Business Analytics

• QST BA 221: Introduction to Data and Business Analytics
  Undergraduate pre-requisite: QSTSM 131 or sophomore standing and QSTSM 131 previously or concurrently. - Exposes students to business data and business analytics. Topics in business analytics include the fundamentals of probability and statistics, but the emphasis is on the collection, structuring, and analysis of data to support business decision-making. Topics include descriptive, predictive, and prescriptive analytics, as well as distributions, sampling, estimation, hypothesis testing, and chi-square analyses. Effective Fall 2025, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning I.
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  • Quantitative Reasoning I
• QST BA 222: Modeling Business Decisions and Market Outcomes with Spreadsheets and Statistical Programming
  Undergraduate Prerequisites: QSTBA 221. - Students must choose either QSTBA 222 or QSTBA 223, and students cannot take both courses. This course examines the use of economic and statistical tools for making business decisions at an advanced level, and prepares students for future study in business analytics. Introduces programming for data analysis (no previous programming knowledge required) and links data analysis to decision making using both spreadsheet modeling and statistical programming. Topics include multiple regression, causal inference, forecasting, predictive analytics, machine learning, demand modeling, and optimization. Case studies apply advanced concepts to practical business problems. Effective Spring 2021, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II.
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  • Quantitative Reasoning II
• QST BA 223: Business Modeling with Spreadsheets
  Undergraduate pre-requisite: QSTBA 221. Formerly QSTQM 222. Students must choose either QSTBA 222 or QSTBA 223 (formerly QM222). Students cannot take both BA222 and BA223. - This course examines the use of economic and statistical tools for making business decisions. The course emphasizes linking data analysis to spreadsheet modeling to support advanced business decision making. Topics include multiple regression, causal inference, forecasting, demand modeling, and optimization. Case studies apply concepts to practical business problems and the principal software tool used in the course is the spreadsheet. Effective Fall 2025, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II.
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  • Quantitative Reasoning II
• QST BA 305: Business Decision-Making with Data
  Undergraduate Prerequisites: QST BA222 or QST QM222 and either CAS CS111 or CDS DS110 - Explores advanced business analytics topics, including risk and uncertainty, optimization, decision analysis, multi-attributes objective functions, and time tradeoffs. Links data models to strategy and ethics. Relies on both statistical programming and spreadsheet modeling and introduces novel techniques. Cases studies and projects apply topics to practical business problems.
• QST BA 510: Neural Networks and AI: From Foundations to Generative Models
  Undergraduate pre-requisite - QSTBA 222 or QSTBA 223 - This course will introduce students to neural networks, from the most basic formulations through contemporary generative AI architectures. Students will learn to implement NNs with various types of structured and unstructured data (e.g., images, text, video, audio) employing TensorFlow and Keras.
• QST BA 572: Business Experiments and Causal Methods
  Undergraduate Prerequisites: CASCS108 or CASCS111 or CDS DS110 or QST BA222 - Formerly BA472. When is making a change to a price, algorithm, or product worthwhile' Rather than relying on the gut intuition of a manager, businesses are increasingly using experiments and other forms of causal data analysis to answer these questions. In this class, we will learn about causal methods, when they work, how to implement them in R, and how to apply them to digital markets. The business topics covered include pricing, balancing digital marketplaces like Airbnb and Uber, reputation systems, measuring influence in social networks, and algorithmic design.
• QST BA 576: Machine Learning for Business Analytics
  Undergraduate Prerequisites: CAS CS108 or CAS CS111 or CDS DS110 or QST BA222 - Formerly BA476. This course introduces students to the foundational machine learning techniques that are transforming the way we do business. Machine learning relies on interdisciplinary techniques from statistics, linear algebra, and optimization to detect structure in large volumes of data and solve prediction problems. You will gain a theoretical understanding of why the algorithms work, when they fail, and how they create value. You will also gain hands-on experience training machine learning models in Python and deriving insights and making predictions from real-world data. Prior programming experience is strongly recommended.
• QST BA 600: Introduction to Programming with Python
  This course provides a short, intensive introduction or refresher to fundamental programming concepts using the python programming language. It will cover pre- requisite topics important for future MSBA classes such as data types, flow control, iteration, functions, I/O, error handling, use of libraries and code documentation. Students will also be introduced to tools or platforms used throughout the MSBA program such as Jupyter notebooks and Git/Github.
• QST BA 602: Fundamentals of Data Analysis and Statistics
  This course provides a short, intensive introduction or refresher to data analysis and statistics. It will cover fundamental concepts such as single and multivariate analyses, probability, statistical distribution, hypothesis testing, sampling, regression, and early data visualization. The class will leverage and further build on python knowledge reviewed in BA600, and in some cases Excel (as more mainstream tool used in many business environments).
• 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.