Note:

• Students who entered MSBA program before 1/1/2023 will still follow the original curriculum with GBA 6761 (1 unit), GBA 6762 (2 units), and GBA 6951 (3 units).
• Students who entered MSBA program after 1/1/2023 will follow the new curriculum with GBA 6763 (2 units), GBA 6764 (3 units), and GBA 6952 (2 units).

Note:

• Students who entered MSBA program before 1/1/2023 will still follow the original curriculum with GBA 6761 (1 unit), GBA 6762 (2 units), and GBA 6951 (3 units).
• Students who entered MSBA program after 1/1/2023 will follow the new curriculum with GBA 6763 (2 units), GBA 6764 (3 units), and GBA 6952 (2 units).

Introduction to the descriptive analytics cycle. Problem framing, data collection, data cleaning, data visualization, data analysis, and dissemination of results. Storytelling for intelligence dissemination. Data warehousing and on-memory database solutions. Differences between descriptive analytics and predictive analytics, prescriptive analytics, social media analytics, and Big Data. Ethical and privacy challenges.

This course serves as the technology and programming foundation for business analytics projects. Students are exposed to a programming or scripting language under the context of business analytics cases.

Applications of managerial statistics for business decisions. Data collection, confidence interval estimation of mean and proportion, one and two-population hypothesis testing of mean and proportion, one-way and two-way Chi-square testing, simple linear regression, multiple linear regression, and Analysis of Variance.

Work effectively in cross -functional teams. Nature of teams- types of groups and teams, team objectives, roles, norms, and rules. Team stages of development. Team characteristics and how they affect team functioning. Team cohesiveness, factors affecting team cohesiveness, its advantages and disadvantages. Role of conformity and deviance in team performance. Team task interdependence and decision-making Effective communication and conflict management in teams. Design thinking, creative problem solving and innovation in teams.

This course aims to equip students with knowledge, experiences, and programming skills of applying predictive analytics in business contexts with hands-on exercises and projects. Students will learn to model significant and meaningful patterns embedded in historical data using data mining techniques, evaluate performance of machine learning models, and deploy the models for prediction.

Explore the various facets of how business data are organized, delving into relational database management systems, data warehouses and data marts, and distributed data environments such as NOSQL databases. Students survey the means of creating business data sources through data modeling techniques and review retrieving data working with standard data management languages such as SQL for the purpose of addressing issues such as data quality, data integration, and data management. Software used - MS Access, MS SQL etc.

This course introduces advanced statistical methods and procedures for estimating microeconomic relationships, testing theories, and evaluating and forecasting impacts of business decisions. This course equips students with the capability to read and critique professional empirical literature in business and economics, and to conduct independent research using business data. This course focuses on business applications in areas such as Marketing, Finance, Operations and others.

This course serves as the second step in the three-course sequence of an innovative business analytics project. Students closely work with faculty advisor and advisory board to perform business analytics project analysis, develop and polish business stories based on the analysis, and plan project implementation. 

Data collection, preparation, visualization, and analysis with software applications. Topics include: Web scraping, Application Program Interface (API) data collection, visualization, data type and structure, unstructured data analysis (a.k.a. text mining and social network analysis), and sentiment analysis. Programming language - Java, Python, R and/or others.

This course is to help students understand how complex business problems can be modeled, analyzed, and solved in an optimal manner. Students will learn to develop spreadsheet models for making complex business decisions, as well as interpret the results of such models. The course covers optimization models including various mathematical programming models and decision making under risk and uncertainty. 3 lecture/discussions.

This course covers key technologies and applications for big data analytics. Topics include: big data acquisition, big data storage, and real-time and batch analysis of big data. 

This course serves as the third and final step in the three-course sequence of an innovative business analytics project as the culminating individual project experience. In this course, students will finalize data analytics and generate insights from the data. Based on the project analysis result, students closely work with faculty advisor to develop process improvement and implementation procedures and make final recommendation to the business partners.