BlogWhat to expect from this course
2026-05-10 · By Kelvin Amoaba

What to expect from this course

Four weeks. Two live sessions a week. Three mini-projects, one capstone. Here's how it actually runs.

PySprout is short, structured, and a little intense. Four weeks, twelve sessions, three mini-projects, one capstone. Here's the shape of it.

Each week, three meetings

Two are live teaching sessions (around 60-90 minutes). One is a work block + office hours — you write, we hover, you ask questions in real time.

If you miss a live session, the recording goes up the next day. We'd rather you show up; the cohort works better that way. But life happens.

Each session has a small exercise

We give you something to try the same day, while the concept is fresh. It's small — 15 to 30 minutes — and it doesn't get a real grade. Its job is to make sure you actually pressed the keys, not just watched.

Each week has a mini-project (weeks 1–3)

You'll build:

  • Week 1 — a scholarship eligibility checker (variables, conditionals, functions).
  • Week 2 — a CLI scholarship tracker that survives a restart (lists, dicts, files).
  • Week 3 — a research data summary tool (pandas, or an API).

Each project is graded — see the grading rubric — and reviewed by a real person within 24 hours of submission.

Week 4 is the capstone

You pick one of seven project options (or pitch your own), build a polished version, push it to GitHub, write a real README, and present for three to five minutes on demo day.

The capstone is what we want you to show off in scholarship essays.

Communication

  • We answer in the cohort channel within 24 hours on weekdays. No weekends.
  • If something is on fire, email your instructor directly.
  • Peer help counts. Helping someone else understand a concept is the fastest way to lock it in for yourself.

What we're not teaching

To protect the four-week timeline, we're deliberately skipping:

  • Object-oriented programming (in any depth)
  • Decorators, generators, advanced Python features
  • Web frameworks (Django, Flask)
  • Machine learning
  • Databases / SQL
  • Complex git workflows

These are all great, and we'll point you to good resources at the end. They're just not for this cohort.

See you in Week 1.