BAN-210: Essentials of Analytics
This course provides an introduction to the field of Business Analytics, with a foundational basis in Business Statistics. Specific analytic topics covered include: Data Mining, Data Warehousing, Data Visualization and Analytics Software. 3 credits.
BAN-300: Applied Analytics
This course provides an opportunity for students to conduct analyses of real data, following all stages from data acquisition and preparation through analysis and presentation. While good data analysis requires many skills, the vast majority of an analyst’s time is spent on preparing, cleaning, and understanding what the data actually means – how was data collected, how is data measured, and what does each variable really mean? There are no prerequisites and students are expected to have a range of abilities from novices to some with statistics backgrounds. Work will be done in groups with tasks appropriate for each student’s skill level. Projects will vary in subject areas, and may include survey data, use of public databases (e.g., Census data or sports data), or data sets collected by individual entities (such as particular business entities). Prerequisite: A statistics course. 3 credits.
BAN-310: Data Visualization
This course provides an introduction to the field of data and information visualization, a key sub-field in the area of data analysis and mining. Specific analytic topics covered include: tables & charting, multi-dimensionality of data, handling unstructured data, and advanced visualization tools and techniques. Prerequisite: L.CIT 110 or L.CIT 221. 3 credits.
BAN-320: Predictive Modeling
The rapid expansion of data availability has made possible considerable advances in modeling for the purpose of prediction. Virtually all decisions, at least in part, depend on predictions of what will happen if something changes (either under our control or not). This course explores applications of a variety of current predictive modeling techniques to data. Included are multiple regression modeling, logistic regression, decision trees, random forests, neural networks, and simulation analysis. The emphasis will be on applied analysis, utilizing data from a wide variety of areas, including business, politics, socioeconomic conditions, health, sports and entertainment, etc. Students will build and compare predictive models, learn how to evaluate these models, and how to apply model results to improve decision making. Prerequisite: L.BAN 330 or L.DAT 200. 3 credits.
BAN-330: Introduction to Data Science
Data science is the process of collecting, cleaning, analyzing, summarizing and presenting data in a scalable and generalizable manner. In this course, students will learn each of these steps using R, an open source analytics language, culminating in a project. Prerequisites: L.CIT 115, L.MAT 220. 3 credits.
Gaining a competitive advantage in today’s business environment increasingly demands that organizations know how to innovate. Creativity, continuous improvement, and the ability to turn ideas into action are critical to standing out above the rest. Specific topics will include: the innovation process, disruptive technologies, why plans are bad, and when NOT to listen to your customers. We will also apply our knowledge via an innovation simulation. Prerequisites: L.ACC 227, L.BUS 230, L.BUS 240. 3 credits.
BAN-450: Marketing Analytics
This course explores the topic of Marketing analytics which has grown significantly in recent years in response to the rapidly increasing supply of data generated by marketing campaigns, online sales, websites, social media, customer relationship management programs and integrated marketing communication campaigns. Through enhanced technology, more data are available than ever before. But marketers are faced with the dilemma of how to convert the massive amount of available data into usable information. In this course students will engage in the systematic study of these data which are employed, through the use of statistical analysis and technology, to improve decision making. Prerequisites: L.BAN 210, L.BUS 240. 3 credits.
BAN-460: Big Data Analytics
Big Data is often referred to as a new form of natural resource. This is a poor metaphor, as Big Data is growing faster than any other natural resource grows. Big Data is really a more accurate view of the past, what an economist might refer to as closer to perfect information. This of course can be used for great social value or for personal destruction. This course should be a combination of tactical skills of Apache’s hadoop/map-reduce along with the relevant discussion of the social opportunities offered by big data. Prerequisite: L.CIT 225. 3 credits.