From effective storytelling to strategic career-planning, the skills you need in order to progress in your data science career are varied and increasingly interdisciplinary. Unlike, say, stats or programming, these are also areas where structured educational environments, like degree programs or bootcamps, can only get you so far.
Fortunately, TDS authors come from an incredibly diverse range of professional and personal backgrounds, and have learned a great deal about what works—and what doesn’t—in real-world situations across a wide range of organizations. For our highlights this week, we’ve brought together a strong lineup of articles that focus on these less technical, but still crucial, aspects of data science work: they offer valuable insights based on these professionals’ lived experience.
- Oh, You Meant “Manage Change”?
Navigating the rapidly changing terrain of data science is always a challenge, and even more so within organizations dealing with shifting business priorities. Marc Delbaere unpacks the difficulties of practicing change management within the context of data teams, and looks at how leaders and individual contributors can balance their occasionally conflicting goals. - The 4Ds in Data Storytelling: Making Art Out of Science
“Data scientists’ work without storytelling is merely digital fortune-telling,” says Zijing Zhu, PhD, who goes on to share a detailed framework that goes beyond the basics of visualization to help practitioners deliver data insights with higher efficiency and impact. - Know Your Audience: A Guide to Preparing for Technical Presentations
Approaching the topic of data storytelling from a different angle, John Lenehan looks at the nitty-gritty of preparing data-focused presentations, and offers concrete advice on structuring your insights in ways that engage colleagues and stakeholders and address their concerns. (When you’re done, you should also explore John’s follow-up article, on translating data into cohesive narratives.)
- Don’t Apply to Tech Before Mastering These 6 Must-Have Data Science Skills
In her latest career-focused guide, Khouloud El Alami zooms in on some of the key areas in which data scientists need to have a firm footing if they want to become competitive candidates for roles in major tech companies. Based on her experiences at Spotify, Khouloud covers technical and non-technical skills alike, and explains how they all ultimately must connect to measurable impact. - 3 Key Career Decisions for Junior Data Scientists
Despite the field’s relative recency, there are by now fairly established career paths for data professionals—but what if you don’t want your career to follow one? Matt Chapman’s new post invites early-career folks to examine their priorities, reflect on what truly matters to them, and shape their choices around the types of roles they pursue accordingly.
If you’re looking to expand your skills and knowledge in either directions, too, we’ve got you covered: here are some excellent reads we wouldn’t want you to miss.
- For a fresh look at recommendation systems, head right over to Irene Chang’s introduction to Thompson sampling, the first part in an ongoing series on these ubiquitous algorithmic tools.
- Learn all about pseudo-random numbers and their role in machine learning by following along Caroline Arnold’s accessible and well-illustrated primer.
- Walking us through their latest research, Michael Galkin and his coauthors introduce ULTRA, a foundation model for knowledge-graph reasoning.
- In a recent deep dive, Ms Aerin explores RLHF (reinforcement learning from human feedback) and its role in the training data and learning paradigm powering recent advances in large language models.
- We’re thrilled to welcome a new Carolina Bento explainer: a hands-on guide to hidden Markov models, complete with an intuitive implementation.
- For his debut TDS post, Jon Flynn presented a lucid and comprehensive account of current approaches to continual learning in AI and how they aim to tackle the (major) challenge of keeping models up-to-date.
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Until the next Variable,
TDS Editors
Take the Next Step to Expand Your Data Science Skill Set was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
