Learning Python in First Year: A Realistic Plan

Every branch now generates data that a student could analyse, and most students cannot, because they never learned the small amount of programming that would let them. First year is the easiest time to fix this, because the academic load is lighter than it will be again.

What you are aiming for

Not software engineering. The target is narrow and achievable: read a file of measurements, clean it, calculate something, plot it, and explain what the plot shows. That is perhaps thirty hours of work spread across a year, and it is genuinely useful in every branch.

A sequence that works

1. Weeks 1 to 4. Variables, types, lists, loops, conditionals, functions. Nothing else.
2. Weeks 5 to 8. Reading and writing files, especially CSV. This is where it becomes useful.
3. Weeks 9 to 14. One data library for tables and one for plotting. Learn them on real data.
4. Rest of the year. Apply it to your own laboratory measurements. Nothing else teaches as fast.

The mistake to avoid

Do not spend the year watching tutorials. The gap between following along and writing code from a blank file is the entire skill, and only the second one closes it. A rule that works: for every hour of instruction, spend two hours writing code without anything to copy from.

Using your own laboratory data

This is the part that separates students who retain the skill from those who do not. You already record measurements every week. Plotting your own strength of materials or thermal results, and comparing them against the theoretical value, teaches programming and the subject at the same time.