Portfolio projects · 4 guides · public data
Four projects that look like real analyst work, on public datasets you can download today. Each guide starts from a business question and ends with a write-up, and every step links to the levels that teach the skill.
Between them they cover Excel, Power BI, SQL, pandas and basic statistics. Two finished projects with good write-ups beat six half-done notebooks.
Excel, Power BI · Beginner · 10 to 13 hours
Which product categories and regions drove revenue in 2017, and where did average order value slip?
Data: Brazilian E-Commerce Public Dataset by Olist · Online Retail II · Financial Sample workbook
Open the guideSQL, pandas · Intermediate · 12 to 16 hours
What share of first-time customers order again within 90 days, by month of first order, and is it improving?
Data: Online Retail II · Brazilian E-Commerce Public Dataset by Olist · theLook eCommerce
Open the guidePython, Statistics · Intermediate · 8 to 12 hours
Did the new landing page convert better than the old one, by enough to be worth shipping?
Data: A/B testing (ab_data.csv) · Marketing A/B Testing · Mobile Games: A/B Testing (Cookie Cats)
Open the guideSQL, Power BI, Excel · Advanced · 14 to 20 hours
How did revenue, orders, average order value and return rate move this month, against last month and the same month last year, and why?
Data: AdventureWorks sample databases · theLook eCommerce · Brazilian E-Commerce Public Dataset by Olist
Open the guideEach project starts from what a manager would ask, written as a SMART goal. Reviewers look for that first: it shows you know why the analysis exists.
Frame, clean, analyze, chart, write up. Every step links to AnalystQuest levels where you can try the skill on the Harbor & Pine data before you use it on the real files.
A README structure for GitHub, a LinkedIn post outline, two CV bullets and a write-up template you can copy, plus the mistakes that reviewers notice.
