Content overview

Programming overview

Quarter 1:

• Week 1:

  • Theory on computers and environments in book

  • Theory on files and folder

  • Software installation from Learn-Programming book

  • PA01: checking Python in notebook, basic dictionaries

  • WS1.1: create conda environment, navigate to working directory

• Week 2:

  • PA02: basic matrix operations using numpy; linear algebra/regression; normal distribution (with and without data); report using markdown

  • In class activity: import using numpy.loadtext

• Week 3:

  • PA03 README: Create github account, report username, download/upload/check workflow GitHub

  • PA03 notebook: numpy.genfromtext, data cleaning, Visual Studio Live Share

  • GA3: dictonaries

• Week 4:

  • Theory on git

  • Install git

  • Git workflow clone, push pull

  • PA04: commit table with git

• Week 5:

  • Git branching, merging, forking, conflicts

  • PA05: Asserts, list comprehension, plt.bar, export figure to markdown report

  • PA05: merging, branching, pull requests

  • PA05: forking, pull requests

• Week 6:

  • Theory on errors

  • PA06: run python scripts

  • PA06: find bugs in python script using Python traceback

  • PA06: plt.matshow, filling a matrix, range vs arange

• Week 7

  • Theory on OOP

  • PA07: OOP

  • Ws1.7: scipy.stats

• Week 8

  • Theory on SymPy

  • PA08: create new conda environment

  • PA08: SymPy

Quarter 2

• Week 1

  • PA2.1: classes and mesh creation

• Week 2

  • PA2.2: scipy.parse matrices

• Week 3

  • PA2.3: iterables range, enumerate, zip, module operator, stem

• Week 4

  • Gurobi installation

  • PA2.4 README: environments yml

  • PA2.4A: Gurobi installation check

  • PA2.4B: numpy axes, statsmodels

• Week 5

  • PA2.5: Pandas

• Week 6

  • PA2.6: random number generator, data splitting

• Week 7

  • PA2.7: Datetime, pandas

• Week 8

  • Survey