Phase 09: Business case studies
Data Analyst Case Studies: 9 Free Business Cases
Real business questions, end to end: context, KPIs, analysis, recommendation and its limits.
The earlier phases teach methods. This phase teaches the job: a vague business problem, data that does not quite fit, and a recommendation someone will act on. Each case takes about four hours and uses SQL, statistics, metrics and Python together.
The 9 lessons, in learning order
- 1. E-commerce: why is revenue falling?
Revenue is down 8% on last quarter. Is it traffic, conversion, basket size or mix?
Level 2: Intermediate · about 4 hours
- 2. SaaS: reduce subscription churn
Which customers leave, when, and which change would keep the most revenue?
Level 2: Intermediate · about 4 hours
- 3. Marketing: is the campaign incremental?
The campaign got credit for 3,000 sales. How many would have happened anyway?
Level 2: Intermediate · about 4 hours
- 4. Food delivery: why are cancellations rising?
Cancellations doubled in six weeks. Is it restaurants, riders, customers or the app?
Level 2: Intermediate · about 4 hours
- 5. Fintech: loan approvals and defaults
If we approve more applicants, how many more defaults do we buy?
Level 2: Intermediate · about 4 hours
- 6. Retail: what to restock
Which products deserve more inventory next month, and which are tying up cash?
Level 2: Intermediate · about 4 hours
- 7. Growth: why is acquisition cost rising?
CAC is up 35% this year. Is it channel saturation, pricing or tracking?
Level 2: Intermediate · about 4 hours
- 8. Banking: which segments are profitable?
Which customer segments earn more than they cost to serve?
Level 2: Intermediate · about 4 hours
- 9. Operations: why are deliveries late?
On-time delivery fell from 94% to 87%. Where in the chain is the delay?
Level 2: Intermediate · about 4 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: why is revenue falling?:
QUARTER_SQL: sessions, orders and revenue per quarter, without a fan-out. - SaaS: reduce subscription churn:
LIFE_SQL: a life table per plan, with a recursive CTE for the months. - Marketing: is the campaign incremental?:
GROUP_SQL: customers, orders, code orders and revenue per period, segment and group, with 0 for empty groups. - Food delivery: why are cancellations rising?:
WEEKLY_SQL: the cancellation rate per week. - Fintech: loan approvals and defaults:
BAND_SQL: applicants, approvals, matured loans and defaults per score band. - Retail: what to restock:
SKU_SQL: one row per product with weeks of history, in-stock weeks and units sold, keeping products that have no sales. - Growth: why is acquisition cost rising?:
QUARTER_SQL: spend, signups and subscribers per quarter and channel, joined without fan-out. - Banking: which segments are profitable?:
PROFIT_SQL: revenue, cost to serve, charge-off and profit per customer, with the rates read from the rates table. - Operations: why are deliveries late?:
WEEKLY_SQL: shipments and on-time rate per week.
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/09-business-case-studies/01-ecommerce-why-is-revenue-falling
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:
