Special Topics

Summer 2013

CSS 290: Educational Software

This course examines constructionist methods of using computers to help learners. Students will evaluate educational software and then participate in design projects aimed at solving real problems. Students, particularly those from Education and Computing and Software Systems programs, will work together in project teams. No prior programming experience and no prior knowledge of teaching is required. Project work can be used in school or after-school settings by project members who are already teaching. Project work can also be used in informal settings such as homes and museums.

Spring 2013

CSS 390: Scripted Languages

This special topics class will introduce the intermediate and advanced student to a variety of scripting languages, including at least bash, Perl, and Python. These languages are useful tools for a wide range of tasks, including automation of various workflow activities, ad hoc utility creation, rapid prototyping, systems integration, small and medium scale software development, job control, and as utilities for many peripheral software development tasks. We will not only learn how to use these tools to make ourselves more productive, we will also compare and contrast each language's capabilities, strengths, and weaknesses.

Prerequisite: CSS 342

CSS 490: Algorithms in Bioinformatics

Bioinformatics is one of the most exciting fields with many computer science applications along with computational methods. Computer scientists have been involved in this field by developing algorithms and implementing them as tools to help create, analyze and manage biological data. Instead of learning about those computational tools, this course will focus on learning the algorithms that implemented the tools and computational models applied to bioinformatics. Students will learn how those algorithms have been developed to solve a variety of bioinformatics problems: Dynamic programming for DAN/protein sequence alignment; Graph algorithms for delineating dynamics of biological processes, Pattern matching technique to search databases; Combinatorial algorithms for DNA sequence processing; Hidden Markov models for sequence annotation; Statistics for haplotype frequency inference; Clustering and Trees for gene expression analysis, etc. This course also uses Python programming language to implement these algorithms for given problems. Students will conduct group projects which develop bioinformatics tools/platforms with any programming language including Python.

Prerequisite: CSS 343