Concepts

Week 1

• Python install
• VS Code install
• editing a .py Python program and executing it in the shell
• the REPL
• expressions and evaluating them
• mathematical operators and operator precedence
• using brackets ( and ) to override operator precedence
• numeric vs character string data types

Week 2

• variables: str, int, float, bool, inspect using type()
• conversion: str(), int(), float()
• operators: + - * /, **, modulus %
• logical operators < > == >= <= != and or not True False
• operator precedence, using brackets ( ) to save your sanity
• import statements—for example, from math import cos
• print() and formatted output using f-strings
• getting input from the user using input()
• commenting code using #
• reserved keywords in Python (e.g. for, return, etc)
• operations on strings (strings are “objects” with “methods”)
• getting help using help()
• conditionals using if elif else
• the list variable type in Python
• loops using while and for
• range() & zero-based indexing in Python

Week 3

• in Python list variables are pointers
• b = a vs b = a.copy()
• NumPy arrays
• creating arrays
• np.zeros() and np.ones()
• shape of arrays using np.shape()
• multidimensional arrays
• vectorized operations on arrays
• slicing & indexing into arrays
• functions in Python
  • defining a function
  • function header
  • inputs
  • the work
  • outputs
  • variable scope
  • named inputs
  • default values
  • idea of modularity of code

Week 4

• Python dictionaries
• Python list comprehensions
• using the enumerate keyword in for loops
• hexadecimal
• Python functions as variables
• progress bar using tqdm
• file input & output: low-level
  • all files are binary, a series of 8-bit bytes
  • ASCII encoding (and utf-8 more modern version)
  • using hex editor to view a file as hexadecimal bytes
  • Python defaults: 32-bit int, 64-bit float, 8-bit char
  • little-endian vs big-endian byte ordering (sys.byteorder to check)
  • using numpy to read and write binary bytes by specifying dtype
• file input & output: high-level using NumPy & pandas

Week 5

• procedural programming: functions acting on data structures
• object-oriented programming (OOP): classes & objects encapsulate attributes & methods
• hierarchical organization of Classes (and superclasses and subclasses)
• attributes (data)
• methods (functions)
• the __init__() method and the self variable
• inheritance
• overriding inherited methods
• polymorphism
• super().__init__()
• special methods: __str__() and __repr__()
• copying objects
  • shallow copy using the copy module copy.copy()
  • deep copy using copy.deepcopy()
• operator overloading, e.g. __eq()__, __lt__(), __gt__()

Week 6

• matplotlib in Python
• plt.plot() & plt.show()
• dots, lines, colours, shapes
• plt.subplot() vs fig, ax = plt.subplots()
• axis labels, axis limits
• figure title
• legend
• fig.tight_layout()
• fig.savefig()
• graphics file formats
• bitmap vs vector graphics

Week 7

• plaintext documents
• pandoc + markdown
• LaTeX

Week 8

• writing clean code
• sketching a directed graph
• raw data, processed data, summary tables, figures, stats

Week 9 & 10

• time domain vs frequency domain
• Fourier series
• Fourier decomposition
• sampling
• nyquist frequency
• aliasing
• spectrum of a signal (frequency vs magnitude)
• power spectral density (e.g. Welch method)
• decibel scale
• spectrogram
• filtering
• low-pass, high-pass, band-pass, band-stop
• corner / cutoff frequency
• filter roll-off
• derivatives of noisy signals
• quantization & bit depth

Week 10 & 11

• population vs sample & parameter estimation
• role of sample size
• sampling distribution of means
• standard error of the mean
• logic of null hypothesis significance testing
• what is a p-value
• type I and type II errors
• effect size
• statistical power
• confidence intervals
• parametric vs resampling methods