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.

Examination of large-scale data storage technologies including NoSQL database systems for loosely-structured data, and warehouses for dimensional data.

Application and interpretation of analytics for real-life decision making.

The requirements capture, design, and development of relational database applications; analysis of business requirements and development of appropriate database systems.

This course covers basic as well as advanced concepts to gain a detailed understanding of Natural Language Processing tasks such as language modeling, text to speech generation, natural language understanding, and natural language generation. Students can learn the necessary skills to design a range of applications, including sentiment analysis, translating between languages, and answering questions. The practical implementation of these applications with deep neural networks is also discussed.

This course will teach the foundations of AI and give students a practical understanding of the field. This course gives an overview of the core concepts of AI, including the intelligent agents, knowledge and reasoning, reinforcement learning, planning and acting, belief networks, computational learning, Markov decision process, and more.

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.

This course will cover the foundations on neural networks and deep learning networks. It covers the core concepts of deep neural networks, including the convolutional neural networks for image recognition, recurrent neural networks for sequence generation, and generative adversarial networks for image generation.

This course will cover the main theory and approaches of reinforcement learning, along with deep learning and common software libraries and packages.

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

This course provides an exploration of current and emerging big data solutions for handling large quantities of data in real-time. In particular, this course investigates methods to design, develop, and implement several systems used for real-time data analysis and storage such as document databases, column-based databases, queueing systems, and real-time processing systems.

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.

Design and implement data science and analytics systems using contemporary tools and techniques. Choice of project topic mutually determined by student and instructor. Students must complete all core and required courses before enrolling.