data-science-projects-and-resources / data-science-ebooks

Data Science E-books, Interview Resources and Cheat-sheets

Data Science Resources

This list contains free learning resources and Interview resources for Data Science, Machine Learning, Deep Learning, Data Analysis, NLP, Big Data and other Data Science related tools.

Table of Contents :

No	Section	Link
1	E-books	Click Here
2	Interview Resources	Click Here
3	Cheat-Sheets	Click Here

E-books

No	Section	Link
1	Data Analysis	Click Here
2	Data Science	Click Here
3	Deep Learning	Click Here
4	Machine Learning	Click Here
5	Miscellaneous	Click Here
6	Natural Language Processing	Click Here
7	Python	Click Here
8	R	Click Here
9	SAS	Click Here
10	SQL	Click Here
11	Statistics	Click Here
12	Text Analysis	Click Here
13	Transfer Learning	Click Here

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Data Analysis

No	Title	Author	Link
1	Python for Data Analysis	Wes McKinney	pdf
2	Think Stats	Allen B. Downey	pdf

Data Science

No	Title	Author	Link
1	Data Science From Scratch	Joel Grus	pdf
2	Python Data Science Handbook	Jake VanderPlas	pdf
3	Think Like A Data Scientist	Brian Godsey	pdf

Deep Learning

No	Title	Author	Link
1	Advanced Applied Deep Learning	Umberto Michelucci	pdf
2	Deep Learning	Ian Goodfellow, Yoshua Bengio, Aaron Courville	pdf

Machine Learning

No	Title	Author	Link
1	Hands on Machine Learning with Scikit-Learn, Keras & Tensorflow	Aurélien Géron	pdf
2	Introduction to Machine Learning	Ethem Alpaydın	pdf
3	Machine Learning	Tom M. Mitchell	pdf
4	Machine Learning : A Probabilistic Perspective	Kevin P. Murphy	pdf
5	Machine Learning Models and Algorithms for Big Data Classification	Shan Sutharan	pdf
6	Prediction Machines	Ajay Agarwal, Joshua Gans, Avi Goldfarb	pdf
7	Python Machine Learning	Sebastian Raschka	pdf
8	Understanding Machine Learning: From Theory to Algorithms	Shai-Shalev Schwatrz, Shai Ben-David	pdf
9	Handbook of Research on Emerging Trends and Applications of Machine Learning	Arun Solanki, Sandeep Kumar, Anand Nayyar	pdf
10	Practical Machine Learning with AWS	Himanshu Singh	pdf

Miscellaneous

No	Title	Author	Link
1	Beginning Regular Expressions	Andrew Watt	pdf
2	Ethics of AI	Nick Bostrom, Eliezer Yudkowsky	pdf
3	Hadoop : The definitive guide	Tom White	pdf
4	Introduction to AI	Wolfgang Ertel	pdf
5	LearningAlgorithms	George T. Heineman	pdf

Natural Language Processing

No	Title	Author	Link
1	Natural Language Processing with Python	Steven Bird, Ewan Klein & Edward Loper	pdf
2	Real-World Natural Language Processing	Masato Hagiwara	pdf

Python

No	Title	Author	Link
1	Python Notes for Professionals	-	pdf

R

No	Title	Author	Link
1	A Beginner's guide to R	Alain F. Zuur, Elena N. Leno, Erik H.W.G Meesters	pdf
2	R Programming for Data Science	Roger D. Peng	pdf

SAS

No	Title	Author	Link
1	Introduction to Regular Expression in SAS	K. Mathew Windham	pdf

SQL

No	Title	Author	Link
1	SQL for dummies	Allen G. Taylor	pdf

Statistics

No	Title	Author	Link
1	An Introduction to Statistical Learning with Applications in R	Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani	pdf
2	Head First Statistics	Dawn Griffiths	pdf
3	Practical Statistics for Data Scientists	Peter Bruce, Andrew Bruce	pdf
4	Statistical Inference in Data Science	Brian Caffo	pdf
5	Statistics Unplugged	Sally Caldwell	pdf
6	Statistics for Buissness and Economics	Anderson Sweeney Williams	pdf
7	Statistics in Python	Gael Varoquaux	pdf
8	The Cartoon Guide to Statistics	Larry Gonick, Woollcott Smith	pdf
9	The Elements of Statistical Learning	Trevor Hastie, Robert Tibshirani, Jerome Friedman	pdf

Text Analysis

No	Title	Author	Link
1	Applied Text Analysis with Python	Benjamin Bengfort, Rebecca Bilbro & Tony Ojeda	pdf

Transfer Learning

No	Title	Author	Link
1	Transfer Learning	Qiang Yang, Yu Zhang, Wenyan Dai, Sinno Jialin Pan	pdf

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Interview Resources

No	Title	Link
1	Big-data Analysis Interview Questions	pdf
1	Data Science Interview Questions	pdf
2	ML Interview Questions	pdf
3	ML Interview Questions Part 2	pdf
4	Python Interview Questions	pdf
5	Ultimate Guide to Data Science Interviews	pdf

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Cheat Sheets

No	Title	Link
1	CLI	pdf
2	Data Exploration in Python	pdf
3	git	pdf
4	git for subversion	pdf
5	Jupter Notebook	pdf
6	Kubernetes	pdf
7	Keras	pdf
8	Matplotlib	pdf
9	Pandas	pdf
10	Pandas DataFrames	pdf
11	Python3	pdf
12	Python basic	pdf
13	Python intermediate	pdf
14	Python for Data Science	pdf
15	Scikit-Learn	pdf
16	Seaborn	pdf
17	SQL	pdf
18	VS-code	pdf
19	Workflow of version control	pdf

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How to Contribute

If you find a nice ebook or interview resource or cheatsheet related to Data Science, feel free to fork PythonBooks and add it in a few steps :

• Fork the repository.
• Add your ebook.
• Add Book title, Author and link to the book inside the specified category in alphabetical order in the ReadMe.
• Submit a pull request.

That's it.

Alternatively, you can send us an email with book infos : [email protected]

Thanks per advance 💙!

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