Staying Ahead: How to Keep Learning in a Fast-Moving Data Science World

The data science field is evolving at a breakneck pace. New papers, tools, and benchmarks emerge every week, making it challenging to stay current. Flatiron School sat down with Frances Cue, a data scientist and AI engineer at Hartford HealthCare and one of our data science facilitators, to discuss practical strategies for keeping up in this fast-moving field.

The Pace of Change is Real

Frances has been in data science for nearly 6 years, and the transformation during that time has been remarkable. "The changes that I've seen over in that small amount of time in 6 years, it's been absolutely crazy, especially with the large language models coming out," she shared.

For professionals and newcomers alike, the constant stream of innovations can feel overwhelming. But she emphasized that success isn't about knowing everything. It's about building sustainable habits and focusing your energy strategically.

Stay Informed Without Burning Out

One of Frances's key strategies for keeping up is subscribing to targeted newsletters. She mentioned Rundown AI and various data science substacks as valuable resources. The key insight? You don't need to read everything every day. Simply having quality sources in your inbox ensures you'll catch major developments.

"I don't always get to open them every day, but it is good to have some newsletters in your inbox," Frances explained. This approach allows you to stay connected to the field's pulse without feeling pressured to consume every piece of content.

Essential Tools That Changed Everything

When asked about tools that have become indispensable, she didn't hesitate: AI code assistants. Tools like GitHub Copilot, Cursor, and Gemini CLI have transformed her workflow. "I don't think I could live without the code assist anymore," she said, noting that tasks that once took a day or two for exploratory data analysis can now be completed much faster.

The caveat? You need foundational knowledge to use these tools effectively. "As long as you know how to prompt it properly and you know how to check and make sure that it's not going haywire on its own, it's a very, very helpful tool to have," she advised.

Portfolio Over Certifications

For those just starting in data science, she offered clear guidance: prioritize building a strong portfolio. While certifications have their place (especially in fields like cybersecurity), showcasing well-documented, readable code projects is more valuable when you're beginning your career.

"Make sure that your code is nice in the notebooks, it's readable, nicely formatted, up to standards," Frances emphasized. Employers often ask new data scientists to walk through their projects, so having polished repositories on GitHub demonstrates both your technical abilities and your communication skills.

Ask Questions Early and Often

One of Frances's biggest regrets from early in her career was being too intimidated to ask questions. "I never used to ask questions. I was too scared," she admitted. This led to spending days stuck on problems that could have been resolved in five minutes with help from a senior colleague.

The lesson? Senior engineers and data scientists are there to support you. Asking thoughtful questions isn't a sign of weakness. It's an essential part of upskilling and demonstrates your engagement with the work.

Find Your Focus

With so many areas to explore in data science (NLP, computer vision, MLOps, data engineering), how do you avoid feeling overwhelmed? Frances's advice is to specialize in what genuinely interests you.

"If you're really interested in a topic that you know you want to learn more, just focus on that one thing," she suggested. For her, that was natural language processing. By diving deep into NLP rather than trying to master everything at once, she developed valuable expertise.

Once you're in a role, she recommends focusing on the industry-specific skills you need to succeed in that position. Working in healthcare, for example, meant learning the data side of the industry, not just general data science techniques.

Communication is Everything

If there's one skill Frances wishes she had developed earlier, it's communication. As an introvert, speaking up about data anomalies or explaining findings to stakeholders was initially difficult. But she learned that communication skills transfer everywhere in your data science career.

"The communication piece, once you have it, you take that communication skill with you everywhere you go," Frances noted. "It doesn't matter where you end up in the data science world. If you have that part taken care of, you could go anywhere with it."

A practical tip she shared: demo your work regularly, even when it's only halfway complete. "The more you learn how to explain how something works, the better you're going to be," she said. Practicing these explanations to both technical and non-technical audiences builds confidence and clarity.

Keep Learning Through Teaching

Frances approaches lifelong learning through multiple channels. She recommended asking employers about learning subscriptions during interviews (platforms like Pluralsight, Udemy, or Google Cloud can provide continuous education).

But one of the most effective learning strategies? Teaching others. "I learn by also by teaching others what I know," Frances explained. "If you're able to teach someone how something works, how the process works, I feel like it's somehow does something to your brain where you're able to actually memorize things a little bit better and understand things a little bit better."

This doesn't mean you need to become a formal instructor. Contributing to Stack Overflow, mentoring junior colleagues, or participating in open-source projects all provide opportunities to reinforce your knowledge by sharing it.

Plan Your Career Progression

France's final piece of advice for new hires: ask your employer for their career ladder documentation. Understanding what it takes to move from level one to level two (and beyond) removes ambiguity from your professional development.

"Make sure that you know how to get from level one to level two," she advised. "That's going to let you know how do you jump, how do you upskill, what do I have to do in order to get promotion or you know, level up."

The Path Forward

Staying ahead in data science isn't about chasing every new trend or mastering every tool. It's about building sustainable learning habits, focusing on areas that excite you, developing strong communication skills, and continuously seeking opportunities to both learn and teach.

The field will keep evolving rapidly, but with the right strategies, you can thrive in this dynamic environment. As Frances's journey shows, combining technical expertise with soft skills and a commitment to continuous learning creates a foundation for long-term success in data science.