Analytics Courses
501: Computational Intensive, 502: Applied Math Intensive, 503: Managerial Economics Intensive. A month-long intensive in three boot camps (computational, applied math and economics) with exposure to modern statistical packages, SQL, R and Python, review of probability and statistics, linear algebra, linear regression, and review of basic micro and macroeconomics, including pricing and demand, uncertainty and consumer modeling.
Mathematical techniques for analytics, including time series analysis, regression methods, problem-solving. Prerequisites: MSAN 501, MSAN 502, and MSAN 503.
Data mining, including classification and association. Rules, trees, and classifiers. Clustering. Data cleaning. Use of relational and non-relational (NoSQL) data stores. Prerequisite: MSAN 601.
Application of basic analytical methods to business problems. Topics include market basket analysis, management science, optimization and satisficing techniques, survey design. Prerequisite: MSAN 602.
Application of analytical techniques to economic models. Topics include econometrics, risk analysis, forecasting, and portfolio theory. Prerequisite: MSAN 603.
Provides both skills and experience in working with clients and opportunities to practice the professional skills required by business. The course features frequent presentations by program partners about real analytical problems and how they are addressed. The course features significant one-on-one mentoring and integration of topics presented in program’s courses. Prerequisite: MSAN 604.
During winter intersession (January), students work in small teams on a real-world project for a client. The team project takes a real-world data set and a set of client concerns, performs a comprehensive analysis, and prepares a business report, presentation and plan for the client. Prerequisite: MSAN 605.
Topics include: advanced data mining, text mining, modeling of problems for hadoop/MapReduce, network analysis, managing large data sets. Prerequisite: MSAN 611.
Presentation of complex visual data, including multivariate data, geospatial data, textual data, networks and graphs, and design principles. Prerequisite: MSAN 621.
Topics include: advanced regression methods, nonparametric and order statistics, and error analysis. Heavy emphasis on problem solving and application of techniques to real data sets in a specific domain. Prerequisite: MSAN 622.
Topics include: survival analysis, longitudinal data analysis, simulation, anomaly detection. These topics will be applied to specific real-world problems using marketing data. Prerequisite: MSAN 623.
Students are placed with a client as part of a semester-long project with weekly deliverables and meetings. Continued mentoring and development of professional business skills are also provided. Prerequisite: MSAN 624.
Topics include GIS, sports analytics, web mining, log mining, social networks, data integration. Prerequisite: MSAN 625.
Continuation of Practicum. Students also receive “soft skills” training in creating their CV, interviewing and networking, and study of the venture capital and startup process. Prerequisites: MSAN 625 and MSAN 631.
