Business Analytics

• QST BA 222: Modeling Business Decisions and Market Outcomes with Spreadsheets and Statistical Programming
  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. 4 cr. 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 305: Business Decision-Making with Data
  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 472: Business Experiments and Causal Methods
  Formerly MK472. 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 476: Machine Learning for Business Analytics
  Formerly MK476. 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 755: Describing, Analyzing, and Using Data
  This course focuses on how to learn from data, specifically to 1) organize, portray, and summarize data; 2) assess the validity of conclusions that have been drawn from statistical analyses to support business (and other) decisions; and 3) recognize the extent to which variation characterizes products and processes, and understand the implications of variation on organizational decisions when interpreting data. Students will increase their understanding of the use of probabilities to reflect uncertainty; how to interpret data in light of uncertainty to assess risk; and how to build and interpret regression models, which can be used to inform core business and organizational decisions.
• QST BA 765: Introduction to Programming for Data Science
  This course will cover the fundamentals of programming for data science using R, the command line, and version control. These skills will be reinforced via lectures and hands-on exercises focused on elevating common programming challenges and highlight best practices. The aim of this course is to provide the pathway to fluency in the tools required to analyze data and fully manage data science projects both as an individual contributor as well as in team settings.
• QST BA 775: Business Analytics Toolbox
  This course will primarily focus on data and the key techniques that are necessary when working programmatically. Data is obtained from a data source; students will learn how to work with the most common data sources and how to load it into R. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset.
• QST BA 780: Introduction to Data Analytics
  This course focuses on data munging and the standard techniques that are necessary to work with any structured dataset. Students will learn how to work with the most common data sources and how to load them into python. Once the data is loaded and before it can be analyzed one needs to apply a series of steps known as data munging to get a tidy and workable dataset. Data munging will be the core of this course, where students will learn how to clean the data, handle missing values, perform data transformations and manipulations, and prepare it for analysis. Through learning data visualization, exploratory techniques, and summarizing methods students will become competent to perform exploratory data analysis. These techniques are typically applied before any modeling begins and can help to formulate or refine the business problem. They are also stepping stones in informing the development of more complex statistical models. The course will conclude with creating data reports and interactive dashboards, two major communication tools required in any data science project.
• QST BA 810: Supervised Machine Learning
  The internet has become a ubiquitous channel for reaching consumers and gathering massive amounts of business-intelligence data. This course will teach students how to perform hands-on analytics on such datasets using modern supervised machine learning techniques through series a lectures and in-class exercises. Students will analyze data using the R programming language, derive actionable insights from the data, and present their findings. The goal of the course is to create an understanding of modern supervised machine learning methods, and the types of problems to which they can be applied.
• 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 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 880: People Analytcs
  
• QST BA 885: Adv Analytcs II
  
• QST BA 886: Anlytc Prjct I
  
• QST BA 887: Anlytc Prjct II
  

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