Subject

Programming

Tidying, Manipulating, and Plotting Data with the tidyverse

From Machine Learning with R, tidyverse, and mlr by Hefin Rhys

This article covers:

• What the tidyverse is
• What’s meant by tidy data
• How to install and load the tidyverse
• How to use the tibble, dplyr, ggplot2 and tidyr packages of the tidyverse

Embrace JavaScript!

From The Joy of JavaScript by Luis Atencio


slideshare-embrace-javascript

When Functional Programming Meets C++

Six Questions for Ivan Čukić, author of Functional Programming in C++ 

Ivan Čukić has been coding since 1998, and is now a core developer in C++ at KDE. He teaches modern C++ and functional programming at the Faculty of Mathematics at the University of Belgrade.

By Frances Lefkowitz

Free eBook: Exploring Haskell

ExploringHaskell

Chapters selected by Marcello Seri

Tracking an Evolving Language

Six Questions for Jon Skeet, author of C# In Depth, 4th Edition

Jon Skeet (@jonskeet) is a senior software engineer at Google, London and a recognized authority on Java and C#. He is the top contributor to Stack Overflow.

Learn Coding Best Practices in Python

From Code Like a Pro by Dane Hillard

slideshare-learn-coding-best-practices-in-python

The Towers of Hanoi: six questions with David Kopec

Six Questions for David Kopec, author of Classic Computer Science Problems in Python

David Kopec is Assistant Professor in computer science at Vermont’s Champlain College and author of two books in the Classic Problems series. If you want more, find @davekopec on Twitter.

Rust’s Borrowing by Example

From Rust in Action by Tim Mcnamara

Our strategy for this article is to use an example that compiles, then make a minor change that triggers an error which appears to emerge without any adjustment to the program’s flow. Working through the fixes to those issues should make the concepts more complete.

Basic Text Processing in Functional Style

From Haskell in Depth by Vitaly Bragilevsky

This article explores text processing in the functional programming style.

Constraint-Satisfaction Problems in Python

From Classic Computer Science Problems in Python by David Kopec

A large number of problems which computational tools solve can be broadly categorized as constraint-satisfaction problems (CSPs). CSPs are composed of variables with possible values which fall into ranges known as domains. Constraints between the variables must be satisfied in order for constraint-satisfaction problems to be solved. Those three core concepts—variables, domains, and constraints—are simple to understand, and their generality underlies the wide applicabilit

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