The University of St. Thomas

Graduate Programs in Software

Topics Courses

Topics Courses

GPS Topics Course Listings and Descriptions

Topics courses are the newest courses that are available for GPS students. Each Topics course will count as an elective in all programs.

Topics: Big Data
Topics: Data Analytics and Visualization 
Topics: Global Software Development 

   SEIS 785-XX Topics: Global Software Development {Saturdays, Fall semester 2013}
 
The objective of this class is to give students practical experience developing software in a global environment. The course will be taught in conjunction with a paired course at a German University (Hochschule Trier, Trier University of Applied Sciences).

Teams of 3-6 students (with a mix of German and US students on each team) will be formed to develop a software product (using the programming skill sets of the student teams). The course will revolve around a semester-long project roughly divided between the first half (project design) and the second half (project implementation). During the first half of the semester all teams will design a project, and during the second half, all teams will implement a project designed by a different team.

Activities thorough the semester will include:

  • Design portion
    • Brainstorming
    • System Definition (using an IEEE 830 Software System Design template)
    • System Design Review
    • Full Requirements Specification
  • Implementation portion
    • Ongoing meetings with implementation team (and design team as needed)
    • Work plan, role assignments, program management (including minutes of meetings, etc.)
    • Coding, testing, and verification/validation

Prerequisites: SEIS610 and SEIS630
Instructor: Dr. Bonnie Holub
Fall semester, 2013
SEIS 785 Section 01, CRN 42616
Meets most Saturdays in fall semester 2013 from 8:30 a.m. to 12:15 p.m. (September 7 through December 14th. No class meeting on November 30th.)
3 credits

Please contact your advisor Doug Stubeda at 651-962-5503 or djstubeda@stthomas.edu if you have any questions or need assistance with registering for this course.
 

   SEIS 785-XX Topics: Big Data
 
This course will cover emerging technologies for handling very large datasets, with special focus on semi-structured and unstructured data.  Most of these technologies for big data complement the traditional relational database/SQL combination.  The course material will be wide-ranging, covering operating system, architecture, security, big data structure and storage, big data management, and applications.  Students will use both a Linux virtual machine and GPS’s Hadoop cluster to explore MapReduce, HBase, Hive, Pig, Cassandra and other big data technologies, using the Java programming language, SQL, and other data access languages. In addition, guest speakers will share their experience deploying these systems.
 
Prerequisites: SEIS610, SEIS630, and SEIS640 and Java programming experience
Instructors: Dr. Saeed Rahimi and Dr. Brad Rubin
Fall semester, 2012
SEIS 785 Section 02, CRN 43024
Wednesdays, 5:45 – 9:00 p.m.
3 credits

Please contact your advisor Doug Stubeda at 651-962-5503 or djstubeda@stthomas.edu if you have any questions or need assistance with registering for this course.
 

   SEIS 785-XX Topics: Data Analytics and Visualization
 
The objective of this class is to give students practical experience locating, preprocessing, analyzing, visualizing and presenting data, and relevant relationships among them. The student will study and develop Key Performance Indicators (KPIs) in data, and develop dashboards to monitor them, refine them with experience, and present them for review.

The course uses open source tools including R and RapidMiner.

During the class, students will complete small analytics projects on open-source data, and then complete a large group or individual project where they will locate a significant data source, design the analytics approach, apply those analytics, develop iterative visualization , key performance indicators, refine their technique, and report on the results.

This course sits at the intersection of Data Mining, Data Warehouses, and Big Data. Students, who have taken any of these other classes, will find an opportunity to apply and enrich those skills in this class. Conversely, students who have not taken those classes will be encouraged to do so after this class, to deepen their understanding of the wide array of techniques introduced summarily in this class.

Prerequisites: SEIS610 and SEIS630
Instructor: Dr. Bonnie Holub
SEIS 785, Section 02, CRN 22956
Spring semester, 2013
Tuesdays, 5:45 – 9:00 p.m.
3 credits

This course will count as an elective in all programs. This course can also be counted in the Data Management concentration.

Please contact your advisor Doug Stubeda at 651-962-5503 or djstubeda@stthomas.edu if you have any questions or need assistance with registering for this course.

 


The University of St. Thomas is registered as a private institution with the Minnesota Office of Higher Education pursuant to sections 136A.61 to 136A.71. Registration is not an endorsement of the institution. Credits earned at the institution may not transfer to all other institutions.