You, like many other data science enthusiasts, write code on a near-daily basis. But are you programming effectively? Did you choose the right programming languages for your projects? Did you use an IDE or notebook to boost development efficiency? Did you leverage any libraries or frameworks to optimize code performance?
Effective programming is not only about writing high-performance code, but also leveraging available tools to develop projects more efficiently. Learning to code more effectively is an investment that will pay dividends in your career. The following is a collection of our best articles on effective programming techniques and tips.
Xinran Waibel – Editorial Associate
Great Developers Never Stop Learning
By Semi Koen – 10 min read
7 ways I.T. Professionals can foster a Continuous Learning mindset
Top 7 Modern programming languages to learn now
By Md Kamaruzzaman – 17 min read
How Rust, Go, Kotlin, TypeScript, Swift, Dart, Julia can boost your career and improve your software development skills
Rock the Command Line
By Jeff Hale – 10 min read
21 Bash commands to save you time
Getting started with Git and GitHub: the complete beginner’s guide
By Anne Bonner – 18 min read
Git and GitHub basics for the curious and completely confused (plus the easiest way to contribute to your first open source project ever!)
Bringing the best out of Jupyter Notebooks for Data Science
By Parul Pandey – 9 min read
Enhance Jupyter Notebook’s productivity with these Tips & Tricks.
The case against the jupyter notebook
By Jeremie Harris – 3 min read 🎧
Joel Grus on the TDS podcast
The Most Undervalued Standard Python Library
By Tyler Folkman – 3 min read
Collections for data scientists
Turn Python Scripts into Beautiful ML Tools
By Adrien Treuille – 7 min read
Introducing Streamlit, an app framework built for ML engineers
From ‘R vs Python’ to ‘R and Python’
By Parul Pandey – 7 min read
Leveraging the best of both ‘Python and R’ in a single project.
A Complete Machine Learning Project Walk-Through in Python
By Will Koehrsen – 15 min read
Putting the machine learning pieces together
SQL at Scale with Apache Spark SQL and DataFrames – Concepts, Architecture and Examples
By Dipanjan (DJ) Sarkar – 21 min read Wrangle, aggregate, filter data at scale using your friendly SQL with a twist!
We also thank all the great new writers who joined us recently, JP Hwang, Ian Rowan, Ivan Ilin, Owen Flanagan, Pavel Horbonos (Midvel Corp), Mykyta Solonko, Alain Tanguy, Lindo St. Angel, Andy Chen, Khuyen Tran, Courtney Whalen, Brittany Bowers, Kate Marie Lewis, Tom Waterman, JR Kreiger, Jay Budzik, Ashton Sidhu, Xeno Acharya, Raz Haleva, and many others. We invite you to take a look at their profiles and check out their work.
