If you're uncomfortable with the following concepts, you might want to brush up using a basic resource like a textbook.

• Vectors and matrices
• Matrix multiplication
• Inner product
• P-norm
• Linear independence
• Transpose
• Invertibility

Below we'll outline some resources for doing this kind of math in Python.

Python has a wonderful suite of libraries for doing scientific computing (i.e. vectorized linear algebra). Rather than using slow for-loops, you should always vectorize your operations using numpy and scipy. If you like videos, try watching this one and following along with their GitHub code.

Action Item:

1. Some excellent tutorials can be found from scipy.org, Python Course, and UCSB. For those of you familiar with Matlab, here's a cheatsheet for translating your Matlab knowledge into numpy.

2. Do the Neophyte, Novice, and Apprentice levels from the numpy 100 exercises page.

In addition to understanding logic and control statements, algorithms and data structures are important foundational knowledge for programming. There are a lot of 'dumb' ways to do things. These are the 'smart' ways to do them that will save you a lot of time on the job.

There's a great interactive Python-based tutorial available online. From Basic Data Structures, understand:

• Stacks
• Queues
• Deques
• Lists

From Sorting and Searching, understand:

• Hash Tables
• The Bubble Sort
• The Selection Sort
• The Insertion Sort
• The Shell Sort
• The Merge Sort
• The Quick Sort

Action Item: To test your knowledge, make sure you can answer the first five exercises for Basic Data Structures and Sorting and Searching.

Again, consider doing these exercises with a timer because they will really help you.