Study Material, Tutorials and Exercises
Tutorial 1: Introduction to Unix
Preliminaries
- Official documentation for the Vera cluster at C3SE can be found here.
- Introduction slides can be found here.
Tutorial
Homework
- HW 1: Unix
- To be submitted on Canvas by January 29th.
Recommended Reading
Tutorial 2: Introduction to Python programming for data
analysis
Tutorial
Python
Intro Tutorial
Homework
Reading
A selection of small chapters and sections that explain concepts we
cover in the course:
- An
Introduction to Programming for Bioscientists: A Python-Based
Primer by Ekmekci et al.
- This PLoS article is a good overview/introduction to the use of
Python for data science.
- Some aspects may seem confusing in the beginning, but Python (like
all languages, programming or otherwise) is best learned through
use. As you work your way through this course, this article can
be used a reference.
- Python for Data
Analysis, 3E by Wes McKinney
- An alternative option for exploring the use of Python in the context
of data analysis & scientific computing
- Much more detailed than the PLoS article above, but less relevant
for bioinformatics - a potentially useful reference
- Intro to
Advanced Python by Bernd Klein
- This website provides resources for some of the more advanced
utility of Python, which can improve your overall coding ability.
- The Intro to
Python Tutorial portion of the same website has more basic
introductory materials, if you like all of your references to be in the
same place.
- Official documentation pages: You do not need to read these in their
entirety, but they can provide helpful information for a variety of the
modules we will be using. Here, I will include a few of the basics:
- Miscellaneous :) There are many good resource on the web. We’ll
point out some when relevant.
Tutorial 3: Version control with Git
Preparation
Do this before you start with the tutorial.
Tutorial
Recommended Reading
Tutorial 4: Sequencing Technologies
Tutorial
Tutorial 5: Introduction to Algorithmic Thinking
Tutorial
Here you can find a comparison
of the time needed by some of the methods you used to work with lists
and Pandas data frames during the tutorial. If you want to try it
yourself, you can download the jupyter notebook from here
Practical info for projects
Practical info for clusters
Chalmers University of Technology