Data Science
Data science is using data to answer questions and inform decisions. Not just "what happened" but "why did it happen, will it happen again, and what should we do about it?" The work spans exploratory analysis, statistical modeling, prediction, causal inference, and communicating findings to people who need to act on them.
The discipline sits between pure statistics and applied engineering. A data scientist frames the right question, finds and prepares the data, chooses the right analytical approach, validates the results, and communicates what it means in language that decision-makers can use.
The track
Projects span from basic exploratory analysis to complex modeling and causal inference. You'll direct AI to analyze data, build models, and produce reports for fictional clients, then verify whether the analysis is sound, the conclusions are warranted, and the recommendations are useful.
The skill you're building isn't statistics from a textbook. It's directing AI to do analytical work and verifying the result: knowing which questions to ask, which methods fit, where AI's analysis falls apart, and how to communicate findings honestly.
Before you start
- Read the Introduction: what the field is, how the work flows, what tools you'll use
- Complete the Platform Setup: accounts, terminal, Claude Code, Git (same for all tracks)
- Complete the Data Science Setup: Python, analytical libraries, and a hands-on demo