Feeling inspired to write your first TDS post? We’re always open to contributions from new authors.
Not so long ago, it seemed like landing your first data science job or switching to a more exciting data or ML role followed a fairly well-defined sequence. You learned new skills and expanded your existing ones, demonstrated your experience, zoomed in on the most fitting listings, and… sooner or later, something good would come your way.
Of course, things were never quite as straightforward, at least not for everyone. But even so, we’ve experienced somewhat of a mood shift in the past few months: the job market is more competitive, companies’ hiring processes more demanding, and there appears to be a lot more uncertainty and fluidity in tech and beyond.
What is an ambitious data professional to do? We prefer to avoid shortcuts and magic hacks in favor of foundational skills that showcase your deep understanding of the problems you aim to solve. Our most seasoned authors seem to point at the same direction: the lineup of articles we’re highlighting this week offer concrete insights for data and ML practitioners across a wide span of career stages and focus areas; they foreground continuous learning and building resilience in the face of change. Enjoy your reading!
- Combining Storytelling and Design for Unforgettable Presentations
Regardless of role, seniority level, or project type, effective storytelling remains one of the most crucial skills data professionals can develop to ensure their work reaches its audience and makes an impact. Hennie de Harder offers actionable guidelines for crafting a compelling slide deck that packs a punch and delivers your message to diverse audiences of stakeholders. - How to Keep on Developing as a Data Scientist
For Eryk Lewinson, “being a data scientist often involves having the mentality of a lifelong learner.” While courses, books, and other resources abound, what makes his advice particularly helpful is its focus on learning that can take place during your regular work hours, from pair programming and mentoring to knowledge exchanges and feedback cycles.
