A STEM Designated Masters
The MSBA is a 50-unit, STEM designated degree program. The curriculum consists of four 4-unit core courses, two 1-unit core courses, and 32 units from a set of elective courses, as well as a mandatory Pre-term Orientation.
Required Core Courses
- MGTA 401: Professional Seminar, 1 unit
- MGTA 451: Business Analytics in Marketing, Finance, and Operations, 4 units
- MGTA 452: Collecting and Analyzing Large Data, 4 units
- MGTA 453: Business Analytics, 4 units
- MGTA 454: Business Analytics Capstone Project, 4 units
- MGTA 455: Customer Analytics, 4 units
Business Analytics Electives (a minimum of 16 units required):
- MGTA 402: Data-Driven Communications, 2 units
- MGTA 414: Web Data Analytics, 2 units
- MGTA 415: Analyzing Unstructured Data, 4 units
- MGTA 456: Supply Chain Analytics, 4 units
- MGTA 457: Business Intelligence Systems, 2 units
- MGTA 458: Experiments in Firms, 4 units
- MGTA 459: Managerial Judgment and Decision Making, 4 units
- MGTA 460: Business Analytics Project Management, 2 units
- MGTA 461: Recommender Systems, 4 units
- MGTA 462A: Big Data Technology & Business Application, 2 units
- MGTA 462B: Big Data Technology & Business Application, 2 units
- MGTA 463: Fraud Analytics, 4 units
- MGTA 479: Pricing Analytics, 4 units
- MGTA 495: Special Topics in Business Analytics courses, 2-4 units
- Additional courses may become available
Electives in Addition to Business Analytic Courses (up to 12 units permitted):
- CSE 250B: Principles of Artificial Intelligence: Learning Algorithms, 4 units
- CSE 253: Neural Networks for Pattern Recognition, 4 units
- CSE 256: Statistical Natural Language Processing, 4 units
- MGT 451: Technology and Innovation Strategy, 4 units
- MGT 475: Research for Marketing Decisions, 4 units
- MGT 477: Consumer Behavior, 4 units
- MGT 489: E-commerce, 4 units
- MGTF 405: Business Forecasting, 4 units
- MGTF 406: Behavioral Finance, 4 units
- Additional courses may become available
- MGTA 454: Business Analytics Capstone Project (4)
Students will pursue their capstone experience as part of a team to solve problems companies are facing. Each team will work as external consultants on a project to create value for a client company.
Performance on the capstone project will be used as a primary measure of a student’s learning in the MSBA Program. Each student will be evaluated based on his/her grasp of the course material and his/her ability to apply the course material to the capstone project. Satisfactory completion of the Capstone Project is required to obtain the MSBA degree.
Each student must successfully pass a comprehensive examination. This examination includes (i) a formal presentation of the capstone project, (ii) a written report, and (iii) an individual oral examination. The capstone project will require students to complete a project that solves a business problem for a real-world client and document their work in a written report.
Rady organizes a mandatory Pre-term Orientation for all incoming students. During this time, students will attend workshops about academic requirements, career services, and graduate student life, as well as refresher and introduction courses on various topics relating to the MS Business Analytics program, such as Python, R, Math, and Statistics. Attendance is mandatory and exact dates will be posted approximately 1-2 quarters before admission.
MGTA 401: Professional Seminar (1)
Discussion series where domain experts and business leaders present up-to-date research, discuss industry trends, and provide professional skills development. S/U grades only. May be taken for credit two times. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 402: Data-Driven Communications (2)
This project-based course explores and develops applied communication skills needed for analytical professions to make the business case. Students work individually and in teams to develop persuasive messaging to communicate complex technical information and business insights to, and between, technical experts and business decision-makers.
MGTA 414: Web Data Analytics (2)
This course focuses on collecting, drawing inference, and making business decisions based on data. It will cover tools – Python, APIs, and NLP – to collect, manipulate, and analyze data from the web and other sources, with the objective of making students data savvy and comfortable with deriving insights from real-world, large datasets. Students will be exposed to the power of clickstream analysis and the possibilities that can be unleashed from industry applications of web data analytics.
MGTA 415: Analyzing Unstructured Data (4)
Unstructured data such as text, images and video are routinely collected by companies and other organizations. The complex nature and scale of data such as this requires specialized analytics frameworks. In this course we discuss and use tools that empower decision makers to extract actionable information from unstructured data.
MGTA 451: Business Analytics in Marketing, Finance, and Operations (4)
Business analytics projects should strive to create substantial value to an organization by solving impactful business problems. In this class, students will learn to identify business opportunities in the substantive areas of marketing, finance, and operations. Prerequisites: restricted to master’s of business analytics students or with department and instructor approval.
MGTA 452: Collecting and Analyzing Large Data (4)
This course aims to provide students with an introduction to data analytics in R where the emphasis is more on business “data” than “analytics.” The emphasis is on collecting, handling, manipulating, and summarizing large data sets. Case studies are used throughout the course. Prerequisites: MGTA 451 and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 453: Business Analytics (4)
This course is designed to help a business manager to use data to make good decisions in complex decision-making situations. Several analytical and econometric methods will be covered including decision analysis, regression analysis, optimization, and simulation. Prerequisites: MGTA 451 and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 454: Business Analytics Capstone Project (4)
Students will pursue their capstone experience as part of a team to solve practical business problems faced by companies. Each team will work as external consultants on a project to create value for a client company. May be taken for credit two times for a maximum of four units. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 455: Customer Analytics (4)
Customer Analytics focuses on the use of data, statistics, and machine learning to create, develop, and maintain _exchanges_ with individual customers. Many firms have extensive information about customers' behavior. However, few firms have the people or expertise to act intelligently on such information. In this course, you will learn the modern analytics driven approach to marketing and will gain the hands-on experience required to collect, analyze, and act on customer data.
MGTA 456: Supply Chain Analytics (4)
This course focuses on improving the performance of production and service operations, as well as supply chains, through the combination of data and analytical tools including statistics, forecasting, and optimization. Students will learn to employ analytics in capacity and distribution facility planning and contracting; how to determine data collection requirements for dynamic management of inventory levels; and how to improve revenue management under demand learning. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 457: Business Intelligence Systems (2)
In this course we will cover the principles of designing and building dashboards and data visualizations. We will leverage Tableau's library of resources to demonstrate best practices for data visualization and data storytelling.
MGTA 458: Experiments in Firms (4)
Students will learn to design and run experiments to guide policy and business decisions. They will learn to distinguish between a correlation and a causal effect and also to critically evaluate causal claims based on nonexperimental data. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 459: Managerial Judgment and Decision-Making (4)
Decision making is difficult with scarce resources, inexact information, and conflicting stakeholder agendas (i.e., most of the time). Students will use advanced behavioral research to gain a competitive advantage, by improving their decisions and their understanding of how others decide. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 460: Business Analytics Project Management (2)
Many projects fail to produce valuable results, are over budget, or not finished on time. This course will teach students how to set and manage goals and expectations of the team and of the executive sponsors for business analytics projects. Prerequisites: MGTA 451, MGTA 452, MGTA 453, and restricted to master’s of business analytics students or with department and instructor approval.
MGTA 461 (CSE 258): Web Mining and Recommender Systems (4)
This course is devoted to current methods for recommender systems, data mining, and predictive analytics. No previous background in machine learning is required, but all participants should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra.
For additional information see: http://cseweb.ucsd.edu/classes/fa19/cse258-a/
MGTA 462A: Big Data Technology & Business Application (2)
Analyzing large-scale and complex data is an integral part of many business applications today. This course is designed to provide students with the skills of handling large volumes of data through database systems. The students will learn fundamental operations like data cleaning and the extract-transform-load (ETL) process that collects data from external sources into a database, advanced levels of SQL to perform complex queries as well as in-database analytics, and principles of data warehouse design for enterprise-level data analytics.
MGTA 462B: Big Data Technology & Business Application (2)
Analyzing large-scale and complex data is an integral part of many business applications today. This course is designed to provide students with skills and knowledge to perform analytics at scale. Students will learn the steps in the analytics process used to gather, prepare, process, and evaluate data. Skills and technologies to perform scalable analytics on big data using distributed computing and cloud-based tools will be the focus of the course. Hands-on experience will be provided with the Spark distributed platform and AWS cloud-based analytics. Through assignments and a project, students will apply the tools and techniques they learn in the course to leverage big data.
MGTA 463: Fraud Analytics (4)
In this course you will learn everything you need to know to build statistical/machine learning models to find fraud in data events. We will cover the basics of building fraud models: understanding the problem dynamics, modes of fraud, data analysis and preparation, building expert variables/features, different algorithm choices, and measurements of model goodness. There will be five homework problems and two group projects, with group sizes typically 4 to 6 people. We will talk through in detail several examples fraud problems and solutions, including tax fraud, application identity fraud, healthcare claims fraud, and credit card payment fraud. We will cover the topic of statistical modeling with a drill-down into all steps to building machine learning models. There will be a high-level discussion of a wide variety of statistical modeling methods, including linear/logistic regressions, clustering, neural nets, trees, SVM, random forests, deep learning, boosting and bagging. The overall fraud solution emphasis will be on creating expert variables/features which are the key to building successful fraud algorithms. The class will go into detail on how to build both supervised and unsupervised fraud models. When we have records that are known to be fraudulent (labeled as fraud) then we can usually build supervised models to predict new fraud going forward. In many cases we don’t have labeled fraud records, meaning we don’t have any or sufficient specific examples of fraud events from the past. In this case we must build unsupervised fraud models, where we typically look for anomalous records that are unusual in some important way. Other topics covered will be forensic accounting vs. real-time fraud algorithms, encoding of difficult fields, missing data values, categorical variables, variable scaling, fuzzy matching/linking, Benford’s law, and preparing data quality reports. The two projects to be completed are (1) finding anomalies/potential fraud in a collection of past credit card transactions and (2) building a real-time credit card transaction fraud scoring model. The first project is an unsupervised forensic accounting problem and the second project will be supervised, where we can evaluate the model performance and estimate the business impact. The data set is real and comes from an open dataset of government payment cards. For each of the two projects the teams will build the fraud algorithms and prepare a presentation and a proper business report, which can then be useful tools for future job interviews. Students are expected to have good backgrounds in math and statistics, and a working ability in any modern programming language that can interact with open source machine learning algorithms, such as R or Python.
MGT 475: Research for Marketing Decisions (4)
Marketing research is the process by which firms acquire and synthesize information about their customers and competitors. Managers use marketing research in the face of uncertain decisions so they can take actions that will lead to better outcomes. This course has a particular emphasis on information acquisition in the digital and automated age and statistics will feature prominently. Equally emphasized in this course is the development of skills to take in information and translate it to a sensible business strategy. Methods taught and applied to marketing research include hypothesis testing, clustering, factor analysis, multi-dimensional scaling, causal inference with panel data, A/B testing, and experimental design.
MGTA 479: Pricing Analytics (4)
This class covers the key drivers for making data-driven pricing decisions. Price setting is one of the most crucial of all tactical marketing decisions. It involves a detailed understanding of both supply-side (e.g. costs) and demand-side factors (e.g., consumer willingness to pay). In this course, we will approach the pricing decision as an intersection of economic, strategic, and behavioral considerations. We will cover the data required and analytical techniques to make optimal pricing decisions. We will cover tools and techniques for primary (e.g., survey) data as well as for secondary data (e.g., POS scannner data). The course will consist of lectures, where we will discuss the theory of pricing, and in-class labs, where we use data to determine the appropriate price to charge. The class will be heavily analytical and a good working knowledge of R, an understanding of regressions, and predictive modeling are required.
MGTA 495: Special Topics in Business Analytics courses (2-4)
The increasing availability of data has the potential to create vast financial and social benefits, but could also harm interests in areas such as privacy and discrimination. This course is designed to introduce students to the legal, policy, and ethical issues that arise from the collection, aggregation, use, and analysis of data, and examines responses to address these unintended consequences. Assignments will be both written, and project-based, and the course will discuss examples from real world controversies.
MGT 451: Technology and Innovation Strategy (4)
Outlines tools for formulating and evaluating technology strategy, including an introduction to the economics of technical change, models of technological evolution, and models of organizational dynamics and innovation. Provides an understanding of how technology firms gain and sustain competitive advantage. Students may not earn credit for both MGT 451 and MGT 271. They are course equivalents. Prerequisites: admission to MBA program or consent of instructor.
MGT 477: Consumer Behavior (4)
The course identifies the factors that influence the selection and usage of products and services. Students will be introduced to problems/decisions that include evaluating behavior; understanding the consumers’ decision process, and strategies to create desirable consumer behavior. Students may not earn credit for both MGT 477 and MGT 203. They are course equivalents. Prerequisites: admission to MBA program or consent of instructor.
MGT 489: E-commerce
In this class students will create prototypical e-commerce businesses. We proceed through market selection, sourcing, customer and market research, differentiation, positioning, branding, store design, user experience testing, pricing, social media, content marketing and digital advertising. Data-driven decisionmaking is emphasized at every stage.
MGTF 405: Business Forecasting (4)
Introduction to state-of-the-art forecasting methods in finance. Students will learn to estimate forecasting models based on past values of the predicted variable(s), surveys, market information, and other economic data. Participants will become critical consumers of forecasts reported in the media. Letter grades only. Prerequisites: restricted to master of finance program, MBA program, or by consent of instructor.
MGTF 406: Behavioral Finance (4)
Develop theories of behavior motivated by psychology to describe various features of financial markets. Examine how the insights from behavioral finance complement the traditional paradigm and shed light on investors’ trading patterns, the behavior of asset prices, and corporate finance. Letter grades only.
MGTF 415: Collecting and Analyzing Financial Data (4)
Teaches students how to obtain and process data in order to answer empirical questions in finance. The data can be numerical or textual, and structured or unstructured. Specific data sources may include CRSP, Compustat, Thomson Reuters, and Bloomberg.
CSE 250B: Principles of Artificial Intelligence: Learning Algorithms (4)
This course covers algorithms for supervised and unsupervised learning from data. Content may include maximum likelihood, log-linear models including logistic regression and conditional random fields, nearest neighbor methods, kernel methods, decision trees, ensemble methods, optimization algorithms, topic models, neural networks and backpropagation.
CSE 253: Neural Networks for Pattern Recognition (4)
Neural networks have come back into fashion recently with the advent of deep networks, which are winning all of the most important computer vision contests, and have also been used in a number of other pattern-recognition and pattern transformation problems, and have become the method of choice in reinforcement learning. In this course, we begin with the fundamentals of neural networks: We introduce Perceptrons, linear and logistic regression, multilayer networks and back-propagation, convolutional neural networks, recurrent networks, and deep networks.
CSE 256: Statistical Natural Language Processing (4)
Natural language processing (NLP) is a field of AI which aims to equip computers with the ability to intelligently process natural (human) language. This course will explore statistical techniques for the automatic analysis of natural language data. Specific topics covered include: probabilistic language models, which define probability distributions over text sequences; text classification; sequence models; parsing sentences into syntactic representations; machine translation, and machine reading.