The Python Developer Route
Before you start
You need: Article 02 — you have chosen Python.
Read the scope section below before anything else. If your goal is data science or machine learning, this route is not it, and knowing that now saves you five months.
Time: about 30 minutes to read. Then roughly 20 weeks to walk.
Learning objective
Know which tracks make you a junior Python backend developer, and be clear about where this route stops.
Topics
- What this route leads to, and what it does not
- The route at a glance
- Why this order
- What each stage gives you
- The four tracks you take alongside
- Milestones you can put on a resume
- If data or ML is your real goal
What this route leads to — and what it does not
It leads to: junior Python developer, junior backend developer, automation or QA engineer. You will build web APIs with FastAPI, work with SQL Server and MongoDB, and connect a React frontend.
It does not lead to data science, machine learning or AI engineering.
That is not an oversight. The Sahasra syllabus lists these as explicitly excluded from the Python course:
Advanced Python internals
Machine-learning specialization
Advanced async systems
Distributed backends
Advanced data engineering
Why say this so bluntly? Because "Python" and "data science" are so linked in students' minds that people take a Python course expecting one and get the other. Discovering that in month five is a genuinely bad outcome, and it is avoidable with one paragraph.
If ML is your goal, this route is still a reasonable first step — it gives you the programming foundation that every ML path assumes, and you will write better code than someone who started with notebooks. But you then need statistics, linear algebra, and a different curriculum. See the last section.
The route at a glance
Class counts come from the Sahasra syllabus. A class is 90 minutes.
| # | Track | Classes | What it gives you |
|---|---|---|---|
| 1 | 02 Software Industry Foundation | 8–10 | The vocabulary a team uses |
| 2 | 13 Python & FastAPI | 34–42 | The language, OOP, files, testing, a first API |
| 3 | 06 SQL Server | 30–36 | Relational data and real SQL |
| 4 | 14 MongoDB | 16–20 | Document databases, common in Python work |
| 5 | 09 Web Development | 36–44 | HTML, CSS, JavaScript, TypeScript |
| 6 | 12 React | 28–36 | The frontend |
| — | Alongside from week one: 15 Tools, 16 Git, 17 Debugging, 18 AI | 40–56 | How the work is actually done |
Roughly 195–240 classes. At 13 hours a week that is about 20 weeks — shorter than the .NET route, because Python's own track covers both the language and its API framework.
Why this order
02 first because the vocabulary of teams, requirements and environments applies whatever the language.
13 second and in full. Track 13 is unusual: it takes you from installing Python all the way to a working FastAPI backend in one track. Do not stop at article 08 because "the basics are done" — articles 09 to 11 (testing, databases, FastAPI) are where it becomes employable.
06 SQL before 14 MongoDB. Relational thinking first, then document modelling as a contrast. Doing it the other way round produces someone who models documents like tables, which is the classic mistake — Track 14 Article 05 is explicitly about not doing that.
Both databases, not one. Python roles ask for either, often both. SQL Server also happens to be where this site's worked examples live, so the School schema carries across.
09 before 12 for the same reason as the .NET route: a React component is HTML, CSS and TypeScript with a framework arranging them.
Frontend last so there is a real FastAPI backend to call.
What each stage gives you
After 02 — Software Industry Foundation (1 week)
You can read a work item, ask clarifying questions, and follow a stand-up.
After 13 — Python & FastAPI (5 weeks)
You can write structured Python, model concepts as classes, process files and JSON, write tests with pytest, query a database, and build a validated REST API. Milestone: a FastAPI backend with Pydantic models and correct status codes. This is the biggest single jump in the route.
After 06 — SQL Server (4 weeks)
You can design a normalised schema, write joins and stored procedures, and use transactions. Milestone: the School database.
After 14 — MongoDB (2 weeks)
You can model documents by access pattern rather than by relationship, query arrays and embedded documents, and index sensibly. Milestone: the same data modelled both ways, with a written comparison — which is an unusually good interview talking point.
After 09 — Web Development (4 weeks)
You can build responsive semantic pages and call your API with fetch. Milestone: a working frontend, no framework.
After 12 — React (4 weeks)
You can build a routed React frontend against your FastAPI backend. Milestone: the full stack, connected.
The four tracks you take alongside
Same rule as the .NET route: these run with the programme, not after it.
| Track | Start it | Note for Python |
|---|---|---|
| 16 Git | Week 1 | Six months of commits is evidence nothing else replaces |
| 15 Developer Tools | Week 1, then as needed | Focus on VS Code, SSMS and Postman. The Visual Studio article is for the .NET route |
| 17 Debugging | Your first bug | The VS Code debugger, not Visual Studio |
| 18 AI-Assisted | Week 1 for Article 03; full track around month 4 | — |
One Python-specific warning from Track 13 Article 01: the single most common VS Code problem is having the wrong interpreter selected. The package is installed; VS Code is just looking somewhere else. Learn Ctrl+Shift+P → Python: Select Interpreter in week one.
Milestones you can put on a resume
| Month | You have |
|---|---|
| 1 | Python programs with tests, and a virtual environment you understand |
| 2 | A validated FastAPI backend with correct status codes |
| 3 | A designed SQL Server database, and the same data modelled in MongoDB |
| 4 | The API backed by a real database, with transactions |
| 5 | A React frontend connected to your API |
| 6 | An AI feature, a deployment, and a walkthrough you can give |
The month-3 milestone is worth emphasising. Very few fresher candidates can discuss why they chose an embedded document over a foreign key. Doing both and writing the comparison is a twenty-minute exercise that gives you a distinctive answer.
If data or ML is your real goal
Be honest with yourself now rather than in month five.
This route is a good first step if:
- You want to be employable while you study the mathematics
- You want to write production-quality Python rather than notebook Python
- You are open to a backend role first and moving across later — which is a common and realistic path
This route is the wrong first step if:
- You want an ML role directly out of college
- You are not prepared to study statistics and linear algebra separately
- You expected pandas, NumPy and scikit-learn to be in the syllabus. They are not
The realistic path from here to ML is: take this route, get a Python backend job, keep studying mathematics and ML on your own, then move internally or apply across in two or three years with real engineering experience behind you. That route works and is common. Applying for ML roles as a fresher with no mathematics does not.
Common mistakes
- Expecting data science, and discovering the scope in month five
- Stopping Track 13 after the language basics, before testing and FastAPI
- Skipping SQL because "Python uses MongoDB"
- Modelling MongoDB documents the way you would model tables
- Leaving Git until the end
- Installing packages globally instead of using a virtual environment
- Ignoring the interpreter-selection problem and fighting imports for a week
- Building the frontend against mock data instead of your own API
Practice
- Reread the excluded-topics list. Write one sentence on whether this route matches your goal.
- Write your 20-week plan with the six main tracks and start dates.
- Add Git in week one.
- Write the six milestones with target dates.
- Plan the month-3 comparison: the same School data, relational and document, with a written trade-off.
- Check your local listings for Python fresher roles. Note how many ask for a second skill — testing, automation, data — and which.
- If ML is your goal, write down what you will study alongside, and when.
You can now
- Name the six main tracks in order, and why that order
- State plainly what this route does not lead to
- Say why both SQL Server and MongoDB are in the route
- Explain why Track 13 must be finished, not stopped at the basics
- List the six milestones and what each proves
- Decide honestly whether this route matches your goal
Review questions
- What does this route explicitly not qualify you for, and where is that stated in the syllabus?
- Why learn SQL before MongoDB rather than the other way round?
- What makes the month-3 milestone a strong interview answer?
- If ML is the long-term goal, what is the realistic path from here?
Next: Week-by-week schedule