Data Science Courses Offerings

The following courses are offered Fall 2017 as part of the Data Science Initiative. Each of the courses listed below counts as a required or an elective toward the Certificate in Big Data Analytics.

  1. BMIS 326 – Introduction to Data Analytics. TR 2:10-3:30 in GBB 213. This course introduces the terminology and application of big data and data analytics.  Students will complete cases in a variety of disciplines as they become acquainted with some of the software, tools and techniques of data analytics, including introductions to Python, R, Hadoop, and Tableau. (no prereqs). Required for the Certificate in Big Data Analytics.
  2. BMIS 491: Telling the Story with Big Data. This course explores how we turn data into stories that can be understood by nontechnical decision makers. We will follow the lifecycle of data science projects: question formulation, data specification, analysis, and communication of results. The course will culminate in presenting data-driven results to business stakeholders across several companies.
  3. M 461 - Practical Big Data Analytics.  W 2:10-5 in M 306. Introduction to big data analytics. Students will learn the core algorithms of data science by programming and applying the algorithms to real data sets. Class time is split between lecture (one third) and lab time (two thirds). Python and R are used extensively as well as Hadoop/MapReduce. Prereq: two mathematics classes at the 200 level or above and a course in probability or statistics at the 300 level or above.  Elective counting towards the Certificate in Big Data Analytics.
  4. BMKT 491: Advanced Marketing Analytics. This course applies the multidisciplinary skills of data science to marketing problems. Marketing today requires data to support strategic recommendations.  Working with many of the tools of the trade, including not only Excel, but also R, Python, SQL, and Hadoop, Students will deliver real results to real clients.  In addition, students in this class will collaborate with "Telling the Story with Data" to provide those students with the data for the ‘storytelling’ phase.
  5. BMIS 465 / CSCI 491: Students will use IBM Infosphere Streams software to extract relevant data from data sources in motion, and they will analyze the data using appropriate statistical and mathematical techniques.
  6. CSCI 444: Data Visualization. Visualization fundamentals and applications using special visualization software; formulation of 3-D empirical models; translation of 3-D models into graphical displays; time sequences and pseudo-animation; interactive versus presentation techniques; special techniques for video, CD and other media.
  7. CSCI 464: Big Data Mining.  This class will expose students to applications of data. Students will become functionally adept at data acquisition, data cleansing, feature selection, and data analysis. Though some time will be spent on the internals of popular algorithms for data mining, this discussion will be limited to developing intuition for proper use of off-the-shelf utilities, not for implementation.