FreeCampus Python

Python Learning Pathways

See how FreeCampus Python courses connect and choose the right starting point.

Start with your current skills

You do not need to complete every Python course. Choose the pathway that supports the work you want to do, and use prerequisites to avoid being asked to guess at skills a course assumes.

TipNew to programming?

Start with Python Foundations. It is the common entry point for every pathway and assumes no programming experience.

Software-development pathway

  1. Python Foundations
  2. Intermediate Python: Applied Problem Solving — curriculum TBD
  3. Advanced Python: Complex Problem Solving — curriculum TBD

Choose this route to apply Python to progressively less guided problems, then solve complex problems with meaningful correctness, performance, and design constraints. While those two applied courses are being designed, complete the full Python Foundations sequence to strengthen files, environments, testing, OOP, and reusable Python patterns.

Scientific-computing pathway

  1. Python Foundations
  2. Scientific Computing with Python

Choose this route to work with arrays, numerical methods, multidimensional scientific data, specialized formats, scientific visualizations, performance, and progressively larger computations. Statistics is not a prerequisite, so you may start here directly after Foundations.

Statistics pathway

  1. Python Foundations
  2. Statistics with Python — curriculum TBD

Choose this route to describe variation, reason about probability and samples, quantify uncertainty, compare groups, examine relationships, and communicate what evidence can and cannot support. Scientific Computing is not a prerequisite, so the two courses may be completed in either order.

Data Science and Machine Learning convergence

Before starting Data Science and Machine Learning with Python, complete both Scientific Computing and Statistics. The two incoming paths are joint prerequisites: one supplies computational scientific practice and the other supplies statistical reasoning.

Solid arrows are prerequisites. The two arrows entering Data Science and Machine Learning mean that both parallel courses are required, with no required order between them.

flowchart LR
  F[Python Foundations]
  I[Intermediate Python:<br/>Applied Problem Solving · TBD]
  A[Advanced Python:<br/>Complex Problem Solving · TBD]
  S[Scientific Computing<br/>with Python]
  T[Statistics with Python<br/>TBD]
  M[Data Science and Machine Learning<br/>with Python]
  F --> I --> A
  F --> S
  F --> T
  S --> M
  T --> M

Readiness guide

If you can already… Consider starting with…
Write small programs with conditions, loops, collections, and functions but have not used files, tests, or projects Continue Python Foundations from Unit 6
Independently parse, model, test, and explain unfamiliar small problems Intermediate Python when its curriculum is published
Compare algorithms and handle state, scale, or performance constraints Advanced Python when its curriculum is published
Use foundational Python and want to investigate numerical or scientific questions Scientific Computing
Use foundational Python and want to reason about samples, variation, and uncertainty Statistics when its curriculum is published
Use NumPy arrays and plots and can explain sampling variability and statistical uncertainty Data Science and Machine Learning
Recognize some syntax but cannot yet explain or debug it confidently Python Foundations

When uncertain, open a prerequisite course and try the unit challenge near your last confident topic. A short review is usually faster than struggling with missing foundations.

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