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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!
One Mindset Shift That Will Make You a Better Data Scientist
“I’ve grown convinced that an ownership mentality is one of the key things that sets high performers apart from their peers.” Tessa Xie reflects on her own data science journey and outlines the three most common manifestations of this type of proactive mindset—and how to grow towards it step by step.A New Manager’s Guide to High Performing Data Science Teams
If you’ve finally achieved your goal to step into a management role, you might quickly discover that a whole new set of challenges awaits you. Zachary Raicik offers thoughtful advice on how to start on the right foot and set yourself—and your team—up for long-term success.Photo by Conor Brown on UnsplashCombining 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.
There are so many different ways to grow as data and machine learning professionals; our other reading recommendations this week can each be its own point of departure for learning about new skills, tools, and workflows.
Nobody likes to dwell on failure, but learning from mistakes can be extremely productive when done right—as Elaine Lu demonstrates in a post about the common causes behind AI projects that don’t succeed.Starting out with the goal of generating better and more customized movie recommendations, Ed Izaguirre walks us through the process of building a RAG system with a self-querying retriever.In a new installment of her “Courage to Learn ML” series, Amy Ma offers a (very) comprehensive survey on activation functions, weights initialization, and batch normalization (with implementations in PyTorch).Based on her recent research, Sandi Besen invites us to explore the current state of AI agent architectures, one of the most promising and fast-growing areas within an already-buzzing field.For an accessible and thoughtful explainer on Long Short-Term Memory (LSTM) networks, look no further than Diego Manfre’s excellent deep dive.Interested in some hands-on learning? Deepsha Menghani is back on TDS with a useful tutorial that shows how to make the most of Shiny modules—using the fun example of… Bigfoot sightings!If you’re curious about AI ethics and the challenges of model explainability, don’t miss Andy Spezzatti’s recent article, which unpacks the limitations of current approaches and points at promising future directions.
Thank you for supporting the work of our authors! We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, don’t hesitate to share it with us.
Until the next Variable,
TDS Team
How to Stand Out as a Data Scientist in 2024 was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
