The black box is waiting
First lesson: The second signal: Kotomarket's black box
Interactive course · Data Analysis
Investigate the data.
Become an analyst.
Python, pandas, product metrics, cohorts and statistics — on the Kotomarket archive. Code and charts run right in the browser: nothing to install.
- Python and statistics from zero
- 11 investigations
- an investigator's licence at the end
# where exactly did revenue sag? orders.groupby("platform")["revenue"].sum()
android 1 412 300 ios 3 980 100 web 2 106 400
The broken checkout
Find where exactly the funnel tore — and prove it with numbers.
Outcome
What you will be able to do
Not a list of technologies — the working moves of an analyst.
Find the cause of a metric drop
You will break the change down by segment, localise the moment of failure and cut off coincidences.
Test hypotheses with statistics
Confidence intervals, p-value, power and MDE — through simulation, not textbook formulas.
Compute the product economics
CAC, LTV, margin, payback and cohort retention — up to "this is where we lose money".
Deliver the conclusion
Charts in code, an executive dashboard and a final report a decision can be made on.
How it works
A course you work through with your hands
Code — right on the page
Python, pandas and matplotlib execute in the browser (Pyodide). No installs, no cloud accounts.
11 cases instead of exercises
Every case has the same shape: a metric fell — evidence, hypotheses, verdict. You run the analysis.
Python and stats from zero
The course continues the SQL one, but assumes no prior Python or statistics.
Kotomarket black box · Vault-9 · 2184
Eleven cases from one store
Vault-9 has opened the black box of a dead store. Your mentor L. reached the last entry of her journal and vanished. You are the investigating analyst: eleven of her cases await, and in each one a metric fell for its own reason.
- 04the lying average order value
- 05one-rouble subscribers
- 08review manipulation
- 09delayed cancellations
- 10growth that is too smooth
The route
Four acts and a finale
The program is built like an investigation: first read the data, then obtain it, then find the cause, then prove it.
The language of metrics
What an analyst does and how a product is measured
The tools
Python, pandas and the first look at data
The craft
Cleaning, joins, the method of inquiry
The proof
Segments, cohorts, statistics, dashboards
The language of metrics
2 chapters · 11 lessons · 2 h 46 minWhat an analyst does and how a product is measured
"The Second Signal"
You will see what an analyst actually does and run your first Python cell right in the browser.
Anatomy of Metrics
You will learn to compute DAU/WAU/MAU, funnels and retention — and read human behaviour behind them.
Checkpoint · end of act I
You already read metrics
- you compute DAU, WAU and MAU and know the difference
- you build a funnel and see which step leaks
- you tell retention from repeat purchases
- you run Python in the browser with zero installs
What comes next
The first 11 lessons are open with no payment. Beyond them the investigation itself begins: pandas, cleaning, joins, cohorts, statistics, 11 cases and the capstone.
The tools
3 chapters · 18 lessons · 5 h 16 minPython, pandas and the first look at data
Python for Analysts: A Bear in the Archive I
You will take exactly as much Python as you need to tame pandas: lists, dicts, loops, functions.
pandas: A Bear in the Archive II
You will master the DataFrame: boolean masks, sorting and groupby on slices of the Kotomarket archive.
First Look at the Data: inspection, statistics, charts
You will survey an unfamiliar dataset: descriptive stats, distributions and your first matplotlib charts.
Evidence
- Web: 5,600 → 5,656, +1%
- iOS: 10,000 → 9,800, −2%
- Android: 26,500 → 14,044, −47%
Your task. The overall metric fell 30%, yet nowhere is there a 30% drop. Find where the hole sits and prove it with a breakdown.
The craft
3 chapters · 19 lessons · 5 h 38 minCleaning, joins, the method of inquiry
Data Cleaning: Archive Hygiene
You will bring a dirty archive into working shape: gaps, duplicates, types, dates and a 9,999,999 ₽ order.
Joins and Pivots: Stitching the Archive
You will stitch scattered tables with merge, catch fan-out and build a pivot report in five lines.
The Method of Inquiry: hypotheses and checks
You will get the method: a hypothesis funnel, the "check the tracking first" reflex, effect size vs noise.
Evidence
- It must be counted by order-date cohort
- Orders from May 1–14 cancelled at 5.2%
- The promo hypothesis is not supported
Your task. A cancellation arrives weeks after the order. The calendar metric shows nothing — the cohort metric shows it at once.
The proof
4 chapters · 30 lessons · 8 h 56 minSegments, cohorts, statistics, dashboards
Segments and Seasonality
You will find the segment dragging the metric down and tell seasonality from a real incident.
Cohorts and Product Economics
You will build cohort matrices and compute the economics: CAC, LTV, margin and payback.
Statistics and A/B Tests
You will tell signal from noise: confidence intervals, p-value, power and MDE — by simulation, not formulas.
Charts and Dashboards: The Console
You will draw metrics in code, assemble a four-chart executive dashboard and crack three fraud cases.
Portfolio
What stays with you after the course
Every artefact is one you build yourself, in code, on real archive data.
A funnel and its leak
Steps, conversions and the step where the product loses people.
A cohort matrix
Retention by cohort lifetime month and an LTV calculation on top.
An A/B-test decision
An effect interval, a p-value and a clear verdict instead of "looks up".
An investigation dashboard
Four charts on one matplotlib canvas — plus the post-mortem report.
Finale
The post-mortem
All of L.'s cases fold into a single chronicle — and you write the verdict on it.
The Post-Mortem Report: capstone
You will write the post-mortem on the black box: the GMV break, the retention collapse and a cash-gap forecast.
The first case is waiting in the prologue
The prologue and the first chapter are open with no payment — 11 lessons, your first Python cell and your first investigation.
Start for free