From SQL to Julia: Data Science’s Other Programming Languages
Python might be the bread-and-butter coding language for data scientists and ML practitioners, but the benefits of becoming a programming polyglot are as clear as ever. The projects that data teams work on cross platforms and architectures, and being able to switch to less common—but sometimes more efficient—languages can be a powerful advantage.
For our highlights this week, we chose recent articles that cover a lot of ground: they focus on a wide range of languages, projects, and workflows. These posts invite you to roll up your proverbial sleeves and start tinkering with some code—and they might just inspire you to expand your knowledge even further.
- Database-querying language SQL is hardly an unknown entity; it’s been around for decades, and remains a common gateway into coding for many data folks. Iffat Malik Gore’s overview of window functions is a great introduction (or refresher, as the case may be) to a core element of SQL’s vocabulary.
- If your work is statistics-heavy, it’s likely you’ve already encountered R in the wild. The language’s use cases might be more versatile than you think, though—a point that Jenna Eagleson makes clear with a helpful hands-on tutorial on using R for addressing common challenges in people analytics.
- Python libraries have been so dominant in the realm of data visualization that it’s easy to forget you can create sleek charts in other languages, too. Mahmoud Harmouch’s deep dive into Rust’s visualization options focuses on the Plotters library and its powerful features.
- Getting your data ready for an analysis or modeling project is a crucial step regardless of the language you’re working in. Emma Boudreau walks us through the process of data filtering in Julia—a language that might still be relatively niche, but whose popularity among data scientists has exploded in recent years.
- Version control is an essential component in any software development process, and Git, while not a programming language itself, is the go-to system for working and collaborating on code across the many languages it supports. If you’re still at the early stages of your coding journey, reading Khuyen Tran’s comprehensive guide to using Git as a data scientist is a sensible next step to take.
To broaden your horizons in other directions, we’re thrilled to share a few more of our recent standouts:
- How did the startup ecosystem evolve in response to AI’s rapidly growing footprint? Viggy Balagopalakrishnan offers sharp insights based on the latest Y Combinator cohort.
- Lambert T Leong, PhD presents recent research at the intersection of healthcare and deep learning, aimed at predicting all-cause mortality based on body-composition imaging.
- If you’ve heard about Code Interpreter, the ChatGPT plugin, and wanted to see what the buzz was all about, Natassha Selvaraj’s project walkthrough is the place to go.
- For a clear, thorough, and engaging explanation of the law of large numbers and how it works, don’t miss Sachin Date’s new deep dive.
- What would be the environmental impact of billions of people using generative-AI tools every day? Kasper Groes Albin Ludvigsen tackles a thorny, timely, and crucial question.
Thank you for supporting our authors! If you enjoy the articles you read on TDS, consider becoming a Medium member — it unlocks our entire archive (and every other post on Medium, too).
We hope many of you are also planning to attend Medium Day on August 12 to celebrate the community and the stories that make it special—registration (which is free) is now open.
Until the next Variable,
TDS Editors
From SQL to Julia: Data Science’s Other Programming Languages was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
Python might be the bread-and-butter coding language for data scientists and ML practitioners, but the benefits of becoming a programming polyglot are as clear as ever. The projects that data teams work on cross platforms and architectures, and being able to switch to less common—but sometimes more efficient—languages can be a powerful advantage.
For our highlights this week, we chose recent articles that cover a lot of ground: they focus on a wide range of languages, projects, and workflows. These posts invite you to roll up your proverbial sleeves and start tinkering with some code—and they might just inspire you to expand your knowledge even further.
- Database-querying language SQL is hardly an unknown entity; it’s been around for decades, and remains a common gateway into coding for many data folks. Iffat Malik Gore’s overview of window functions is a great introduction (or refresher, as the case may be) to a core element of SQL’s vocabulary.
- If your work is statistics-heavy, it’s likely you’ve already encountered R in the wild. The language’s use cases might be more versatile than you think, though—a point that Jenna Eagleson makes clear with a helpful hands-on tutorial on using R for addressing common challenges in people analytics.
- Python libraries have been so dominant in the realm of data visualization that it’s easy to forget you can create sleek charts in other languages, too. Mahmoud Harmouch’s deep dive into Rust’s visualization options focuses on the Plotters library and its powerful features.
- Getting your data ready for an analysis or modeling project is a crucial step regardless of the language you’re working in. Emma Boudreau walks us through the process of data filtering in Julia—a language that might still be relatively niche, but whose popularity among data scientists has exploded in recent years.
- Version control is an essential component in any software development process, and Git, while not a programming language itself, is the go-to system for working and collaborating on code across the many languages it supports. If you’re still at the early stages of your coding journey, reading Khuyen Tran’s comprehensive guide to using Git as a data scientist is a sensible next step to take.
To broaden your horizons in other directions, we’re thrilled to share a few more of our recent standouts:
- How did the startup ecosystem evolve in response to AI’s rapidly growing footprint? Viggy Balagopalakrishnan offers sharp insights based on the latest Y Combinator cohort.
- Lambert T Leong, PhD presents recent research at the intersection of healthcare and deep learning, aimed at predicting all-cause mortality based on body-composition imaging.
- If you’ve heard about Code Interpreter, the ChatGPT plugin, and wanted to see what the buzz was all about, Natassha Selvaraj’s project walkthrough is the place to go.
- For a clear, thorough, and engaging explanation of the law of large numbers and how it works, don’t miss Sachin Date’s new deep dive.
- What would be the environmental impact of billions of people using generative-AI tools every day? Kasper Groes Albin Ludvigsen tackles a thorny, timely, and crucial question.
Thank you for supporting our authors! If you enjoy the articles you read on TDS, consider becoming a Medium member — it unlocks our entire archive (and every other post on Medium, too).
We hope many of you are also planning to attend Medium Day on August 12 to celebrate the community and the stories that make it special—registration (which is free) is now open.
Until the next Variable,
TDS Editors
From SQL to Julia: Data Science’s Other Programming Languages was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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