Top 12 Must Read Books for Data Scientists on Python

If you are looking to learn python than what could be a better source than taking help from books written by professionals? In order to help you with your search we have created a list of best book for python data science, so that you don’t have to wait and based on your requirements you can start your learning process with best books to learn python:

Top Must Read Books for Data Scientists on Python

S.No. Books for Data Scientists on Python Author Name
1. Mastering Python for Data Science Samir Madhavan
2. Python for Data Analysis W McKinney
3. Introduction to Device Studying with Python Andreas Muller and Sarah Guido
4. Python Device Learning Sebastian Raschka
5. Advanced Device Studying with Python John Hearty
6. Programming Combined Intelligence Toby Segaran
7. Think Stats: Probability and Statistics for Programmers Allen B. Downey
8. Probabilistic Development & Bayesian Methods for Hackers Cam Davidson-Pilon
9. Understanding Machine Learning: From Theory to Algorithms Shai Shalev-Shwartz and Shai Ben-David
10. Thіnk Stаtѕ2 Allen Dоwnеу

1.) Mastering Python for Data Science

This information is published by Samir Madhavan. This book begins with an introduction to data components in Numpy & Pandas and provides useful information of publishing data from various resources into these components. You will figure out how to perform linear algebra in Python and make analysis by using inferential statistics. Later, the book takes onto the innovative ideas like developing a recommendation engine, high-end visualization using Python, ensemble modeling etc. If you are a complete newbie and are looking for a book to learn python, then this book is one of the best book for python beginners.

2.) Python for Data Analysis

Want to begin with data analysis with Python? Get your hands on this data analysis information by W McKinney, the main writer of Pandas library. There isn’t any online course as extensive as this book. This book includes each and every aspect of data analysis from manipulating, processing, cleaning, visualization and crunching data in Python. If you are new to data science python, it’s a must read for you. It’s power-packed with case studies from various domains. This book is ranked amongst our best books to learn python due to the extensive knowledge it provides to python learners.

3.) Introduction to Device Studying with Python

This book is published by Andreas Muller and Sarah Guido. It’s intended to help newbies get started with machine learning and is recommended as one of the best book for python beginners. It teaches to build ML designs in python scikit-learn from scratch. It assumes no prior knowledge; hence it’s best suitable to individuals with no idea on python or ML information. In addition, it also includes innovative means of design assessment and parameter tuning, methods of working with text-data, written text -specific handling methods etc.

4.) Python Device Learning

This book is published by Sebastian Raschka. It’s one of the best book’s I’ve found on ML in Python. The writer describes every crucial detail we need to know about machine learning. He takes a stepwise strategy in describing the ideas reinforced by various illustrations. This information cover subjects such as neural networks, clustering, regression, classification, ensemble etc. It’s the best book on python if you want to Master ML on python.

5.) Building Device Studying Systems with Python

This book is published by Willi Richert, Luis Pedro Coelho. In this book the writers have selected a direction of, starting with basic concepts, describing ideas through tasks and finishing on a higher note. Therefore, I’d recommend this secrets and techniques for newbie python machine learning lovers. It includes subjects like image processing, recommendation engine, sentiment analysis etc. It’s clear and understandable and fast to apply written text information. The book is recommended as one of the best book for python data science for beginners because it takes learners through step by step learning of python and is easy to understand.

6.) Advanced Device Studying with Python

This book is published by John Hearty. It’s a definite read for every machine learning lovers. It allows you to increase above basic concepts of ML methods and jump into unsupervised methods, deep belief networks, Auto encoders, feature engineering methods, ensembles etc. It’s definitely a book you would want to read to improve your positions in machine learning contests. The writer sets equivalent focus on theoretical as well realistic factors of machine learning. If you are not a newbie and are looking for a best book on python data science for gaining an in-depth knowledge of ML methods and machine learning then advanced device studying with python will definitely enhance your knowledge the way you want it to.

7.) Programming Combined Intelligence

This book is published by Toby Segaran. With an exciting headline, this book was created introducing you to several ML methods such as SVM, trees, clustering, optimization etc using exciting illustrations and used cases. This is information is most effective for individuals new to ML in python. Python, known for its amazing ML collections & support should allow you to understand these ideas quicker. Also, the sections consist of exercises for practice to help you create better knowing.

8.) Think Stats: Probability and Statistics for Programmers

Think Stats is an introduction to Probability and Statistics for Python programmers written by Allen B. Downey. 
Think Stats focuses on simple methods you can use to discover actual data sets and answer exciting questions. The information provides a research study using data from the Nationwide Institutions of Health. Visitors are motivated to work on a job with actual datasets. This is one of the best books for python because it helps learners, learn through practical work i.e. by working on actual data sets.

9.) Probabilistic Development & Bayesian Methods for Hackers

An introduction to Bayesian methods and probabilistic programming from a computation/understanding-first, mathematics-second perspective. This book is authored by Cam Davidson-Pilon.

The Bayesian method is the natural way of inference, yet it is invisible from readers behind sections of slowly, statistical research. The common written text on Bayesian inference includes two to three sections on probability concept, then goes into what Bayesian inference is. Unfortunately, due to statistical intractability of most Bayesian designs, the audience is only shown simple, synthetic illustrations. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the writer’s own before viewpoint.

10.) Understanding Machine Learning: From Theory to Algorithms

Shai Shalev-Shwartz and Shai Ben-David authored an amazing book ‘Understanding Machine Learning: From Theory to Algorithms’.

Machine learning is one of the quickest growing areas of information technology, with far-reaching programs. The aim of this text is introducing machine learning, and the algorithmic paradigms it offers, in a principled way. The information provides a theoretical account of basic concepts actual machine learning and the statistical derivations that convert these concepts into realistic methods.

Following an exhibition of basic concepts, the novel includes a wide range of main subjects unaddressed by past books. Included in this are a conversation of the computational complexness of learning and the ideas of convexity and stability; important algorithmic paradigms such as stochastic slope nice, sensory systems, and organized outcome learning; and growing theoretical ideas such as the PAC-Bayes strategy and compression-based range.

11.) Buіldіng Mасhіnе Learning Sуѕtеmѕ with Pуthоn

Thіѕ is one of mу fаvоrіtе bооk on mасhіnе lеаrnіng and Pуthоn. You hаvе tо know thаt this bооk іѕ not іntеndеd fоr bеgіnnеrѕ, уоu ѕhоuld have a gооd grаѕр оf Python and mасhіnе learning tо understand the соdе аnd mасhіnе lеаrnіng techniques uѕеd in thіѕ bооk. Thе bооk іѕ ѕоmе thіng mоrе than a ѕummаrу оf mасhіnе lеаrnіng algorithms, bесаuѕе іt also shows you hоw to сhооѕе thе rіght аlgоrіthіm fоr a рrоblеm аt hand. Thе bооk uѕеѕ ѕсіkіt learn tо іmрlеmеnt thеѕе mасhіnе learning algorithims, уоu ѕhоuld dеfіnіtеlу knоw scikit-learn tо run machine lеаrnіng аlgоrіthmѕ іn Python.

If уоu wаnt to еxрlоrе mоrе аbоut Scikit-learn, there аrе two оthеr bооkѕ Mаѕtеrіng mасhіnе learning with ѕсіkіt lеаrn аnd Lеаrnіng Scikit-learn уоu should look into.

12.) Mіnіng thе ѕосіаl wеb

Thіѕ іѕ more thаn a “book” – іt is a соurѕе, аnd a vеrу wеll thоught thrоugh, wеll supported соurѕе аt thаt. Thе book introduces the APIѕ рrоvіdеd bу ѕоmе оf the lаrgеr ѕосіаl platforms, and аlѕо gіvеѕ a good intro tо dаtа munging and аnаlуѕіѕ оf dаtа. The сlеаr аnd еаѕу to fоllоw examples аrе furthеr еnhаnсеd through thе ассоmраnуіng vіrtuаl machine оf the bооk, аllоwіng уоu tо еѕсаре thе hеаdасhе оf installing, соnfіgurіng, аnd ѕеlесtіng thе right vеrѕіоn оf all the ѕuрроrtіng ѕоftwаrе аnd lіbrаrіеѕ. Yоu саn аlѕо check оut authors website mіnіng thе social web, where hе wrіtеѕ ѕоmе really good аrtісlеѕ оn ѕосіаl mеdіа mining.


The above list of books to learn python programming for beginners is also meant for intermediates and experts too, so based on your requirements choose one of the best books on python from the above list and start learning.

Here is a list of Machine Learning Books that you can also take into consideration.

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Published by
Deepesh Sharma