners will be able to prioritize the most promising directions for an AI project; diagnose errors in an ML system; build ML in complex contexts, such as incompatible training/test sets; set up an ML project to compare and/or surpass human performance; and knowing when and how to apply end-to-end learning, transfer learning, and multitasking learning.

Get your copy here.

The Secret to Unlocking Tableau’s Hidden Potential

Because Tableau makes analytics simple and accessible to everyone, it is currently considered the market leader in self-service BI with high execution. This free ebook will be useful to those who use Tableau, but want to get the most out of this powerful data visualization tool. This will turn a learner into a seasoned Tableau user. With this book, readers will discover the secrets of powerful features they never knew existed, for in-depth data analysis and insights.

Get your copy here.

The Data Science Handbook

This ebook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice. This is not a technical guide to data science. Rather, it will provide data scientists with common career questions such as What separates the job of a data scientist from that of a statistician and software engineer? How can they work together? What should you look for when evaluating data science roles in business? What does it take to build an effective data science team? What mindsets, techniques, and skills set a great data scientist apart from a good one? What future for data science? and more.

Get your copy here.

Data Scientists: The Numbers Game Deciphered

Since the demand for data scientists continues to grow, the best data scientist will be the most knowledgeable. This ebook will educate data science enthusiasts on relevant aspects of this hot field and give them a competitive edge when applying for this coveted position. The ebook will provide the basics of big data and data science; history and developments of data science; the prerequisites for becoming a data scientist; preferred educational qualifications; miscellaneous Soft skills; study plan and useful resources.

Get your copy here.

Command line data science

This how-to guide shows how command-line flexibility can help a learner become a more efficient and productive data scientist. In this book, users will learn how to merge small but powerful command-line tools to quickly get, clean, explore, and model their data. This ebook obtains data from websites, APIs, databases and spreadsheets that help to explore data, calculate descriptive statistics, create visualizations and manage the workflow of the data science.

Get your copy here.

Practical statistics for Data Scientist

This practical guide aims to explain how to apply various statistical methods to data science, teaches learners how to avoid their misuse, and advises them on what is imperative and what is not. This book will enable learners to learn: why exploratory data analysis is a key preliminary step in data science? How can random sampling reduce bias and produce a higher quality data set, even with big data? How do the principles of experimental design provide definitive answers to the questions? How to use regression to estimate results and detect anomalies? Key classification techniques to predict which categories a recording belongs to and much more.

Get your copy here.

Exploratory data analysis with R

This ebook is a key part of the data science process as it allows learners to refine their questions and refine their modeling strategies. It significantly teaches how to use R to effectively visualize and explore complex data sets. This book is based on the industry-leading Johns Hopkins Data Science specialization.

Get your copy here.

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