Most data science content stops at notebooks, libraries, and clean examples. This session goes a step further.

In this showcase-style workshop, we walk through how data science is actually practiced—from framing the right questions to choosing methods, dealing with messy data, and making decisions when things don’t work as expected. You’ll explore real academic and independent projects, including causal inference, clustering, target trial emulation, and time-series analysis using public health datasets.

Rather than focusing on flashy models, the session highlights structured thinking, methodological tradeoffs, and honest reasoning: why certain approaches were chosen, what failed, and how conclusions were reached. Ideal for students who want to understand how data science works beyond tutorials—and how to think like a real data scientist/engineer.

What to expect:

Explore a data analytics repository featuring causal inference, clustering, and target trial emulation in Python. Then dive into an independent flu vs. COVID-19 time-series analysis using public health datasets. See how data scientists frame problems, choose appropriate methods, and interpret results, with transparent reasoning about what worked, what didn't, and why.

Real Projects, Real Workflows, Real Decisions!