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 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
  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
  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 R. 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
  This programming-based analytics course will cover how to perform statistical analysis of large datasets that do not fit on a single computer. We will design a Hadoop cluster on Google Cloud Platform to analyze these datasets. Utilizing Spark, Hive, and other technologies, students will write scripts to process the data, generate reports and dashboards, and incorporate common business applications. Students will learn how to use these tools through Jupyter Notebooks and experience the power of combining live code, equations, visualizations, and narrative text. Employer interest in these skills is very high. Basic programming in python, and basic analytics are prerequisite. 3 cr.
• QST BA 860: Marketing Analytics
  This is an introductory course on Digital Marketing emphasizing analytics that seeks to familiarize students with digital marketing tactics. At the heart of marketing lies consumers and their marketing journey through the stages of awareness, intent, conversion and finally retention. In this course, we will learn how digital has revolutionized the interactions between firms and consumers along this journey. Digital offers powerful tactics to reach consumers along the funnel: online display ads raise awareness, search listings reach consumers with intent, on-site e-commerce marketing facilitate conversion, and social medial both energizes and retains customers.
• QST BA 865: Advanced Analytics Topics
  This course will introduce you to the Python programming language and the ecosystem of software packages needed for Data Science and to build and train Neural Networks in Python, including: NumPy, Pandas, SKlearn, and PyTorch. After reviewing key Python building blocks, the course will focus on Neural Networks and Deep learning Concepts and implementation in PyTorch. This is an intensive course and the majority of it will be presented through interactive python notebooks (Google Colab).
• 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: MSBA Marketing Elective
  
• QST BA 882: Deploying Analytics Pipelines
  Pre-requisites: QST BA775, BA780, BA810, or BA820. 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 II
  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.
• QST BE 101: Introductory Microeconomics for Business and Strategy
  Business economics provides students with an intellectual framework for understanding how businesses work: how firms interact in markets, and how markets respond to regulation and policy. Business economics has a dual mission: it is both a social science that describes how markets function and a framework that provides practical guidance for business leaders. This course focuses on business-relevant questions of how markets and businesses interact to create and distribute value. The course takes a data-based, empirical approach to these questions and uses experiential learning and interactive activities to enhance students' applications of economics to BU business problems. The course describes how social value is created via innovation and economic growth and how social value can be destroyed through harmful externalities. Effective Fall 2024, this course fulfills a single unit in each of the following BU Hub areas: Critical Thinking, Ethical Reasoning, Social Inquiry I.
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  • Critical Thinking
  • Ethical Reasoning
  • Social Inquiry I
• QST BE 325: Strategy in the Health and Life Science Sector
  Undergraduate Pre-requisite: Sophomore standing. - This course examines the distinctive strategic and economic challenges that healthcare and life science firms face. It explores how innovators, providers, and insurers in the healthcare industry create and capture value. We will develop frameworks of competition specific to the healthcare industry. Public policy responds to the unique features of these markets, and we will examine how this generates new affects business opportunities. The course offers insights into the unique aspects of the U.S. healthcare system and how it compares globally. We explore questions such as: How does payment affect the types of drugs firms develop? How do insurers avoid expensive customers? Who is incentivized to offer high-quality health care? Effective Fall 2024, this course fulfills a single unit in the following BU Hub area: Social Inquiry II.
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  • Social Inquiry II
• QST BE 350: The Psychology of Decision Making: Implications for Business and Public Policy
  Undergraduate Pre-requisite: Sophomore standing. - We provide an introduction to how individuals make decisions, applying the tools of psychology and economics. We will learn to identify common mistakes and biases. Students will have the opportunity to evaluate their own decision- making ability and learn how to make improved decisions. We link each aspect of decision-making studied to current personal finance decision, business problem & public policy issue. This course will improve negotiation ability and prepare students to use social science data to support decisions. The course consists of cases, discussions, lectures & project. Effective Fall 2024, this course fulfills a single unit in each of the following BU Hub areas: Social Inquiry II, Critical Thinking.
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  • Critical Thinking
  • Social Inquiry II