On this page I will be posting and updating lecture materials, exercises and materials for the current year brushup.

Shell (Command Line Interfaces)

Have a look at the shell exercises here.

The repo contains 3 folders corresponding to different assignments, one handout “handout_bash.html”, and one “.gitignore” file (it makes git to omit several files from consideration at all). In the handout you can find a lot of information not only about Bourne again Shell, but also about Git and GitHub, and Servers.

Recommended literature: “Classic Shell Scripting” by Arnold Robbins and Nelson H. F. Beebe.

Python materials

Lectures

Class 1: Introduction, Variables, Basic Data Types, Expressions, Comparisons
Class 2: Functions, Catching Exceptions; Conditionals and Raising Exceptions
Class 3: Sequence Types, Loops, List Comprehensions, Dictionaries, Set Types
Class 4: Handling Parameters: zip, itertools, Handling Functions: map, lambda; Object Oriented Programming in Python; Script: Car Example
Class 5: OOP (cont’d) and Modules; JSON, numpy, pandas

Exercises

[0] Hello World; [1] Functions; [2] Conditionals; [3] Loops; [4] Dictionaries; [5] Classes; [6] JSON.

Extra materials on Pandas: Intro; Grouping; Project. I recommend you to complete these assignments since it will help you to get comfortable with Pandas. There are also some notebooks with tutorial materials for pandas. I will do a coding review for these assignments upon requests.

R materials

All the main materials are incorporated into a GitHub Classroom assignment: “R: Tutorial”.

You can find the in-class handouts there together with some practical exercises. For more information, please read the README file in the repo.

In the corresponding repository, you can also find Rextra folder which contains some extra materials on linear models in R.

Recommended literature: “R in a Nutshell” (2nd edition) by Joseph Adler; “R for Data Science” by Hadley Wickam; “R for everyone” by Jared P. Lander.