Learning how to be a professional data scientist is different now, but it’s not impossible Today’s column is partly about data science, but it’s also about the sociology of work. As a senior practitioner in the field, I started my data science career long before Covid-19 and the radical shift in the way we work today developed. I started my professional career years before even that. As a result, my years of learning how to be a professional anything, let alone a professional data scientist, were spent in close quarters with much more experienced people, and in many ways that made it possible for me to get where I am.

Businesses are jumping on a bandwagon of creating something, anything that they can launch as a “Generative AI” feature or product. What’s driving this, and why is it a problem? The AI Hype Cycle: In a Race to Somewhere? I was recently catching up on back issues of Money Stuff, Matt Levine’s indispensable newsletter/blog at Bloomberg, and there was an interesting piece about how AI stock picking algorithms don’t actually favor AI stocks (and also they don’t perform all that well on the picks they do make).

Data science roles can be very different, and job postings are not always clear. What hat do you want to wear? After the positive responses to my recent post in Towards Data Science about Machine Learning Engineers , I thought I would write a bit about what I think are the real categories of roles for data science practitioners in the job market. While I was previously talking about the candidates, e.

Machine Learning Engineers — What Do They Actually Do? Does “Machine Learning Engineer” mean something new to our field? If so, what? The title is a trick question, of course. Much like Data Scientist before it, the title Machine Learning Engineer is developing into a trend in the job market for people in our profession, but there is no consensus about the meaning of the title or the functions and skills it should encompass.

I sometimes mention in my written work and speeches that I have a sociology background, and used to be an adjunct professor of sociology at DePaul University before embarking on my data science career. I loved sociology, and still do — it shaped so much about how I understand the world and my own place in it. However, when I made a career change and turned to data science, I spent a lot of time explaining how that background, training, and experience were assets to my practice of data science, because it wasn’t obvious to people.

If you haven’t read it yet, please start with Part 1 to understand the foundations of this project and why we did it! Implementation In part 1, we talked about applying a data science mindset to the problem of location accuracy. But how do we actually carry out this process? We used python and a combination of several tools (see table below) to make this idea a reality. The first thing we needed was a clustering technique.

Want to learn more about this project and how we implemented it? Join me at MLOps Community Chicago on Nov 10, 2022 where I’ll be presenting this work with a special focus on the deployment and taking it through to production. At project44, we offer customers a whole assortment of data driven products that help them better understand their shipments and the movement of goods around the world. We build complex, intelligent tools that make logistics easier and more transparent.