10 Real Ways to Get Better at Data Science & AI Ankit Tomar, June 13, 2025June 6, 2025 Over the past decade, I’ve built countless models, launched data products, and worked across geographies in the field of data science and AI. One thing that stands out to me is the wide skill gap among data science professionals. While many are good at the core task—model development—most fall short in the complementary skills that truly differentiate a good data scientist from an average one. Back in 2020, we saw a massive wave of people entering the field, driven by booming demand and the “shiny object” appeal of AI. Many joined for the high pay and prestige, but not necessarily out of genuine interest. And now, as the hype has settled, a lot of them are struggling to sustain their careers and are pivoting to other roles. This blog is my attempt to share what I’ve learned—and what I wish more people knew—about becoming a truly effective data scientist. ⚠️ Disclaimer: This post combines my personal experience and insights I’ve learned from others. I don’t claim sole credit, and I deeply appreciate the broader data science community for its contributions. 1. Pursue Higher Education (If You Can) Video courses are great to get job-ready—but they don’t always build deep knowledge. A Master’s degree in data science, AI, or a related field can give you a strong foundation in math, theory, and engineering principles.These programs also give you the luxury of time—to read books, reflect, and slowly internalize concepts. The hands-on problem-solving part, however, often needs to be learned outside—via side projects or real-world jobs. 2. Avoid Shiny Certificates I’m not a fan of paid “AI certificates.” Most are overpriced and don’t deliver enough value. You’ll probably learn more through free resources online, project-based learning, or actual work experience. 3. Understand the Domain Data scientists don’t work in a vacuum—they solve business problems. The best ones I’ve seen either know their domain deeply (e.g., marketing, finance, supply chain) or actively try to learn it.Speaking the language of the business helps you ask better questions, propose smarter hypotheses, and build trust with stakeholders. 4. Lead with Hypotheses, Not Models Jumping straight into model building without clear hypotheses is a rookie move. Good data scientists spend time thinking deeply about what they’re solving and why it matters. Modeling is just one part of the process—often not even the biggest. 5. Become a Great Communicator You’ll spend a lot of your time explaining your work—whether it’s to stakeholders, teammates, or decision-makers.Build the habit of storytelling. Good communication helps you get buy-in, explain results, and improve your models with real-world feedback. 6. Understand the Data Ecosystem Don’t be the data scientist who only works off CSVs or flat files. Learn where your data comes from—APIs, warehouses, event streams, etc. Spend time with the data engineering or platform teams. Understand the pipelines and limitations. It will elevate your work. 7. Prototype First. Always. Your first model won’t be your best model—and that’s okay. Build quick prototypes, learn what works, throw away what doesn’t, and iterate.The more models you build and discard, the more thinking you’ve done. That’s the difference between experimenting and blindly coding. 8. Measure What Matters One of the most underrated (but critical) skills is setting the right business metrics.Statistical accuracy is nice—but decision-makers care about impact. Tie your models to metrics they care about: revenue lift, churn reduction, time saved. That’s how you make your work matter. 9. Stay in the Loop (Macro Learning) Data science is evolving fast—and AI is evolving even faster. What’s hot today might be outdated next year.You don’t need to chase every trend, but you do need to stay aware of the big shifts. Follow news, read papers, watch thought leaders, and stay curious. 10. Listen More Than You Speak You’re constantly gathering information—from business users, domain experts, and customers.Being a great listener helps you identify problems better, build relevant solutions, and create trust. Ask good questions, but don’t rush to answer. Absorb first. Final Thoughts There are many more lessons I could add, but if you focus on these 10, you’ll already be ahead of most.Success in data science and AI isn’t just about models—it’s about curiosity, communication, business context, and the drive to improve every day. Let me know what your biggest challenge has been in your data journey—or what helped you level up. I’d love to hear your story. And if you liked this blog, feel free to subscribe for more content around AI, data careers, and life as a data professional. Post Views: 631 Career Machine Learning AIgenAIML
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