With so much uncertainty in the past couple of years – from lockdowns and layoffs to global instability and inflation – the very notion of a career path can feel quaint. How can we plan our next year, to say nothing of the next decade, with so much in constant flux?
Even if your data science journey is still a fuzzy work in progress, we always recommend listening to others when they share their own successes, struggles, and epiphanies. This week, we’re presenting several standout posts where authors reflect on their data science Careers and flesh out the lessons they’ve learned along the way.
- A supportive mentor can make a huge difference. In our recent conversation with Murtaza Ali, we heard how the encouragement of one professor helped Murtaza discover his passion for data and teaching. As a current PhD student, he also elaborates on the kinds of questions you should ask yourself before deciding if an advanced date-science degree is the right move for you.
- How to turn a negative experience into a learning opportunity. A bad job interview can (and does) happen to the best of us. For Margo Hatcher, the important part is how we recover. Her post provides a handy framework that can help applicants get back up on their feet quickly. It focuses on technical interviews, but you can easily adapt it for other stages of the hiring process.
- Choosing the right area of growth is key. As a seasoned leader, Cassie Kozyrkov has seen many data analysts struggle to move ahead in their careers. Her unequivocal advice: focus on speed. Her post unpacks this maxim and explains how to navigate speed’s more complex dimensions.
- What if you want to make a big change? There are many roles a versatile data pro can transition into. If your starting point is entirely outside the field, however, the path into Data Science contains many more twists and turns. After nearly a decade in sales and business strategy, Lucy Rothwell took the leap – and is now in a position to offer some helpful observations about the process.
- The practical aspects of stepping into a management position. As Tessa Xie notes, there are many guides out there about landing a job, but fewer resources for people who want to leave an individual-contributor role to become a manager. So Tessa wrote one – and it’s full of pragmatic, experience-based ideas around communication, mentorship, and more.
You’ve made it all the way down, which means you deserve, at the very least, a handful of bonus picks! For a break from career and work talk, give any of these a try.
- Maya Murad continued her exploration of AI ethics with a new article: it walks us through a participatory framework that can enable the responsible use of algorithmic decision-making systems (ADS).
- If you’re still taking your first steps in the realm of outlier detection, don’t miss Sadrach Pierre, Ph.D.‘s thorough, Python-focused introduction, which covers both clustering and descriptive-statistics approaches.
- For another hands-on, tinkerer-friendly tutorial, check out Orjuwan Zaafarani‘s debut TDS post – it explains how to build an image-duplicate finder system from scratch.
- To end on a lofty note, Gadi Singer recently shared a fascinating overview of the concept of context in AI. He stressed its importance for ML practitioners, who will need to better incorporate it into their work in order to "raise the bar on machine enlightenment."
We appreciate the time you spent with us this week, and all the support you offer our authors. For another way to give your favorite authors a boost, consider becoming a Medium member.
Until the next Variable,
TDS Editors
