Courses

The listing of a course description here does not guarantee a course’s being offered in a particular term. Please refer to the published schedule of classes on the MyBU Student Portal for confirmation a class is actually being taught and for specific course meeting dates and times.

• QST BA 775: Business Analytics Toolbox
  Pre-requisites: QST BA600, QST BA602. - In today's data-driven world, much of the valuable information companies hold resides in databases. This hands-on course equips students with the skills to navigate various relational databases and master SQL, the industry-standard language for querying data. Students will gain a strong foundation in modern and traditional databases by engaging with diverse datasets and learning essential SQL techniques such as selecting, filtering, sorting, grouping, and joining data. Students will set up their own databases in the cloud, working with real-world data to understand practical applications. The course will also introduce key cloud components, including cloud storage and computing, enabling students to create and manage a virtual cloud environment for their analyses. Building on these fundamentals, the course will guide students to develop visualized data summaries and construct dynamic business intelligence dashboards. By the end of the course, students will be proficient in data storytelling and equipped with the tools to create insightful and impactful dashboards that drive business decisions.
• QST BA 780: Introduction to Data Analytics
  Pre-requisites: QST BA600 and QST BA602. Data analytics involves exploring, discovering, interpreting, and communicating meaningful patterns in data. Business analytics applies these principles to organizational data, driving data-driven decision-making and creating competitive advantages. This course emphasizes data munging and techniques for handling structured datasets. Students will learn to work with common data types and import them into Python. The core focus will be on data manipulation, including data cleaning, handling missing values, and performing transformations to prepare datasets for analysis. Students will also develop exploratory data analysis, visualization, and summarization skills. These foundational steps are crucial for formulating business problems and developing complex statistical models. The course will conclude with creating data reports and interactive dashboards, essential tools for effective data communication in any data science project..
• QST BA 810: Supervised Machine Learning
  Pre-requisites: QST BA600, QST BA602, QST BA780. This course provides students with practical skills in performing hands-on analytics on real-world datasets predominantly gathered through digital interactions. Students will learn to apply supervised machine learning techniques to analyze this data, using the Python programming language. The course includes a series of lectures and in-class exercises designed to help students derive actionable recommendations from their analyses and effectively present their findings. The goal of the course is to foster a deep understanding of popular supervised machine learning methods and to illustrate the types of business problems to which these techniques can be effectively applied.
• QST BA 815: Competing with Analytics
  The objective of this class is to examine how business analytics is applied across different industries and functions, how it delivers value, which skills are core to capturing this value, and which pitfalls await organizations. The course will rely extensively on seasoned industry experts sharing their direct experiences and include readings, case study discussions, and hands-on team assignments. Rather than taking a narrow(er) focus on any one topic, the course will take a broad lens and provide a wide set of pertinent examples of application in industry (e.g. recommender systems, web analytics, personalization campaigns, pricing and revenue management, ML Ops, data storytelling, demand forecasting/sensing, inventory optimization, fraud and claims analytics, ESG modeling, managing data science projects, etc.).
• QST BA 820: Unsupervised and Unstructured Machine Learning
  Pre-requisites: QSTBA 600, QSTBA 602, QSTBA 780, QSTBA 810. It has been reported that as much as eighty percent of the world's data is unstructured. This course will cover the methods being applied to both unstructured and unlabeled datasets. Through a series of lectures and hands-on exercises, students will examine the techniques to unlock insights from data that appear to lack a known outcome. The goal of this course is to compare and contrast the application of various methods being applied today and provide the foundation to develop impactful insights from these datasets.
• QST BA 830: Business Experimentation and Causal Methods
  Pre-requisites: QSTBA 600, QSTBA 602, QSTBA 780, QSTBA 810. This course teaches students how to measure impact in business situations and how to evaluate others' claims of impact. We will draw on a branch of statistics called causal inference that studies when data can be used to measure cause and effect. The course will begin by discussing randomized controlled trials, the most reliable way of measuring effects, and will move onto other methods that can be used when experiments are not feasible or unavailable. We will learn how to implement these methods in Python. Causal inference has become especially important for digital businesses because they are often able to run experiments and to harness 'big data' to make decisions. We will illustrate the methods we learn with examples drawn from digital businesses such as Airbnb, Ebay, and Uber and through topic areas such as price targeting, balancing digital marketplaces, reputation systems, measuring influence in social networks, and algorithmic design. We will also use data from other business and social science applications.
• QST BA 840: Data Ethics: Analytics in Social Context
  This class examines ethical issues of data, data science, and algorithms. We consider unintended consequences and transparency of algorithms, phenomena such as mass personalization and experimentation, and examine competing ideas about privacy and the sometimes blurry line between the private and the public spheres in the digital age. The course is intended to place analytics in a social context and equip students to anticipate and understand the ethical tradeoffs they will be making in the process of doing analytical work.
• QST BA 843: Big Data Analytics for Business
  Pre-requisites: QSTBA 600, QSTBA 602, QSTBA 780, QSTBA 810, QSTBA 820. Every company is a ¿data company,¿ possessing vast quantities of data from operations, customers, products, and transactions. With big data comes significant challenges requiring specific infrastructure and skills. The analytics process, including deploying and using big data tools, is essential for organizations to improve efficiency, drive new revenue streams, and gain a competitive edge. This course addresses these challenges, discusses methods to overcome them, and common pitfalls in implementation and unnecessary analysis. Data analytics involves exploring, discovering, interpreting, and communicating meaningful patterns, whereas big data analytics focuses on analyzing data on a larger scale, where a single computer cannot process it timely. Distributed computation, the foundation of big data analytics, involves a network of computers processing data segments. This course teaches students to perform statistical data analysis of large datasets using distributed computation and introduces machine learning techniques and libraries that handle big data.
• QST BA 860: Marketing Analytics
  Pre-requisites: QSTBA 600, QSTBA 602, QSTBA 780, QSTBA 810, QSTBA 820, QSTBA 830. This is a course on analytics in digital marketing. The core of marketing is reaching your audience and communicating the value of your brand and products to them, so that you can grow and retain customers. Digitization offers a variety of new data and tools that makes this effort more accessible for large and small companies alike. This course aims to familiarize students with digital marketing analytic tools, as well as the mindset of focusing on incrementality when analyzing the effects of marketing strategies. We will introduce marketing tactics used in different stages of a customer's journey, including advertising, search engine optimization, pricing, and on-site marketing. In the context of these topics, we introduce analytic tools to measure marketing effects and optimize campaign efforts, including experiment design and analysis, targeting campaign design and assessment, recommender models, and attribution modeling.
• QST BA 865: Neural Networks in Business: From Foundations to Generative AI
  Pre-requisites: QSTBA 600, QSTBA 602, QSTBA 780, QSTBA 810, QSTBA 820. This course provides a basic introduction to the theory and implementation of artificial neural networks (ANNs) in Python. We will introduce students to Keras and PyTorch, Python packages / frameworks that support the implementation of neural networks. We will then develop an understanding of fundamental concepts behind neural network architecture. We will explore a variety of use cases, deal with various types of unstructured data (text, images, audio, time series), and gain hands-on experience, starting with the implementation of simple networks and building up towards harnessing the powers of more complex pre-trained ones. This is an intensive course. Students will pursue a hands-on group project over the duration of the course.
• QST BA 870: Financial Analytics
  This is an introductory course on Financial Analytics providing students with knowledge about key "financial" concepts (financial accounting, financial statements, managerial accounting, corporate finance, and investments) so that they can intelligently apply their prior analytics knowledge and tools to real- world financial applications.
• 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.