A successful data scientist is more than just a bundle of technical abilities (as important as these might be). The people we work with are first and foremost, well, people: their temperament, communications quirks, and approach to problem-solving are at least as important as, say, their mastery of Python. This week, we’ve selected a handful of recent articles that focus on the so-called soft skills that help data professionals stand out. Let’s dive in.
- Mental models and personality frameworks are invisible—but crucial. For Ani Madurkar, personal qualities like curiosity, resilience, and even humor are strong indicators of performance, to the point that "calling these concepts ‘skills’ is almost a disservice, because they are more personality frameworks that take far longer to master than traditional skills do." The good news, though, is that we can work on growing in these areas just as we do in coding or math.
- Taking the first steps towards building a network. By now, it’s a widely accepted truth that networking is key for landing (and excelling at) a new job. Sophia Yang‘s practical tips for newly minted data scientists cover what might be the most difficult part: getting started and forming the foundations for future success.
- Is your time precious? Use it more wisely. That feeling of days flying by and to-do lists getting longer? Most of us know it; few of us love it. Megan Dibble recently shared several concrete ideas for making the most of your workday, from parallel processing to ongoing optimization. Megan’s suggestions focus on the workflows of data analysts, but are easily transferable for other data-focused domains.
- Infuse your technical work with design thinking. Making smart data-storytelling decisions involves a mix of skills from across the soft-to-hard spectrum. Brian Perron encourages data scientists to think about their work beyond the constraints of specific tools and technical approaches—and his insights can inspire you to make small but visible tweaks to your visualization process.
- Your code isn’t good if nobody can read it. Programming? A soft skill?! Cranking out functional code might indeed be a highly technical activity, but as Julian West reminds us, no data scientist or developer codes in a vacuum. Julian’s five strategies for creating readable code ensure your work stays relevant and future-proof in the context of collaborative projects.
Interested in some of our other recent highlights? They covered so much ground! Here are a few favorites:
- Hajar Khizou covered the foundations of MLOps principles, and explained why you should consider applying them in your next ML project.
- Learn how to debug your code more effectively by following along Dr. Varshita Sher‘s most recent, Python-focused tutorial.
- Do you find stats terminology intimidating? don’t miss Cassie Kozyrkov‘s new primer, which explains basic (and less basic) concepts in clear, plain English.
- If, on the other hand, you’re feeling good about stats, but less so about nailing your next statistics interview, Emma Ding has a great overview of what to expect.
- For his debut TDS post, Lars ter Braak shared a thorough and useful introduction to probabilistic classification in the ML context.
We’re grateful as always for your support —and for the time you invest in the work we publish.
Until the next Variable,
TDS Editors
