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If you like making a boring, repeated task disappear with a script that runs while you sleep, this role is for you.
Writes scripts that do repetitive work automatically: files, reports, data pulls and web pages. Python is the most common language for automating boring work, with libraries for almost every task.
Take the lessons top to bottom. Each one opens with code you run, and ends with graded exercises that check your answer.
16 lessons every path shares. Skip the ones you already know.
Write your first Python program and print to the screen.
Numbers, strings, booleans — the building blocks.
Arithmetic, powers, the math module — Python as a calculator.
Slicing, methods, and f-strings — text manipulation done right.
Make decisions and repeat yourself — but only on purpose.
Loop until a condition fails. Bail out or skip with break/continue.
Collections of things, and how to walk through them.
Carve up lists and strings with [start:stop:step].
Two more collections: tuples are fixed, sets are unique.
Key → value pairs. The most important data structure in Python.
Empty things are False. `is` vs `==`. The infamous None.
The Pythonic ways to iterate — count, pair, sequence.
Bundle up logic so you can reuse it.
Lists of dicts, dicts of lists — modelling real-world data.
Filter and transform in one line — the Pythonic way.
Catch what could go wrong, recover gracefully.
13 lessons specific to this role.
How Python finds code. import, from, as.
Parse and produce JSON — the universal data format.
Pattern matching for text — find, extract, validate.
Today, deltas, parsing, formatting — without losing your mind.
Read and write 'files' that live in memory — perfect for browser Python.
Read and write text and CSV using io.StringIO and the csv module — the core of every data-cleaning script.
Parse and build JSON with the json module and navigate nested API-style data confidently.
Pull emails, phone numbers and dates out of messy text with the re module.
Parse an HTML page with BeautifulSoup and extract headings, links and table rows.
Use @dataclass for clean record types with defaults and small helper methods.
Combine CSV parsing, cleaning, dataclasses and aggregation into one small end-to-end pipeline.
Turn a Python script into a proper command-line tool with args, flags, and --help.
Beyond print() — structured logs, levels, handlers, and what real backends actually emit.
Projects and workspace templates that fit this role. Publish the result to your portfolio page.
Finishing this path gives you the Python part of the job, with graded exercises and projects you can show. It is not, by itself, a qualification for the role.