Introduction to scientific computing (short course, 7-18 April 2014)

This course provides an introduction to scientific computing using the Python programming language. The course covers basic data types, data structures, control flow statements, and commonly used functions from the Python standard library. We will also touch on popular third party libraries that provide facilities for efficient mathematical and statistical function, data visualization and plotting, and domain specific tasks (e.g. bioinformatics, image processing). In addition to serving as an introduction to scientific programming, this course will discuss guidelines and tools that help investigators to adhere to principles of reproducible research when carrying out computational and statistical analyses. Registration required.

Pre-requisites:Introduction to Unix” (or equivalent experience). Later sessions will build from topics covered in the earlier ones — by registering for this course, you are agreeing to attend all six sessions (note dates and times below). REGISTER HERE.

Instructor: Paul M. Magwene, Department of Biology and Center for Systems Biology
“An Introduction to Scientific Computing” is co-sponsored by IGSP, Duke Center for Systems Biology & Duke Research Computing

Course schedule

7 April
An Introduction to Scientific Computing – Unit 1
Python computing environments; data types; data structures; control flow statements; effectively using documentation resources. Bostock 023, 4:00 – 5:30 pm.

9 April
An Introduction to Scientific Computing – Unit 2
Writing functions; organizing code into libraries; writing Python scripts that can be called from the command line; tour of scientific libraries I. Bostock 023, 4:00 – 5:30 pm.

11 April
An Introduction to Scientific Computing – Unit 3
Reproducible computational research; IPython notebooks; writing test functions; refactoring code; tour of scientific libraries II. Bostock 023, 4:00 – 5:30 pm.

14 April
An Introduction to Scientific Computing – Unit 4
Data wrangling and basic statistics; efficiently filtering, subsetting,and restructuring data; missing data; data transformations. Bostock 023, 4:00 – 5:30 pm.

16 April
An Introduction to Scientific Computing – Unit 5
Data visualization; generating complex figures in Matplotlib; clustering methods; visualizing and analyzing biological networks. Bostock 023, 4:00 – 5:30 pm.

18 April
An Introduction to Scientific Computing – Unit 6
Building a bioinformatics pipeline; introduction to Biopython; querying local and remote data sources; using Python as a “glue language” to build up complex bioinformatic analyses. Bostock 023, 4:00 – 5:30 pm.


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