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Project Catalog

Real work from six companies. Pick a scenario, write Python right here in your browser — no installs — and work through it with an AI mentor who's on your team.

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Pick a scenario

Real situations — investigate a drop, ship a report, clean an export a team needs.

Write code in your browser

Python runs locally in your browser. No setup, no installs.

AI mentor reviews

Every step is checked. Final notebook gets scored against a rubric.

Skill drills

Every skill these scenarios use has short, autograded practice drills behind it — practice until it's mastered, then apply it at work.

117 drills · 32 skills
Conditionals· 7Functions· 7Loops· 7Strings· 7Arrays· 5Async JS· 4+26 more skills
117 drills · 32 skills
Module 1

Python Foundations

Start here

Pure-Python problem solving — lists, dicts, loops, functions — on real product features.

Tempo Catalog Warmup

beginner·1 hr

You're an analyst at Tempo, a music streaming startup. The label pitch is Friday, and the team needs the prototype catalog sanity-checked before it ships.

Python ProgrammingLists and DictionariesLoops and Conditionals+1 more
with Sarah·5 steps·300 XP

Tally Statement Categorizer

beginner·2 hrs

Tally’s first monthly statement ships Friday: users connect their bank, Tally pulls transactions, and tells them, “You spent ₹X this month, mostly on Y.” The feature depends on the categoriser.

Python ProgrammingLists, Dicts, Sets, TuplesString Cleaning+2 more
with Karan·6 steps·400 XP
Module 2

pandas & NumPy

Three independent scenarios, beginner to advanced — load, filter, group, join.

CityBikes Ride Log

beginner·2 hrs

You just joined CityBikes, a city bike-share network. Your manager drops last month's ride log on your desk — 24 rides, six columns. Before Monday's ops review she needs the basics: how big is the data, who rides longer trips, and what per-minute pricing would look like.

Data Analysis in Pythonpandas DataFramesFiltering & Selection+1 more
with Devon·5 steps·260 XP

FitPulse Session Cleanup

intermediate·3 hrs

You're a product analyst at FitPulse, a fitness-tracker app. The workouts table just landed as a CSV export — and it's messy: blank values where sessions didn't sync, the same activity spelled four different ways. Clean it first, then find which activities users actually stick with.

Data Cleaning in pandasHandling Missing DataGrouping & Aggregation+1 more
with Lena·6 steps·410 XP

Marketly Revenue Report

advanced·3 hrs

You're a senior analyst at Marketly, an online marketplace. Leadership wants the monthly revenue report — but the data lives in three separate tables: orders, customers, products. Some orders have no status; some point at products that aren't in the catalog. Clean it, join it, and deliver the numbers.

Joining Datasets in pandasData CleaningGrouping & Aggregation+2 more
with Marcus·7 steps·540 XP
Module 3

Data Cleaning & Reporting

One company, two acts — clean an untrustworthy export, then ship the quarterly report.

Wanderstay Booking Log Cleanup

intermediate·2 hrs

You just joined Wanderstay, a boutique hotel group with properties in Lisbon, Barcelona, Kyoto and Cape Town. The front-desk system's booking export is a mess — stray spaces in the headers, missing cities, a double export that duplicated rows, room types typed six different ways, and rates stored as text. Nadia in revenue ops won't touch it until it's clean.

Data Cleaning in pandasString OperationsHandling Duplicates & Missing Data+1 more
with Nadia·6 steps·340 XP

Wanderstay Revenue Report

advanced·3 hrs

The Wanderstay booking log is finally clean — you cleaned it. Now Nadia wants the quarterly report: when guests check in, how long they stay, and a month-by-room-type revenue table she can drop straight into the leadership deck.

Dates & Times in pandasPivot TablesReshaping Data+1 more
with Nadia·7 steps·410 XP
Module 4

Data Visualization

Matplotlib and Seaborn on real investigations — from a funnel dig to a four-panel ops exhibit.

The Conversion Drop

beginner·4 hrs

You’re an analyst at Tempo. Trial-to-paid conversion is down 12% in four weeks. Marketing blames the pricing page, engineering blames the email flow, and the PM needs answers in two days.

Python for Data AnalysisData Manipulation in PythonData Visualization+3 more
with Sarah·11 steps·640 XP
New

More scenarios

Fresh from the catalog.

Zesto: The Fee Engine

intermediate·2 hrs

Build the pricing engine a food-delivery app actually runs on.

JS FundamentalsConditionalsArrays+2 more
with Priya Nair·7 steps·165 XP

Zesto Late Orders

intermediate·3 hrs

One-star reviews are piling up at Zesto and every single one says 'late'. Priya, the City Operations Lead, wants to know: late *where*, late *when*, and late *why* — and she wants to see it, not read it. One month of orders, six charts, one answer.

SeabornDistribution AnalysisData Visualization+1 more
with Priya·8 steps·400 XP

Zesto Ops Review

advanced·4 hrs

Your Old City investigation was right — twenty more riders shipped March 1, and the numbers came back to earth. Now Priya's giving you one slide in Thursday's ops review: a four-panel exhibit that walks leadership from symptom to cause to fix to what's next.

Multi-panel FiguresMatplotlib CustomizationSeaborn+1 more
with Priya·7 steps·420 XP