Online Business Analytics Courses
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A Rigorous, Experienced-Based Curriculum
Throughout Georgetown McDonough’s online Master of Science in Business Analytics program, you will complete 18 courses in 16 months studying the theories, concepts, and methods that allow you to lead change and use data ethically across all major business functions.
The MSBA faculty have designed high-quality courses that include practical tools and methods you will use upon graduation. You will explore case studies, use real-world databases, and learn how to apply the latest analytics tools and technologies to challenging business problems.
You will learn in a small class of peers, which allows you to build deep and long-lasting relationships and benefit from their diverse experiences.
The program includes two five-day residencies where you will earn credit through intensive coursework and a final capstone project in which you solve a current business analytics problem with a local, national, or global sponsor.
By graduation, you will be able to speak the language of data and its business application to communicate it effectively with critical stakeholders. This provides your foundation to create, share, and sustain value for business and society.
Core Online Business Analytics Courses
Statistics for Business Analytics
Statistics, being the science of collecting and analyzing data, provides numerous tools and concepts that can be used in the field of business analytics. This course provides an introduction to foundational concepts needed to better understand the statistical tools in the data-related courses of the program. This course emphasizes how statistical methods can be applied to problems and improve decision-making in organizations through the appropriate use of statistics. The course aims to develop analytical thinking and illustrate the appropriate use of these methods in practice. Topics include describing data, transforming data, understanding variation and uncertainty, measuring dependence and establishing associations, inferential methods, goodness of fit, and regression models.
Machine Learning I
Machine learning refers to a set of tools for modeling and understanding complex datasets that build on statistics and computer science. This course introduces the main tools developed in this field. The course first covers traditional regression methods where the dependent variable is either a continuous or a discrete variable. It then introduces students to resampling methods, such as cross-validation and bootstrap. Finally, it covers model selection and regularization methods. These include subset selection methods, shrinkage methods, and principal component regressions. The course emphasizes a rigorous treatment of the material as well as its practical implementation for business.
Machine Learning II
This course is a continuation of Machine Learning I, covering advanced supervised and unsupervised learning methods in more detail. Among the supervised learning methods, the course starts with regression splines, smoothing splines, and Generalized Additive Models (GAM). It then covers tree-based methods such as CART, Boosting, and Random Forests. Finally, it introduces students to support vector machines. Among the unsupervised learning methods, the course covers principal component analysis, k-means clustering, and hierarchical clustering. The course emphasizes a rigorous treatment of the material as well as its practical business implementation.
Predictive Analytics is a subfield of business analytics that draws tools and techniques from data mining, machine learning, and predictive modeling that aim to create models that can precut outcomes for uncertain events. In this course, we will build on earlier classes on statistical foundations and machine learning to enrich our set of tools for prediction. The first part of the course will include uncovering patterns, features and regularities in data to build powerful models for prediction. Topics will include association rules and dimension reduction. The second part of the course will focus on how businesses integrate these ideas to create models for successful prediction. Topics include regression trees, k-nearest neighbors, artificial neural networks, and support vector machines. This section will also cover ensemble modeling where predictions and classifications are made using combinations of models. The course will focus on the benefits, challenges and pitfalls of applying these concepts in practice and help students think analytically through the process of building, implementing and providing prescriptions from predictive models.
Only about 20% of the data available for businesses is in structured data, and the other 80% is unstructured and in free text. This course covers the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be applied to text data.
Programming I: Introduction to Data
In this course, students learn how to write practical applications for business problems in a coding language. This course provides programming skills to create, clean, and handle data for managerial decisions. The course covers basic programming topics such as data types, variables and constants, and their declaration, arithmetic operators, repetition control structures, basic file operations, elementary searching, and sorting. Students will be exposed to programming tools that are commonly used in the business analytics field using R.
Programming II: Data Infrastructure
This course equips students with intermediate concepts in programming that are required in courses such as Predictive Analytics. This course focuses on topics such as object-oriented programming, advanced data structures (arrays, stacks, queues, lists, trees, heaps, graphs), vector and matrix-based calculations, user-defined functions, and recursion. All concepts are demonstrated through business applications.
Programming III: Working with Big Data
This course equips students with advanced concepts in programming that are important for working with massive data. This course builds upon and extends the Data Infrastructure course by considering advanced topics, including robust event and error handling, profiling, benchmarking, artificial intelligence, graphical user interfaces (GUIs), and mobile computing, among others. All of the advanced programming concepts are demonstrated through business applications.
Applied Economics and Modeling
Correlation is not causation. Or is it? This course focuses on how managers can apply statistical methods to learn about causal empirical relationships, leveraging pre-existing knowledge about market conditions. The methods include randomized controlled trials, regression discontinuity design, differences-in-differences regression, and instrumental variables. Students learn the theoretical rationale for these methods and apply them in a series of business and economic applications. The ultimate goal is to provide a better understanding of when data support actionable business recommendations.
Strategic Marketing Analytics
In the 21st century, marketing managers and their teams can transform their business’s bottom line by applying deep knowledge gained through modern analytical tools. Marketing analytics includes understanding various types of data available in the marketing space including point of sale and digital data to build future campaigns and marketing initiatives. This course will focus on analytical techniques to generate insights from data in such areas as market basket analysis, segmentation, targeting and positioning, digital marketing analytics including A/B testing, and conjoint models for product and pricing decisions.
Modeling Analytics in Operations
Managerial decisions in complex environments can be analyzed using quantitative models. This course introduces students to spreadsheet modeling, simulation, and optimization. Simulation models are helpful when making decisions under uncertainty, and optimization models deal with many decision variables that interact with each other through constraints. The application areas are diverse: airlines maximize their revenues by changing prices under demand uncertainty, hospitals improve quality of care by changing staffing levels, manufacturers minimize costs by reorganizing their supply chains to meet consumer demand. Students build models that generate insights for operations management problems and practice their skills in communicating such insights.
Increased customer digital connectivity and availability of massive amounts of data have enabled managers to use analytical tools to better adjust tactical decisions to the dynamic behaviors of individual customers. Companies rely on these adjustments to optimize customer relationships and sustain customer equity for strategic advantage. This course is designed to better understand the managerial and social contexts of customer analytics, and equips students with the skills required to use data for more effective customer relationship management. Students will gain an understanding of the types of customer data available in the marketplace and application of state of the art analytics techniques and tools to manage customers across their life cycle. The course includes analytics topics on customer behavior and preference including choice models, customer churn prediction, RFM analysis metrics and elements, and forecasting customer demand, among other customer relationship concepts and methods.
The objective of this course is to provide students with the tools for financial data analysis and discuss how to apply these tools to the emerging area of financial technology (Fintech). The focus will be on quantitative models for portfolio management, robo-advising, and financial analytics. The course also introduces students to emerging topics such as peer-to-peer lending, cryptocurrencies and blockchains, as well as regulatory issues (Regtech).
Visualization and Storytelling Data (opening residency)
Enormous amounts of data are created every day. Interpretation of descriptive statistics and statistical output is no longer sufficient for supporting robust, actionable business decisions. Instead, managers should rely on their strengths of understanding relationships, patterns, and potential insights from large data sets through proper visualization. This course covers basic ideas about cognition and data visualization, including how different types of data and relationships are best presented, how to communicate a coherent story with data, and how to ensure that the data accurately supports the story.
Using Data to Lead Change (opening residency)
In today’s highly interdependent, constantly changing world, organizations must continuously adapt to new situations to survive and thrive. The course introduces the process of leading successful change beginning with the clarity of purpose that comes from an understanding of customers’ needs. This involves leveraging data analytics to drive the design of products and services to meet those needs effectively. Data are collected and analyzed at every step of the process to 1) frame the case for change; 2) measure inputs, processes, and outputs; 3) assess the success of pilot studies and other activities; and 4) demonstrate the impact of the change program. In this course, students will learn how to use data to accurately conceptualize the change problem, measure process, and evaluate the outcomes.
Psychology of Big Data (mid-program residency)
“Big data” is changing the way organizations function and the way individuals make decisions. This course examines organizational behavior and decision-making research as massive data becomes a bigger part of organizations. In particular, the focus is on data science from a psychological perspective, its impact on how organizations function, and how leaders and managers make decisions. As organizations aggressively collect and analyze massive amounts of data, decision-makers are inundated with information they need to understand. This course examines the practical implications of big data on several major issues, including leveraging lessons from big data to solve existing organizational issues/problems, recognizing new organizational issues caused by the rise of big data, and collaborating across technical and non-technical colleagues to solve organizational problems using analytical insights derived from big data.
Research Design (mid-program residency)
A research design is a plan that includes processes, methods, and tools that provide objective data, information, and insights to help managers make optimal business decisions. The course introduces three general types of research designs, including exploratory, descriptive, and causal. Exploratory designs involve the collection of data through secondary (published) sources, in-depth interviews, focus groups, and ethnography. Descriptive designs involve survey research, including sampling procedures, definition and measurement of key variables, research instrument design, and preparing data for analysis. Causal research designs involve experiments that can test specific hypotheses about cause and effect, leading to confirmatory decisions.
Data Ethics and Privacy
The proliferation of data, cheap storage capacity, and powerful tools for extracting information from data cannot only enhance a firm’s bottom line but also benefit society at large. However, as with all advanced technologies, this comes with the potential of misuse. Adequate controls regarding the collection, storage, and use of data are needed to ensure that firms do not only achieve business success and comply with the law but also adhere to ethical norms. This course starts with providing a basic understanding of how domestic and international rules and regulations shape the legal framework within which firms use data analytics, and discusses why it is important for firms to go beyond these minimum requirements and adhere to their ethical obligations. Subsequently, the course explores how firms can implement data governance practices that help them make value-based data-driven decisions. Regarding the latter, the course discusses various techniques that help firms make data-driven decisions that are not only robust and explainable, but also free of human bias.
The Capstone Project is the peak intellectual learning experience for students in the Georgetown MSBA Program. The course is spread over two terms and applies the concepts, methods, and tools learned in the program to a challenging business analytics problem with a local, national, or global organization as the project sponsor. Student teams will put into practice the process of defining the organizational challenge, identifying and/or collecting necessary data, analyzing possible courses of action, and making a recommendation, culminating with a presentation to faculty and project sponsors.
The program includes two five-day residencies, each one occurring over a long weekend.* The residencies are designed to enhance learning on complex topics while giving you ample opportunities to network and form a deeper connection with faculty, students, and key administrators in the program. The first residency will introduce the program, the curriculum, its technology, the resources available to students, and two intensive courses. The second residency mid-way through the curriculum will include professional development activities in addition to two courses and an introduction to the Capstone Project and the sponsors.
*Please note, the MSBA program is following Georgetown University’s COVID-19 protocols, which also consider the requirements that the District of Columbia sets forth. While there may be some changes to the delivery format for the residency, students can expect an exceptional learning experience and dedication to connecting with peers and faculty.
MSBA Academic Calendar
The MSBA program consists of seven, 7-week modules, each with two courses. You will also participate in two five-day residencies where you will get oriented to Georgetown, meet your classmates and professors, earn credit through intensive coursework, and work toward your capstone project.
The first cohort of classes begins in January 2021.
Our faculty are deeply devoted to the cura personalis approach, or “care for the whole person,” in their teaching. You can expect individualized attention and respect for who you are and the experiences you bring to the classroom. Faculty bring considerable data analytics experience from multiple disciplines and industries, as well as a world-wide perspective.
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