The Data Science industry is seeing a rapid increase in its application and offers a very promising future. To be able to enter this domain, one must be equipped with the various concepts, techniques and have sufficient experience with a wide range of tools available for the job. There are hundreds of resources available, including online courses, websites, videos, and books, to get the hang of the subject, as it may seem daunting at first. In this article, we are listing 10 excellent free data science books which cover the wide variety of topics under Data Science.
This book gives an overview of Data Science. Data Science is a very large umbrella term and this book is good for anyone trying to get their feet wet in the field for the first time. Read it to understand what Data Science is, what are some general tasks and algorithms, and some general tips and tricks.
Foundations of Data Science is a treatise on selected fields that form the basis of Data Science like Linear Algebra, LDA, Markov Chains, Machine Learning basics, and statistics. The ideal readers for the book are the beginner data scientists wanting to make their mathematical and theoretical grasp on the field better.
Based on Stanford courses CS246 and CS35A, the book helps users learn topics to do Data Mining on large datasets. Often a very common problem a data scientist has to solve is to perform simple numerical tasks (which you can do by writing small pieces of programs) on a very large dataset. MMDS works exactly towards that. Added to that, you have topics like Dimensionality Reduction and Recommendation Systems which help you learn about the application of linear algebra and metric distances in the real world. An absolute must-read for all Data Scientists.
Python Data Science Handbook teaches the application of various Data Science concepts in Python. Probably the best book to learn Data Science in Python ( only equivalent is Wes McKinney’s mouse book), this book is also free to read on Github. So you can learn without spending any money.
5. Think Stats
Think Stats teaches readers the basics of statistics, that is, readers will apply statistical concepts and distributions on real-world datasets and try to learn more about data using mathematical characteristics. Probably one of the best books to get started with if you want to learn statistics with Python.
6. Think Bayes
Bayesian Statistics works somewhat differently from normal statistics. The concepts of uncertainty and fitting distributions to real-world datasets make Bayesian methods more fitting to learn about real-world datasets. Prof. Downey’s extremely cool “learn by programming it in Python” style makes the book a treat for those getting started with Bayesian Methods.
Convex Optimization is what many Machine Learning (and almost all Deep Learning algorithms) algorithms use in the background to arrive at the optimal set of parameters.
Metaheuristics are quick learning probabilistic ways to do tasks that would require you to otherwise write programs to search using Brute Force. For maybe small data, Brute Force approaches take lesser effort to implement, but they exhaust very fast with the amount of data added. This book is probably the best introduction to metaheuristic methods like Genetic Algorithms, Hill Climbing, Co-Evolution, and (basic) Reinforcement Learning.
Applied Data Science by Langmore and Krasner is a book that takes a very practical approach to teach Data Science. From using Git, teaching Basic Python, the book goes on to build fundamentals of various algorithms that are used frequently in the field of Data Science.
A book by Efron and legendary Hastie thinking how Statistical Inference (both frequentist and bayesian) should be done in modern times using the computational power available nowadays rather than the pen-and-paper approach most other books take. This is a must-read by anyone (beginner or experienced) who intends to use Statistics in real life.
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