Capstone projects
Everyone at the end. Six full analyses with nothing scaffolded — the portfolio that proves the rest of it landed.
6 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- How did daily cases evolve over time in each country, once we smooth out reporting noise?
- When did each country peak, and how tall was the peak?
- How do countries compare per capita (the only fair basis)?
- What was the case fatality ratio (CFR), and how did it change?
- How fast was exponential growth early on (doubling time)?
- What does length of stay look like overall and by department?
- Do departments differ in LOS significantly (or is it chance)?
- Which factors are associated with 30-day readmission?
Curriculum
6 sections · 6 lessons
Section 1
6 modules
Section 1
- COVID-19 Data AnalysisYour first capstone is a time-series epidemiology analysis. You'll take daily case and death counts across several countries and turn raw, noisy numbers into a clear story: how fast did the virus spread, when did each country peak, how do fair comparisons work across populations of different sizes, and how deadly was it?
- Hospital Data AnalysisHospitals generate rich operational data, and analysing it well saves money and lives. In this capstone you'll work with patient admission records to answer questions a hospital administrator actually cares about: Which departments have the longest stays?
- Titanic Survival AnalysisThe Titanic dataset is the "hello world" of machine learning — and for good reason. It's small, human, and full of teachable structure: who survived the 1912 disaster, and why?
- Sales DashboardAnalysis only creates value when it's communicated. This capstone is about turning a raw retail-sales dataset into an executive dashboard — the kind of single-screen, self-explaining report a sales director reads in ten seconds and acts on.
- Customer Churn PredictionKeeping a customer is far cheaper than winning a new one, so predicting churn — who's about to leave — is one of the highest-value tasks in applied data science. This capstone runs the full ML workflow on a telecom-style churn problem, but with a crucial business twist most tutorials skip: the default 0.5 threshold is almost never right.
- Financial Data AnalysisThe final capstone takes you to the markets. Financial data is the ultimate time-series playground — noisy, fat-tailed, and full of hard-won conventions.
About this course
6 modules from the Python for Data Analysis curriculum.
This course covers COVID-19 Data Analysis, Hospital Data Analysis, Titanic Survival Analysis, Sales Dashboard, Customer Churn Prediction and Financial Data Analysis.
6 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
- Practitioners who can already run the basics and want Capstone projects to a standard they can defend
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- Comfort with the earlier tracks of Python for Data Analysis, or equivalent experience
- Your own dataset is welcome but not required — every module ships with the data it teaches from
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