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Python
20 hours
Product analytics with Python
Monday, first day at a new company. The PM pings you: "Repeat purchases have dropped over the last 3 months, figure out why". In front of you — a year of data and an empty notebook. Over 26 lessons we walk this investigation together: from a first look at the CSV files to a completed A/B test and the report you take to the leadership.
- Pandas in context: metrics, cohorts, funnels, retention and LTV
- A/B testing from noise and p-values to the common pitfalls: peeking, sample ratio mismatch, the survivor trap
- Interactive widgets in the browser: retention matrix, funnel builder, split simulator, peeking animation
26 lessons
Certificate on completion
The course is part of premium — together with the other advanced courses, the AI assistant and certificates.
funnel.py
# conversion by funnel step
funnel = events.groupby("step")
.user_id.nunique()
conv = funnel / funnel.iloc[0]
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What changes after the course
Now
After the course
I open an export in Excel and it freezes at 100k rows
You load a year of data into pandas and compute metrics in a few lines
"Conversion dropped" — but I can't explain why or at which step
You build funnels and retention and show exactly where users are lost
The team argues about a feature by gut feeling, loudest wins
You run an A/B test, check significance and tell signal from noise
I have the numbers but can't turn them into a decision for the PM
You build a clear report with charts and conclusions that gets read in the meeting
Course program
Each lesson is short theory on live examples plus practice in the trainer with auto-checking. Nothing to install: Python runs right in the browser.
About the course: from raw data to a product decision
What a product analyst actually does
Shop-X dataset: a first look
Slices and filters: quick questions for the data
Grouping: the whole picture in one line
Joining tables: revenue by country
Working with dates: what happened in April
Active users: DAU, WAU, MAU
Conversion: from what to what
Average order value: when the mean deceives
ARPU and LTV: money per person
Why cohorts: averages lie
The retention matrix: the flagship table of analytics
LTV cohorts: which channels feed the shop
The purchase funnel: five steps to the money
Segmented funnels: the suspect is found
The funnel over time: the birth date of the gap
The hypothesis: packing up the investigation
Noise: the experiment where nothing changed
P-value: a measure of surprise
Experiment design: how much data does the truth need
A/B test analysis: the verdict
Peeking: how to break a perfect experiment
The report: an investigation on one page
A chart for the decision: one thought per canvas
Case closed: the investigation map and the road ahead
How the learning works
01
Read a lesson with live examples
Theory without filler: every code example can be run and edited right in the browser, with the result shown instantly.
02
Solve tasks in the trainer
You write code and the answer is checked automatically against a set of tests. No local environment setup.
03
Stuck — the AI mentor helps
The built-in assistant points out where the error is and nudges you toward the solution without giving it away.
Who is this course for
Finished Python basics and want to move into analytics
You know the syntax but don't see how a CSV file turns into metrics, charts and business decisions.
A SQL-and-Excel analyst who hit the ceiling
You want to compute cohorts, retention and A/B tests that are painful to do in spreadsheets.
A PM or marketer tired of waiting for an analyst
You want to pull answers from the data yourself instead of filing a ticket and waiting a week for an export.
You'll need Python basics — variables, functions, lists and dicts. If you don't have them yet, start with the free "Python Basics" course, then come back.
A certificate you can verify
Complete all the lessons and get a named certificate of completion. You can add it to your resume or your LinkedIn profile.
Your name and the course title
QR code and verification link
What students say
Marina
Spent six months on Python video courses. Everything made sense in lectures, but sitting down to write code from scratch never worked. Here you write from task one. On harder tasks I'd get stuck, but premium unlocks reference solutions, and I'd compare my janky code to the canonical version line by line. A month in, I wrote a script at work that builds a report from 30 CSV files.
Artem
Bombed a tech interview on a simple dictionary task, brain just shut down under stress. Worked through interview tasks from Yandex and Avito in the trainer, at the next interview I got almost the same problem and had an offer within a week.
Katya
Moved from manual QA to development, and toy tasks about factorials and Fibonacci were driving me crazy. In the trainer it's JSON parsing, CSV processing, API work, exactly what I do at my job, just without deadlines and the fear of breaking prod.
One premium — all courses at once
You're buying not a single course but the whole Python Academy premium: the advanced courses and every trainer feature.
- Access to advanced Python courses
- Unlimited hints from AI Assistant
- Access to tasks from top company interviews
- Access to reference solutions for tasks
- Certificate of successful completion of Python tasks in the trainer
$ 24,90
6-month access
Secure payment by Russian card via YooKassa or by international card via Stripe. Access opens right after payment.
Frequently asked questions
Who is this course for?
People who write basic Python and know basic SQL: lists, dicts, functions and loops plus SELECT with WHERE, GROUP BY and JOIN — that's enough, pandas is explained through familiar SQL analogies. No advanced SQL needed. Analytics, metrics and statistics are explained along the way, from scratch.
What will I have at the end?
A working investigation notebook in which you went from raw CSV to a completed A/B test, an executive summary and a chart for the leadership. The ready-made Jupyter notebook with all the code is downloadable in the final lesson — a case study that fits perfectly on a resume or interview as a junior analyst.
How is this different from a pandas book?
This is not a textbook on the library, it is an investigation with pandas inside. Pandas is woven into every step: load data → ask a question → find the answer. Plus interactive widgets — a retention matrix, a funnel builder, a random-split simulator and a peeking animation — that build intuition you cannot get from formulas.
What do I need to install?
Nothing required — the code and the interactive widgets work right in the browser. If you want to replay the investigation locally, you'll need Python 3.10+ with pandas, numpy and matplotlib.
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