Hi Tip-Sheeters,

This week, I’m sharing a few of the questions that I’ve had this past year from readers, people at events, or folks who’ve reached out on LinkedIn. If you ever have a question or something I can assist with, please hit reply on the newsletter email, and I’ll be happy to share my thoughts.

Let’s jump in!

Q&A About Data, Tech, and Career

Q: What is the new tool or framework you are learning about right now?

If you’ve been keeping up with the Tip Sheet, you won’t be surprised when I say Model Context Protocol (MCP). LLMs need to inhale data for context to generate their text, and MCP servers look likely to become the standard method.

The tools I have explored most so far have been the FastMCP framework and FastMCP cloud — I shared both of those in Tip Sheet #44.

Q: How do I get my first data scientist position after completing my degree?

There is some great advice out there from others, and I always recommend people check out Nick Singh, who has written some great content on this.

I’ll pivot here and suggest that if you haven’t yet made progress yet through the usual routes, go beyond the "data scientist title" and apply for early-career positions in related roles, such as data engineer or data analyst.

Look for companies that have data science functions (either formally or informally) and then plan to build credibility and seek horizontal moves into the data science department or specialization when you have built your chops. Or you might find that you’re enjoying what you’re doing and just lean into those roles. All the data titles get to have a lot of fun with the cool tech these days.

Q: How are you working with generative AI in your development process?

For my side project work, I am still mostly using ChatGPT as a coding buddy to talk through, plan, review, and, in some cases, generate code.

I would like to find time to explore GitHub Copilot coding agents for maintenance work of creating pull requests on some of my open source repos, but I haven’t yet.

Q: How do you see AI affecting job prospects for developers and data scientists?

I have a hard time making any strong predictions because the news feeds on this topic are so whipsawed between doom-and-gloom, startup hype, AI cynics, and everything in between.

My current hypothesis is that skilled technologists and data folks have a bright future ahead of them, although I’ll openly admit that I don’t know exactly what that means in raw numbers or individual roles.

But it’s important to keep the end in mind: use tech and data to bring real value to the business. The better our tools get, the more value we can bring, and the more valuable our skills become. So don’t get too tied to one skill or task that you’re currently doing: stay nimble and focused on business value.

Q: What do you think makes technical books a good format for learning?

I’ll mangle the quote, but someone said that a non-fiction book is the result of a relatively smart person spending a great deal of time thinking intently on one topic for a long time. That gives a depth of explanation on a topic that really isn’t possible in any other medium.

In my own book’s case, I was able to take an end-to-end approach to design and building APIs, then using them in data science and AI. That’s probably a semester’s worth of courses in a university setting — far beyond what I could achieve in even a two-day workshop at a training event.

At one of my speaking events, someone smarter than me gave a really good hueristic: the amount of time that a learner should spend on training materials is proportional to how long it took to create. On one end of the spectrum, a tweet doesn’t take long to write and doesn’t deserve much attention. On the other end, a book or long video training was given more thought by the creator and is worth the investment of more time from the learner.

Q: What non-technical skills have been most valuable for your career?

I would say curiosity and openness have been big for me. In every organization I’ve worked for, there was a need for someone to explore new technology and figure out which pieces have business value (and which ones don’t).

I’ve always enjoyed that trailblazer role, with a focus on the learn-implement-share cycle. So I’ve always been willing to jump in the deep end and then freely share what I learned with my teammates to help them grow.

Q: What technical skill or habit would you prioritize early in a career in data and tech?

The first couple of years in the industry are about getting your feet under you as a contributing employee, and learning to be a professional and a good teammate.

Once that is established, you should start thinking about building a comprehensive knowledge of enterprise technology. Think beyond your small piece of the puzzle you are working on and find people around you who have a bigger picture knowledge.

As you start building that broad knowledge, always seek to learn the industry terminology instead of just whatever term is used at your company or on your team. One good place to find industry terminology is through certification programs.

That's all for this week -- if you have some thoughts to share on those questions, I'd love to hear them. Hit reply and let me know!

Keep coding,

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Ryan Day

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👉 https://tips.handsonapibook.com/ -- no spam, just a short email every week.

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