Business Analytics

• QST BA 875: Operations and Supply Chain Analytics
  This is an introductory course on principles, methods, and techniques used in operations and supply chain analytics. Emphasis is given on the big data age where firms are continuously designing, assessing, and improving the systems that create and deliver their products and services. Students will learn visual representation techniques to enhance their understanding of complex data and models. Such visual techniques will be paired with network analysis to better identify patterns, trends and differences from datasets across categories, space, and time. The course will also draw on real-world applications to demonstrate their use in a variety of contexts.
• QST BA 878: Machine Learning and Data Infrastructure in Health Care
  This course is designed to provide students with a deeper understanding of the key concepts, methods, and tools in data science, machine learning, and data infrastructure applied to the world of health care. The course will cover both theoretical foundations and practical applications of these topics, with a focus on the integration of data science techniques with data infrastructure. The course will include hands-on examples from real world data sets the will enhance skills and experiences in health care. In addition to reviewing key steps in the data science process (i.e. data preparation, exploratory data analysis, feature engineering, model selection, model evaluation, and model deployment) and machine learning techniques, we'll explore how to use, apply, and deploy them in various healthcare settings. Students will learn about data architectures, distributed data processing systems, data pipelines, data transformation, and data visualization tools, and how different healthcare players are solving data challenges at scale. By the end of the course, students will have developed a deeper understanding of data science, machine learning, and data infrastructure, and will be able to apply these concepts to solve complex problems in a variety of healthcare domains across a multitude of data types.
• QST BA 880: People Analytics
  This course focuses on developments in People Analytics, an evolving data-driven approach to employee decisions and practices. Managers must decide how to lead people in the context of new technologies, management practices, empirical methods, and increased collaboration with external stakeholders (e.g. software vendors, consultants, academic researchers). The goal of the course is 1) to provide an overview of the people analytics field, 2) to develop skills in research design, and 3) to understand how to implement people analytics projects in an effective and responsible manner. The course covers theory, practice, and methods that are critical for addressing people- related challenges at companies, such as hiring, retaining, evaluating, rewarding performance, and managing teams and social networks, to name a few. While a background in statistics, analytics and regression methods is helpful, it is not required for success in the course. 3 cr.
• QST BA 881: Analytics for Customer Strategies
  In this course, students learn the principal methods of analytics used to maximize customer profitability. They learn statistical tools to identify, target, acquire and develop profitable customers for the long term. Using a rich range of cases drawn from B2C and B2B companies, emphasis is placed on drawing insights from the analyses to inform business strategy. Students will learn to solve core marketing challenges using analytics including measuring demand, defining customer segments, targeting customers for acquisition, and developing customers for profitability.
• QST BA 882: Deploying Analytics Pipelines
  Pre-requisites: QSTBA 600, QSTBA 602, QSTBA 780, QSTBA 810, QSTBA 820. This course will equip students with the essential skills for transitioning data analysis and machine learning tasks to the cloud, supporting production workloads. It covers the creation and deployment of data and ML pipelines, including those for generative AI applications, with a focus on data integration strategies, cloud data warehousing, BI, and ML-Ops. Leveraging prior coursework in data management and machine learning, students will learn to implement ETL/ELT processes, monitor data quality, and deploy models as APIs using cloud services.
• QST BA 885: Advanced Analytics 2
  This course covers analytics topics in applied optimization (or prescriptive analytics). In contrast to the unsupervised and supervised machine learning studied in BA820 and BA810 (and BA865) where the focus was to discover patterns and predict uncertain events, this course focuses on determining the best course of action given an objective and a set of constraints. In other words, making operational and strategic decisions using a rigorous and principled approach. The methods learned in this course have broad application including in logistics, marketing, health care, finance, and more. Example problems include determining which products to advertise to which customer to maximize sales, identifying best location of warehouses to best serve geographically dispersed stores or customers, and allocating medical resources to health care facilities to minimize the fallout during an active pandemic. Topics include linear programming, integer programming, network models, and related methods. Students will learn how to set up such optimization problems and solve them using spreadsheets and Python.
• QST BA 888: Capstone Project
  The capstone project course will allow students to work on a data project in a team setting. The goal is for the students to solve a real-world problem using the knowledge, tools, and techniques acquired throughout the program and show their skills to potential employers. This course spans across the degree program and requires multi-semester efforts, however, the vast majority of the work will be done during the spring semester. The final product will be presented to a faculty panel at the end of the spring semester, followed by a poster session which will be open to the public.
• QST BA 890: Analytics Practicum
  The analytics practicum provides an opportunity for students to gain individual, practical experience related to business analytics. Students will complete a report based on one of the following: - Reflection paper related to an internship experience: Students will describe work accomplished and knowledge gained from working on a part-time or full-time internship in an area directly related to Business Analytics (e.g., data engineering, data analysis, data modeling, machine learning, data visualization). The paper should demonstrate the student's knowledge of Business Analytics concepts acquired through the internship experience. - Research Project: Students will select a topic related to Business Analytics which has not been covered in existing coursework or significantly extends concepts taught in the MSBA curriculum. The research topic can be novel or can be an extension of work completed during the capstone project. It should be substantive enough in terms of technical, quantitative, data management, or programming aspects and contain appropriate references. Students should not merely compile work of others, but also display genuine critical thinking.
• QST BA 891: Analytics Practicum 2
  0 cr. The analytics practicum provides an opportunity for students to gain individual, practical experience related to business analytics. BA891 is a required course for all MSBA students on the 16-month track that provides additional opportunity for students to explore new topics or deepen their knowledge and skills, in areas covered in prior coursework (for example, in BA888 or BA890). Students will complete a report based either on a reflection paper related to an internship experience, or on a research project based on a topic related to Business Analytics which has not been covered in existing coursework or significantly extends concepts taught in the MSBA curriculum. The research topic can be novel or can be an extension of work completed during the capstone project or during BA890. It should be substantive enough in terms of technical, quantitative, data management, or programming aspects and contain appropriate references. Students should not merely compile work of others, but also display genuine critical thinking.