Phase 10: Capstone projects
Data Analyst Portfolio Projects: 8 Free Capstones
Large end-to-end projects with a brief, data, deliverables, a rubric and a worked solution.
A capstone is the size of real work: 10 to 14 hours, an ambiguous brief, several datasets, and a deliverable a hiring manager can open and run. These are the projects you put in your portfolio.
The 8 lessons, in learning order
- 1. E-commerce performance review
Prepare the quarterly business review (QBR) for an online store, and answer whether its new checkout platform hurt sales.
Level 3: Advanced · about 10 hours
- 2. Customer retention and cohort analysis
Find where retention breaks and which cohort to study first.
Level 3: Advanced · about 10 hours
- 3. Marketing attribution and campaign effectiveness
Reallocate next quarter's budget across channels, and defend it.
Level 3: Advanced · about 10 hours
- 4. SaaS subscription and churn analysis
Build the churn model the finance team can plan with, and find out whether recurring revenue is really falling.
Level 3: Advanced · about 10 hours
- 5. Executive dashboard
Design, model and verify the CEO dashboard, in the tool of your choice, so that the leadership team stops arguing about the numbers.
Level 3: Advanced · about 10 hours
- 6. Automated analytics reporting
Make the weekly business review build, test and send itself, so it is never wrong quietly, never sent twice, and says when last week's numbers changed.
Level 3: Advanced · about 10 hours
- 7. A natural-language-to-SQL assistant
Build, evaluate and guard an assistant that answers store managers' questions from a database, and decide whether it is ready for them.
Level 3: Advanced · about 12 hours
- 8. A multi-agent analytics copilot
Agents that plan, query, check and recommend, with a human approving each step. Can we trust what they found about the fall in weekend rentals, and what should we do?
Level 3: Advanced · about 14 hours
What you will be able to do
By the end of the phase you will have built and tested each of these yourself, one per lesson:
- E-commerce performance review:
clean_ordersandclean_refunds: one row per record, latest load wins, as of any date, in one unit (excluding tax). - Customer retention and cohort analysis:
clean_usersandactive_months: one row per real user and the months each was active, by product's definition. - Marketing attribution and campaign effectiveness:
clean_spendandclean_orders: one value per week and channel including fees, one row per order inside the quarter. - SaaS subscription and churn analysis:
clean_accountsandclean_events: one row per account and per event, latest load wins, as at any load date, amounts in EUR a month. - Executive dashboard:
clean_sales: one row per shop-day, the latest send, net revenue in one definition. - Automated analytics reporting:
read_dropsandnormalise_outcome: read every drop up to a date and map two generations of outcome codes to one. - A natural-language-to-SQL assistant:
clean_tables: the snapshot: latest load wins, late rows out, revenue net of the delivery fee for every month. - A multi-agent analytics copilot:
clean_rentals: the snapshot: latest load wins, late rows out, false starts out in every month.
How to start
Clone the course and run the first lesson's test. It fails until your exercise is right. You need only Python 3.9 or later.
git clone https://github.com/Ani19arya/analytics-from-scratch
cd analytics-from-scratch/phases/10-capstone-projects/01-ecommerce-performance-review
python3 test_lesson.pyJobs that ask for these skills
Open roles on Real Analyst Jobs where this phase's skills come up, read daily from company career pages:
