The Skills That Help Data Scientists Grow
Even if you’re in the early stages of your data science learning journey, you probably have a solid idea of the core skills you’ll need to get a foot in the door: some statistics, working knowledge of a programming language (or two), and a reasonable grasp of how to process, analyze, and visualize data, to name a few obvious ones.
What about the skills that will help you thrive and grow in your career in the long run, though? That’s where things often get murkier — and where this week’s highlights come in handy. We’ve selected a handful of articles by data professionals who share actionable insights based on their own experiences and non-linear career paths. They may focus on specific domains and roles, but the lessons they offer are applicable to many other real-world situations. Let’s dive in.
- One could make a strong argument against looking at skills through a hard-vs-soft binary, but it’s undeniable that those we tend to categorize as “soft” are among the most essential for finding success in a data-focused career. Eirik Berge recently focused on five crucial skills—including collaboration and mentoring—and infused these concepts with refreshingly concrete advice.
- Speaking from the perspective of a software engineer, Naomi Kriger put together a helpful roadmap for nailing project presentations in the context of job interviews. The lessons you’ll find here, however, will be just as useful at your next quarterly planning meeting, ML pipeline postmortem, or any other occasion when telling a clear and compelling story is key.
- Debut TDS contributor Fiona Victoria recently completed the arduous process of applying to PhD programs in artificial intelligence, and wrote about the (many) different factors to consider and steps to take along the way. Even if grad school isn’t on your horizon, Fiona’s thoughtful approach may help you navigate the stress of the unknown — a feature baked into many other career-related decisions.
- “If you’re an aspiring Data Scientist, it might surprise you to hear that this risk of burnout never really goes away,” Matt Chapman warns — and that’s especially true for people transitioning into the field from a different role. Matt’s tips on building your career with long-term sustainability in mind focus on better prioritization and rest, which most of us could benefit from regardless of the career stage we’re at.
If you’ve made it this far, congratulations: your superior time-management skills clearly allow you to keep on reading — which we hope you will, as our other weekly highlights are too good to miss:
- Conor O'Sullivan’s primer on the risks AI tools pose to members of the trans community tackles an urgent and timely issue—and calls for greater awareness and willingness to act from all data science and ML practitioners.
- How do you go about explaining the anomalies in your data? Mariya Mansurova’s accessible new guide on root-cause analysis answers this question in great detail.
- Catch up with emerging techniques for advancing green AI by following along Federico Peccia’s helpful overview of recent work in this increasingly important field.
- For a fresh perspective on recommender systems that successfully retain users, read Christabelle Pabalan’s thorough explainer on the concepts of novelty and serendipity.
- Elena Samuylova’s beginner-friendly introduction to drift detection proposes five methods for measuring drift in ML embeddings.
- MusicGen, a new music-generation model, can take a reference melody and create new themes based on it. Max Hilsdorf tests the model’s abilities and reflects on its potential effects on musicians’ creative process.
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Until the next Variable,
TDS Editors
