Collection of a great programming reference books that might help you in your project development and get additional knowledge from the author. Support author by buying hardcopy to the nearest book store in your place or order books in their respective publisher websites.

Author: Peter Bruce

Publisher: O'Reilly Media Pages: 318

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training.

Author: Irv Englander

Publisher: Wiley Pages: 702

This accessible introduction provides the basic principles of computer system architecture and organization in the context of the current technological landscape.

Author: Tony Gaddis

Publisher: Pearson Pages: 960

Students who are new to programming will appreciate the clear, down-to-earth explanations and the detailed walk-throughs that are provided by the hands-on tutorials.

Author: Wes McKinney

Publisher: O'Reilly Media Pages: 550

Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.

Author: Joel Grus

Publisher: O'Reilly Media Pages: 406

Updated for Python 3.6, this second edition of Data Science from Scratch shows you how these tools and algorithms work by implementing them from scratch.

Author: Joel Grus

Publisher: O'Reilly Media Pages: 330

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science.

Author: William Sullivan

Publisher: Independently Pages: 267

C# is becoming more and more popular with each passing day simply because it's an easy to learn language, robust, comprehensive, practical, and a general purpose language.

Author: Joel Murach; Mary Delamater

Publisher: Mike Murach & Associates Pages: 770

This book gives you 50+ realistic program examples to study, as well as practice exercises for hands-on experience.

Author: Stanley B. Lippman; Josée Lajoie; Barbara E. Moo

Publisher: Addison-Wesley Professional Pages: 976

Tutorial for those new to C++, an authoritative discussion of core C++ concepts and techniques, and a valuable resource for experienced programmers.

Author: Dusty Phillips

Publisher: Packt Pages: 460

Many modern programming languages utilize the powerful concepts behind object-oriented programming and Python is no exception.

Author: Aristides S. Bouras; Loukia V. Ainarozidou

Publisher: Independently Pages: 626

This book is for anyone who wants to learn algorithmic thinking and computer programming and knows absolutely nothing about them.

Author: Lee Vaughan

Publisher: No Starch Press Pages: 424

Impractical Python Projects is a collection of fun and educational projects designed to entertain programmers while enhancing their Python skills.

Author: Al Sweigart

Publisher: No Starch Press Pages: 504

You’ll learn how to use Python to write programs that do in minutes what would take you hours to do by hand—no prior programming experience required.

Author: Ozen Ozkaya; Giray Yillikci

Publisher: Packt Pages: 190

Build up your skill set of computer vision system design using OpenCV by learning fundamentals, camera selection, data acquisition, filtering, processing.

Author: Stephen Cleary

Publisher: O'Reilly Media Pages: 208

If you're one of the many developers uncertain about concurrent and multithreaded development, this practical cookbook will change your mind.

Author: Jeffrey Jackovich; Ruze Richards

Publisher: Packt Pages: 254

Machine Learning with AWS is the right place to start if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services.

Author: Matthew Rever

Publisher: Packt Pages: 182

You'll learn state-of-the-art techniques for classifying images, finding and identifying human postures, and detecting faces within videos.

Author: Andreas C. Müller, Sarah Guido

Publisher: O'Reilly Media Pages: 400

You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library.

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