Module 1 · Python Foundations
Tempo Catalog Warmup
beginner·1 hr
Sanity-check the prototype catalog before the Friday label pitch.
Python ProgrammingLists and DictionariesLoops and Conditionals
with SarahPreviewIndustry-grade projects for your curriculum. Deployed in days.
Give your students real, messy, work-based project experience — autograded, AI-mentored, and zero authoring effort for your program.
No authoring effort100% in-browserAutograded & Mentored
Cohort overview
Batch of 420% on trackCompletion by module
M1
M2
M3
M4
M5
Rohan MehtaModule 3 · Step 41,480 XP
Sana IqbalModule 2 · Capstone1,120 XP
Dev Sharmastuck 3 daysModule 2 · Step 1460 XP
Ananya completed Exercise 3 · just now
The product
What your students experience
Four moments from a real scenario — straight from the product your students use.
Workplace scenarios
A brief from a named manager, messy data, and a deliverable someone is waiting on.
AI mentor with context
Sees their code, their last error, their hints — and nudges instead of solving.
Autograded, step by step
Every step is checked as they work; the final notebook is scored against a rubric.
Profile, portfolio & certificate
Skill mastery, completed projects, and a shareable certificate at the end.
WanderstayBoutique hotel groupScenario
NadiaRevenue Ops Lead
The front-desk booking export is a mess — stray spaces in the headers, duplicated rows, room types typed six different ways. I need it clean before the quarterly review.
1Fix the messy column headers
2Drop the duplicated rows
3Standardise the room types
4Convert rates to real numbers
Deliverable: a booking table Nadia can trust — someone is waiting on it.
Workplace scenarios
A brief from a named manager, messy data, and a deliverable someone is waiting on.
WanderstayBoutique hotel groupScenarioNadiaRevenue Ops Lead
The front-desk booking export is a mess — stray spaces in the headers, duplicated rows, room types typed six different ways. I need it clean before the quarterly review.
1Fix the messy column headers
2Drop the duplicated rows
3Standardise the room types
4Convert rates to real numbers
Deliverable: a booking table Nadia can trust — someone is waiting on it.
AI mentor with context
Sees their code, their last error, their hints — and nudges instead of solving.
Ask Nadia
Revenue Ops Lead · Wanderstay
Currently on: Step 3. Standardise the room types
sees:your codeyour last errorhints used
Nadia
Hey Ananya, I'm Nadia — Revenue Ops Lead here at Wanderstay. I've got your notebook open. What's tripping you up?
My replace isn't fixing the "DLX" rows — the check still fails.
Nadia
You're close. Print
df["room_type"].unique() after your replace — two of the six spellings carry trailing spaces, so an exact match skips them. Which string method would tidy those up before you map?Autograded, step by step
Every step is checked as they work; the final notebook is scored against a rubric.
df["room_type"] = (
df["room_type"].str.strip().str.lower()
.map(room_map)
)
room_type has 3 clean values
rate_eur is float64
no duplicate rows remain
Step passed · +40 XP
Final review0/100
- + Clean, readable cleaning pipeline
- + Duplicates handled before any aggregation
“Solid work — your future self will thank you for those tidy columns.” — Nadia
Profile, portfolio & certificate
Skill mastery, completed projects, and a shareable certificate at the end.
AR
Ananya Rao
Data Science · 1,240 XP
Data Cleaning in pandasMASTERED
Grouping & Aggregation4/6 drills
Joining Datasets3/5 drills
TallyCompleted
CityBikesCompletedWanderstayCompletedCertificate of Completion
Buildly Data Science · shareable link + PDF
The two-layer model
Skills applied, not just taught
Your syllabus teaches pandas. We make them use pandas — because a manager needs an answer by Friday.
1Drill itmaps to your syllabus
Skill drill
Grouping & Aggregation · drill 5 of 6
df.groupby("room_type")["rate"].sum()
5/6
One more drill to MASTERED
groupbysame skill,
real stakes
real stakes
2Use it at workthe scenario
Wanderstay · Step 5 of 6
Revenue by room type, by month
Nadia: I need the month-by-room-type table for the leadership deck — can you have it ready by Friday?
Someone is waiting on the answer — that's the difference.
Every scenario runs the same arc
01
Brief
A manager explains what they need and why.
02
Explore messy data
Real exports — duplicates, typos, missing values.
03
Staged steps
Each step mapped to skills, checked as they go.
04
Capstone deliverable
Scored against a rubric, out of 100.
A portfolio of companies, not textbook chapters
Every scenario lives inside a company students get to know — with a real brand and a named team.
Tempo
Music streaming
Tally
Consumer fintech
CityBikes
Bike share
FitPulse
Fitness tracking
Marketly
E-commerce
Wanderstay
Boutique hotels
Who supports 200 students at once?
Help that knows where they're stuck
Every scenario ships with an AI mentor that has the full picture — so your instructors aren't the only path out of a rut.
Ask Nadia
Revenue Ops Lead · Wanderstay
Currently on: Step 3. Standardise the room types
sees:your codeyour last errorhints used
Nadia
Hey Ananya, I'm Nadia — Revenue Ops Lead here at Wanderstay. I've got your notebook open. What's tripping you up?
My replace isn't fixing the "DLX" rows — the check still fails.
Nadia
You're close. Print
df["room_type"].unique() after your replace — two of the six spellings carry trailing spaces, so an exact match skips them. Which string method would tidy those up before you map?Full context, not a blank chatbot
The mentor sees the brief, the current step, the student’s actual code and outputs, their last failed check, and which hints they’ve used.
Guides, doesn’t solve
It knows the reference solution and is explicitly forbidden from pasting it. Students still do the work.
In the scenario’s voice
Each company has its own persona with a face. Students ask their team lead, not a generic bot.
Always available
No instructor bottleneck at 2am. Every student gets unstuck the moment they’re stuck.
Your students already use AI. Ours teaches your syllabus.
A generic chatbot
- Dumps the full solution — assignment done, nothing learned
- Suggests lambdas and list comprehensions in week one
- Has no idea which step they’re on or what just failed
BUILT IN
The scenario's mentor
- Nudges toward the answer — never pastes it
- Level-locked to your module: for-loops in week one, nothing ahead
- Sees the step, their code, and the check that just failed
Off the shelf
The catalog, live today
Every card is a running project your students could start this afternoon.
with SarahPreviewModule 1 · Python Foundations
Tally Statement Categorizer
beginner·2 hrs
Build the categoriser behind Tally’s first monthly spending statement.
Python ProgrammingLists, Dicts, Sets, TuplesString Cleaning
with KaranPreviewModule 2 · pandas & NumPy
CityBikes Ride Log
beginner·2 hrs
Answer the ops manager’s first questions about a month of ride logs.
Data Analysis in Pythonpandas DataFramesFiltering & Selection
with DevonPreviewModule 2 · pandas & NumPy
FitPulse Session Cleanup
intermediate·3 hrs
Clean a messy workout export, then find which activities users stick with.
Data Cleaning in pandasHandling Missing DataGrouping & Aggregation
with LenaPreviewModule 2 · pandas & NumPy
Marketly Revenue Report
advanced·3 hrs
Clean and join three tables into the revenue numbers leadership asked for.
Joining Datasets in pandasData CleaningGrouping & Aggregation
with MarcusPreviewModule 3 · Data Cleaning & Reporting
Wanderstay Booking Log Cleanup
intermediate·2 hrs
Clean the front-desk booking export until every column can be trusted.
Data Cleaning in pandasString OperationsHandling Duplicates & Missing Data
with NadiaPreviewModule 3 · Data Cleaning & ReportingCapstone
Wanderstay Revenue Report
advanced·3 hrs
Turn the clean booking log into a month-by-room-type report for the leadership deck.
Dates & Times in pandasPivot TablesReshaping Data
with NadiaPreviewModule 4 · Data VisualizationCapstone
The Conversion Drop
beginner·4 hrs
Trial-to-paid conversion is down 12% in four weeks — dig through the funnel and tell the PM why.
Python for Data AnalysisData Manipulation in PythonData Visualization
with SarahPreviewWhite-glove service
Built from your curriculum
Bring a syllabus, a domain, or a dataset — we turn it into a fully-graded scenario your students run inside your course.
You bring
- Your syllabus
- A domain or brief
- A dataset (optional)
That’s it — no authoring, no QA, no infra.
We author & QA
- A branded scenario universe
- Staged exercises with test cases
- Mentor hints tuned per exercise
- A capstone with rubric
Built and hosted on our infrastructure.
Your students get
- Projects slotted into your modules
- AI mentor + autograding, day one
- Instructor analytics for your team
Your syllabus stays the spine — we plug into it.
Your course, our projects
The platform organizes drills and scenarios into your module structure — live for our pilot partners today.
Module 1
Python basics
Module 2
pandas
Module 3
Your domain
Two sides of the deal
Outcomes for students, visibility for you
What students leave with
- Skill-level progress they can see grow
- A portfolio of completed scenario projects
- A shareable certificate on completion
AR
Ananya Rao
Data Science · 1,240 XP
Data Cleaning in pandasMASTERED
Grouping & Aggregation4/6 drills
Joining Datasets3/5 drills
TallyCompletedCityBikesCompletedWanderstayCompletedCertificate of Completion
Buildly Data Science · shareable link + PDF
What your program sees
- Cohort progress and per-student drill-down
- Struggling-student flags before they drop off
- Leaderboard and completion analytics
Cohort overview
Batch of 420% on trackCompletion by module
M1
M2
M3
M4
M5
Rohan MehtaModule 3 · Step 41,480 XP
Sana IqbalModule 2 · Capstone1,120 XP
Dev Sharmastuck 3 daysModule 2 · Step 1460 XP
Ananya completed Exercise 3 · just now
Tracks
Start with one track, grow into more
Data Science
Live now
Everything you saw above — Python foundations through cleaning, reporting, and visualization, drilled and applied inside six companies.
4 modules8 scenarios80+ skill drills100% in-browser — zero setup
Full-Stack
Live now
Build-a-product projects: students design, build, and ship a working web product on a real GitHub workflow.
PR #12 · feat: task board
AI code review passed · 2 suggestions left inline
AI/ML Engineering
Early accessThe next track, shaped with partner programs. Register interest and influence what goes in it.
How it works
From first call to first cohort
Pick your modules
Before you book
The questions every program asks
What does it cost?
Pricing is per cohort and depends on size and whether you want catalog modules or custom-built scenarios. We cover it in the 30-minute demo call.
How much content is there?
The Data Science track ships four modules with 80+ skill drills across 28 tracked skills, plus 8 workplace scenarios — 50+ staged, autograded steps with 150+ hand-tuned mentor hints — set inside six company universes. Full-Stack adds build-a-product projects on a real GitHub workflow, and we build custom scenarios from your syllabus on request.
Won’t students just paste everything into ChatGPT?
Some will try. That’s why the mentor is built in: it already sits inside their notebook, sees their exact code and failing check, and teaches at your module’s level instead of dumping solutions from three weeks ahead. Getting a nudge in place is easier than round-tripping a chatbot — and it teaches your syllabus, not around it.
Does it fit our existing course structure?
Yes. The platform organizes drills and scenarios into your course’s own module structure — live for our pilot partners today. It’s the same mechanism we use to slot custom scenarios into your syllabus.
What do students need to set up?
For Data Science: a browser. Python and pandas run entirely in-page — no installs, no lab machines. For Full-Stack: a free GitHub account; projects run on a real GitHub workflow.
How much instructor time does it take?
Grading, mentor help, and AI review run automatically. Instructors watch cohort progress on the dashboard and step in where the struggling-student flags point.
Nadia, Karan, Sarah and the rest of the team are ready when your students are.
Give your students work experience,
not more homework.
See a live scenario end to end in a 30-minute demo — brief, mentor, grading, dashboard.
AutogradedAI-mentoredData Science 100% in-browser
Currently in pilot with design-partner bootcamps.