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 SarahPreviewDo the work before you get the job.
Join a company. Get a brief from your manager. Wrangle real, messy data — with an AI mentor beside you and a portfolio and certificate waiting at the end.
Free to startPython runs in your browserNo installs, no setup
LenaProduct Analyst Lead · FitPulse
The workout export is full of junk — can you get it clean by Thursday?
session_cleanup.ipynbStep 2 of 5 · runs in your browser+40 XPIn [2]: df["activity"] = df["activity"].str.strip().str.lower()
| activity | minutes |
|---|---|
| yoga | 42 |
| running | 28 |
| cycling | 65 |
Activity names standardised — no stray spaces, one casingCHECKING
Stuck? Ask Lena
Sees your code and your last error · nudges, never solves
The product
Four moments from your first project
Not slides — this is the actual product you'll be working in.
You get a brief, not an exercise
Nadia at Wanderstay needs the booking data cleaned before the quarterly review. That’s your ticket.
A mentor who sees your screen
It sees your code, your last error, your hints — and nudges you instead of solving it for you.
Graded like a code review
Every step checked as you work; your final notebook scored out of 100 against a rubric.
Proof you can show a recruiter
Skill mastery, completed projects, a public portfolio, a certificate with your name on it.
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.
You get a brief, not an exercise
Nadia at Wanderstay needs the booking data cleaned before the quarterly review. That’s your ticket.
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.
A mentor who sees your screen
It sees your code, your last error, your hints — and nudges you instead of solving it for you.
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?Graded like a code review
Every step checked as you work; your final notebook scored out of 100 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
Proof you can show a recruiter
Skill mastery, completed projects, a public portfolio, a certificate with your name on it.
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
Beyond tutorials
You've done the courses. This is the job.
Tutorials teach you pandas. Here you use pandas — because a manager needs an answer by Friday.
Every course you've finished
- Watch. Pause. Type the same code.
- “Exercise 4.2: GroupBy practice.”
- A certificate nobody asks about in interviews.
YOUR FIRST WEEK HERE
Marcus · Marketly
"Leadership wants revenue numbers from three dirty tables — by Friday."
- A named manager waiting on you.
- Data with duplicates, typos, and missing values.
- A deliverable scored like real work.
1Drill itmaps to what you've learned
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.
When they say "tell me about a project," you'll have a story with a company, a mess, and a result.
Stuck at 2am?
Ask your team lead
Every scenario ships with an AI mentor that has the full picture — and a strict rule against doing your work for you.
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?Knows exactly where you are
It sees the brief, your current step, your actual code and outputs, your last failed check, and the hints you’ve used.
Won’t hand you the answer
It knows the solution and is forbidden from pasting it. You do the work — which is why it holds up in an interview.
In the scenario’s voice
Every company has a persona with a face. You ask your team lead, not a generic bot.
Always on
No office hours, no waiting on a forum reply. You get unstuck the moment you’re stuck.
Pick your first company
Real projects, live today
Every card is a running project you could start this afternoon — free.
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 SarahPreviewThe payoff
What you leave with
Every scenario you finish becomes evidence — visible progress, public projects, and a certificate that names what you can do.
Skill-by-skill mastery
Watch your capabilities grow visually as you complete scenarios.
A real portfolio
Each project has a company, a brief, and a shipped deliverable.
Public resume link
A live portfolio page ready for your resume today.
Shareable certificates
Earn verifiable credentials when you complete a track.
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
Tracks
Pick your path
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
Coming soonBuild-a-product projects: design, build, and ship a working web product on a real GitHub workflow. Sign up and you'll be the first to know when it opens.
PR #12 · feat: task board
AI code review passed · 2 suggestions left inline
AI/ML Engineering
Early accessThe next track, shaped with partner programs. Sign up now and you'll be the first to know when it opens.
How it works
From signup to shipped project
Sign up free
Nadia, Karan, Sarah and the rest of the team are ready when you are.
Your first project takes
an afternoon.
Free to start — no invite code, no card, no setup. Your portfolio starts today.
AutogradedAI-mentoredData Science 100% in-browserFree to start