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Not long ago, it seemed like getting your first job in data science or moving into a more interesting data or machine learning role followed a fairly well-defined sequence. He learned new skills and expanded on the ones he already had, demonstrated his expertise, zeroed in on the most suitable listings and…sooner or later, something good would come his way.
Of course, things were never that simple, at least not for everyone. But even so, we have experienced something of a change in mood in recent months: the job market is more competitive, companies' hiring processes more demanding, and there seems to be much more uncertainty and fluidity in technology and beyond.
What's an ambitious data professional to do? We prefer to avoid shortcuts and magic tricks in favor of fundamental skills that show your deep understanding of the problems you aim to solve. Our most experienced authors seem to be pointing in the same direction: the series of articles we're highlighting this week offers concrete insights for data and ML professionals across a wide range of career stages and focus areas; They highlight 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 their audience and creates impact. Hennie Harder offers practical guidelines for crafting a compelling slide presentation that packs a punch and delivers your message to diverse audiences of stakeholders. - How to further develop as a data scientist
For Eric Levinson, “being a data scientist often involves having the mindset of a lifelong learner.” While courses, books, and other resources abound, what makes their advice particularly useful is their focus on learning that can take place during regular work hours, from pair programming and mentoring to knowledge exchanges and feedback loops. .
