Courses

Survey course on the key topics in predictive analytics. Students will learn methods associated with data analytics techniques and apply them to real examples using the R statistical system.

Practical benefits of data mining will be presented; data warehousing, data cubes, and underlying algorithms used by data mining software.

Descriptive statistics, hypothesis testing, power, estimation, confidence intervals, regression, one- and two-way ANOVA, chi-square tests, diagnostics.

Explores the use of descriptive analytics concepts, tools, and techniques throughout a wide range of business scenarios and problems.

Explores the use of predictive analytics tools and techniques throughout a wide range of business scenarios and problems; includes a subset of methods such as neural networks, machine learning, social media analytics, and more.

Development of methods for prescriptive analytics with a focus on business supply side decisions and risk mitigation.

This course examines fundamental concepts and ideas in demography and U.S. and world population trends associated with these concepts.

This course provides an overview of key demographic data sets, and promotes familiarity with, and appropriate use of, these data.

This course provides an overview of applications in applied demography in business, nonprofit organizations, public policy, and health, including a focus on international applications.

Examines tools and techniques required for data collection and computational procedures to automatically identify and eliminate errors in large data sets.

This course will study the inter-relatedness of cyber-social and cyber-technical aspects of an organization or society as a whole.

This course will explore the development of analytics systems and the application of best practices and established software design principles using the Python programming language and its several toolkits.

This course provides a foundation in the principles, concepts, techniques, and tools for visualizing large data sets.

The course examines business intelligence in the era of big data. Emphasis is on the successful implementation of big data in large and small corporations that deliver extraordinary results.

Models and measures of vital processes (fertility, mortality, migration) and their effects on growth and age structure of human populations.

Exposes students to the spatial analysis tools and analytical methods applied to demographic research.

Introduction, intermediate, and advanced topics in SAS.

Analysis of research data through simple and multiple regression and correlation; polynomial models; indicator variables; step-wise, piece-wise, and logistic regression.

Identification of models for empirical data collected over time. Use of models in forecasting.

Analysis and construction of project plans for the development of complex software products; how to manage change and cost control.

Sets business analytics in real-world context. Explores project life cycle from business problem-framing to model lifecycle management.