Data Science is a fast-moving field, with new tools constantly emerging, workflows evolving, and career paths changing rapidly—sometimes within the span of mere weeks.
Our most-read and -discussed articles reflect these trends, with readers flocking to excellent articles by data and ML professional who have insights to share based on their on-the-ground experience. To make sure you don’t miss our best articles, we’re thrilled to share some of our standout stories from the past month. They cover a lot of ground—from coding to LLMs to data storytelling—but share a focus on actionable, firsthand advice. Enjoy!
- Coding was Hard Until I Learned These 2 Things
How do you go from “aspiring programmer” to someone who can actually compete for good, coding-heavy jobs? Natassha Selvaraj’s viral hit looks at the practical aspects of developing a growth mindset and building a daily programming routine. - 6 Bad Habits Killing Your Productivity in Data Science
As Donato Riccio points out, becoming more productive isn’t only—or even primarily—about learning and doing more; avoiding or breaking habits that are detrimental to your work is just as important. The ones Donato focuses on are particularly relevant for the daily workflows of data scientists. - Forget RAG, the Future is RAG-Fusion
Retrieval-augmented generation has become a common approach for optimizing large language models, but it comes with major drawbacks. Adrian H. Raudaschl presents RAG-Fusion, a modified technique that addressed these challenges by incorporating reciprocal rank fusion and generated queries into the process.
- Introducing KeyLLM — Keyword Extraction with LLMs
Still on the topic of making LLMs more efficient, Maarten Grootendorst recently shared the news of the launch of KeyLLM, his extension to the KeyBERT package, which facilitates keyword extraction at scale. He then walks us through an example based on the open-source Mistral 7B model. - How to Become a Data Engineer
If you’re a beginner-level IT practitioner or intermediate software engineer who would like to make a career change, ????Mike Shakhomirov’s practical guide to transitioning into a data engineering role is a great resource to explore. - Creating New Data Scientists in the Age of Remote Work
How has the transition to remote and hybrid work models affected early-career data scientists? Stephanie Kirmer offers a thoughtful reflection on the kinds of challenges both employers and employees are facing in this (relatively) new territory, and what they can do to ensure the next generation of data professionals can still benefit from the experience of seasoned veterans. - TimesNet: The Latest Advance in Time Series Forecasting
Stay up-to-data with the latest cutting-edge research on time-series analysis: Marco Peixeiro’s latest explainer zooms in on TimesNet, unveiled in a paper published earlier this year. It’s a model that uses a CNN-based architecture to achieve state-of-the-art results across different tasks, “making it a great candidate for a foundation model for time series analysis.” - 5 Generative AI Use Cases Companies Can Implement Today
There’s buzz, and then there’s actual value — and it’s not always easy for business leaders to tell the difference when it comes to generative-AI tools. Barr Moses is here to the rescue, outlining five promising use cases where gen-AI approaches might make sense for companies to experiment with. - Interactive Dashboards in Excel
If you’re looking for new creative outlets to present your data in engaging and accessible ways, why not give Excel a try? Jake Teo’s step-by-step tutorial explains how to make the most of the “most widely used data engineering and analytics software in the ‘non-tech’ world” to create sleek interactive dashboards. - Strategic Data Analysis (Part 1)
In her recently launched series, Viyaleta Apgar offers a structured, detailed overview of the questions data analysts are tasked with answering — and the various approaches they can use to address them effectively. If you haven’t read it already, we recommend starting at the very beginning: Part 1 outlines the four basic types of questions data analysts are likely to tackle. (Or feel free to jump ahead to Part 2, which focuses on descriptive questions.)
Our latest cohort of new authors
Every month, we’re thrilled to see a fresh group of authors join TDS, each sharing their own unique voice, knowledge, and experience with our community. If you’re looking for new writers to explore and follow, just browse the work of our latest additions, including Daniel Warfield, Satwiki De, Samuel Montgomery, Alexander Nikitin, Aman Steinberg, Hamed Seyed-allaei, Matheus Cammarosano Hidalgo, Malte Bleeker, Christopher Karg, Akif Mustafa, Gabriel Moreira, Jake Teo, Ilia Teimouri, Jeremie Charlet, Ed Izaguirre, Silvia Onofrei, Markus Stadi, Kairo Morton, Josu Diaz de Arcaya, Deepsha Menghani, Jon Flynn, Lennart Langouche, Guillaume Colley, Angjelin Hila, Emmanouil Karystinaios, Sofia Rosa, Anthony Alcaraz, Kseniia Baidina, Kenneth Ball, and Nicholaus Lawson.
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Until the next Variable,
TDS Editors
Productivity Tips, Data Career Insights, and Other Recent Must-Reads was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
