How to Level Up Your Data Visualization Skills in 2024

AI buzzwords come and go, new machine learning trends explode and fizzle out, but some things stay consistent—and one of those is the storytelling power of a good data visualization.

Presenting data-backed insights through visual media remains a core skill for data professionals, and we love exploring the nitty-gritty details that make charts, plots, and infographics click. We find it equally valuable to dig into fundamental building blocks as it is to keep up with recent tools and novel approaches. This week, we present some excellent articles covering the entire spectrum between these poles: if you were planning to deepen and expand your visualization skills in 2024, you’re in the right place. Let’s kick things off.

• Visualisation 101: Choosing the Best Visualisation Type. A strong foundation in design strategy is key when it comes to creating effective visuals. Mariya Mansurova ’s primer—on the different use cases for data viz, and how to adapt your approach depending on your end goals—is as solid a resource as you’ll find if you’re taking your first steps in this domain.
• Declarative vs. Imperative Plotting. The path from a gorgeous vision in your head to the final product on your screen is filled with numerous intermediary steps, many (if not most) of which come in the form of code. Lee Vaughan ’s explainer on how plotting works in Python is an essential read for anyone who’d like to understand how visualization tools look under the hood—and how to choose the right one accordingly.

• Visualizing Everest Expeditions. For a generous dose of visualization inspiration, don’t miss Karla H. ’s step-by-step tutorial, which guides us through the entire process of creating a sleek, multi-layered, and highly effective infographic. The topic at hand might be mountaineering, but the principles Karla outlines are valuable regardless of the project you’re working on.
• Visualizing Routes on Interactive Maps with Python: Part 1. Apps like Google Maps have been ubiquitous for such a long time that we almost take them for granted; Carlos Jimenez Uribe-echeverría ’s hands-on guide underlines the complexity behind creating maps, but also shows that visualizing rich geospatial data is within reach if you use the right tools in a streamlined manner.

Our authors have kicked off the new year with an exciting burst of activity. It’s always hard to choose, but here are a few more outstanding articles we wouldn’t want you to miss.

• Catch up with all the recent developments in graph and geometric ML and learn about where the field might be headed in 2024— Michael Galkin and Michael Bronstein put together a massive, two part “State of the Art Digest” for you to dig into. Part 1 focuses on theory and architectures, while Part 2 zooms in on practical applications.
• For a comprehensive guide to batch processing, look no further than Xiaoxu Gao ’s latest post, which covers the topic from both technical and business perspectives.
• How will generative AI shape the work of software engineering teams? Omer Ansari ’s deep dive unpacks the stakes and offers insights to help leaders prepare for the (near) future.
• In her latest beginner-friendly article, Gurjinder Kaur introduces the AdaBoost algorithm and offers a clear explanation of its inner workings—as well as a full Python implementation.
• Bringing theory and practice together, Shuai Guo presents a detailed guide on ordinary differential equations and how they can be leveraged to model dynamical systems.
• If you’ve been tinkering with retrieval-augmented generation (RAG) and would like to explore new ways to optimize your workflow, add Iulia Brezeanu ’s tutorial on advanced query transformations to your must-reads list.

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Until the next Variable,

TDS Team