Common Core Courses
Introduction to Data Science
This course introduces fundamentals about data and the standards, technologies and methods for organizing, managing, curating, preserving, and using data. Students discuss broader issues related to data management and use as well as quality control and publication of data.
Data Administration Concepts and Database Administration
This course covers the definition, development and management of databases for information systems. Students learn data analysis techniques, data modeling, schema design, query languages and search specifications.
This course introduces data mining techniques, real-world applications and its challenges, and future directions of the field. Students have an optional hands-on experience with commercially available software packages.
Data Analysis and Decision Making
This course covers concepts, principles and methods to support scientific approach to managerial problem solving and process improvement. Students learn basic statistical techniques, how and when to use the techniques as well as assumptions associated with their use.
This course is designed for graduate students who are interested in developing a portfolio of skills in business analytics. Class discussions will be based on case situations and articles from business and technical publications. Students perform substantial hands-on work in data collection, analysis and interpretation.
Advanced Data Analytics
This course is a broad introduction to analytical processing tools and techniques for information professionals. Students develop a portfolio of resources, demonstrations, recipes, and examples of various analytical techniques.
Analytics Application Core Courses
This course covers accounting analytics including Benford’s Law, current and prior period data, and anomaly detection. Students learn correlation and time series detection, risk assessment and risk scoring, and purchasing card transaction fraud.
This course covers marketing analytics techniques including discriminant analysis, logit, cluster analysis, factor analysis, and conjoint analysis. Students learn marketing decision support models such as new product diffusion, test-market, price, and sales promotion decision models.
This course introduces methods and tools useful in decision making in the financial industry, including: macroeconomic event studies, analysis of term structures, Morningstar equity data, style analysis, credit card receivables, trading analytics, and execution algorithms.
Principles of Management Science
In this course, students learn the concepts and development of analytical model building as used in global supply chain decisions.
Linear Statistical Models
This course covers general regression models, estimation methods, general linear hypothesis tests, residual analysis, and indicator variables. Students learn multicollinearity, autoregressive models, weighted least squares, and variable-screening procedures.
Times Series Modeling and Analysis
This course covers the fundamental concepts and procedures for forecasting discrete time series for planning and control. Students learn regression analysis, ARIMA methods, econometric modeling, transfer functions, intervention analysis, Kalman filters, univariate, and multivariate methods.
Scripting for Data Analysis
This course explores scripting for the data science pipeline. Students learn to acquire, access, and transform different forms of data, including structured, semi-structured and unstructured data.
Natural Language Processing
This course focuses on the linguistic and computational aspect of natural language processing technologies. Course lectures, readings and projects emphasize computational techniques required to perform all levels of linguistic processing of text.
This course is a broad introduction to data visualization for information professionals. Students develop a portfolio of resources, demonstrations, recipes, and examples of various data visualization techniques.
This course introduces the concepts of business intelligence (BI) and the practice/techniques in building a BI solution. Students focus on how to use data warehouses as a BI solution to make better organizational decisions.
This course introduces concepts and methods for knowledge discovery from a large amount of text data. Students learn the application of text mining techniques for business intelligence, digital humanities, and social behavior analysis.
Advanced Database Management
This course is an in-depth analysis of databases and database management system architecture, building complex database objects and database applications using forms and reports. Students learn about data warehouses, establishing and implementing database security, and tuning databases for optimum performance.
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