Master's in Business Analytics Curriculum

Students entering the University of San Diego and/or declaring a major during 2018-2019, should follow information contained in the printed course catalog (also known as the "catalog of record") published on May 1, 2018. Access the catalog of record at http://catalogs.sandiego.edu.

MSBA 500, MSBA 501, MSBA 502, MSBA 504, MSBA 506, MSBA 503, MSBA 505, MSBA 507, MSBA 508, MSBA 509, MSBA 510, MSBA 511, MSBA 512, MSBA 599-01, MSBA 599-02, MSBA 599-03

MSBA 500: Introduction to Data Analytics & Business Concepts

Units: 2

This course introduces the data analytics process (acquisition, exploration, pre-processing, analysis, etc.) and concepts (data sources, structured vs. unstructured data, descriptive / prescriptive / predictive analytics). There will be an emphasis on formulating the problem and defining data needs and missing data issues. Students will develop key skills in Excel, including shortcut keys, text functions, date functions, logical functions, aggregate functions, if statements, vlookup, index match, nested statements, filters, pivot tables, and macros.

MSBA 501: Applied Statistics

Units: 2

This course examines how managers use data as the key input for systematic business problem-solving. Topics will include collecting data, describing and presenting data, probability, statistical inference, regression analysis, forecasting, and risk analysis. Special emphasis will be given to computer techniques, especially using Excel, for statistical analysis and problem solving. Students will gain experience with common business processes and business skills practiced such as gathering and organizing data, quantitative data analysis, forecasting, decision-making under uncertainty and communicating or presenting results.

MSBA 502: Analytics Programming I

Units: 3

This course will apply programming skills in Python for predictive analytics. Topics will include, but not be limited to, programming, repository management, preprocessing and cleaning data, documentation and reproducibility, machine learning, and validation. Students will understand how to clean a data set and apply a predictive algorithm using the edited data.

MSBA 504: Data Management

Units: 2

This course will provide students with an introduction to relational databases concepts, data warehousing, and Structure Query Language (SQL)

MSBA 506: Prescriptive Analytics

Units: 2

This course will provide students with the skills necessary to be able to approach decision making in a systematic manner. Using spreadsheets as the medium, this course will examine such topics as modeling, decision analysis tools, constrained optimization methods, and Monte Carlo simulation. This course will also provide students with experience using these tools in such areas as marketing, finance and operations.

MSBA 503: Analytics Programming II

Units: 2

This course is a continuation of Analytics Programming I with a focus on unstructured data and analytical tools, artificial intelligence and machine learning. Students will apply these tools using Python.

MSBA 505: Interactive Data Visualization

Units: 1

Building upon relational database concepts, this course will focus on the principles of effective data visualizations, and interactive data visualization using Tableau.

MSBA 507: Data for Social Good

Units: 2

This course will explore how data is used in non-profits, schools, granting agencies and social enterprises to further an organization's ability to address its triple bottom line. In this class, different analytics techniques will be explored to investigate social impact analysis, marketing analytics for nonprofits, donor data analytics, funding analytics for foundations and business efficiencies as they relate to the building, measuring and monitoring of social programming.

MSBA 508: Technical Communication

Units: 1

This course will teach students how to make data-driven presentations (written and oral). Students will learn the mechanics of slide design, the art of telling a data-driven story, and will practice presentation skills.

MSBA 509: Professional Seminar

Units: 1

This course will introduce students to analytics professionals. Activities will include panel discussions, site visits, and hands-on case studies.

MSBA 510: International Consulting Project

Units: 3

Students will work in teams to design and develop solutions to a business problem or strategic initiative for a company abroad. The project will provide hands-on experience of the people, markets, economic policies, and business practices of the country in which the company operates. Students will apply creativity and analytical tools to complete the project and communicate results to clients.

MSBA 511: Data Mining for Business Analytics

Units: 3

This course will develop students’ data literacy by discussing current data mining techniques and practices in business contexts. Topics include: market basket analysis, recommender systems and collaborative filtering, clustering and segmentation, classification (decision trees, neural networks, and logistic regression etc.), text analytics. For each of these techniques, the emphasis will be on developing the intuition with the aim of business application. The algorithmic details will be covered only to the extent necessary to understand when and how each technique can be used. Students will use IBM SPSS Modeler throughout this course to independently conduct data mining / predictive analytics projects.

MSBA 512: Capstone

Units: 3

Students will work in teams with an industry partner to refine a business problem, identify necessary data to solve the problem, and statistically analyze existing data to develop solutions to the business problem or strategic initiative. Using company-specific data students will utilize the programming, statistical, and business knowledge learned during the program to develop a solution to a business problem for the industry partner. Each team will have a faculty advisor and a key industry partner. Students will have interim presentations to the client and to classmates. Students will complete the project and communicate the results to clients via a presentation and a written report.

MSBA 599-01: Discipline - specific Class 1

Units: 3

See descriptions below

MSBA 599-02: Discipline -specific Class 2

Units: 3

See descriptions below

MSBA 599-03: Discipline -specific Class 3

Units: 3

See descriptions below

Accounting Analytics

Units: 3

This course will explore how financial statement data and nonfinancial metrics can be linked to financial performance. In this course, students will learn how data is used to assess what drives financial performance and to forecast future financial scenarios. While many accounting and financial organizations deliver data, accounting analytics deploys that data to deliver insight, and this course will explore the many areas in which accounting data provides insight into other business areas including consumer behavior predictions, corporate strategy, risk management, optimization, and more. By the end of this course, students will understand how financial data and non-financial data interact to forecast events, optimize operations, and determine strategy.

Forecasting / Applied Time Series Analysis

Units: 3

This course will examine the business cycle and techniques for forecasting fluctuations. The emphasis of the course will be to gain hands-on exposure to specific business forecasting techniques and learn to apply them to limit the range of uncertainty in management decision making. Specific techniques covered will include lead-lag, exponential smoothing, and econometric and ARIMA (Box-Jenkins) time series analysis.

Financial Analytics

Units: 3

This course will provide a detailed, hands-on examination of financial modeling techniques applied to optimal decision making in the areas of corporate finance and investment banking. Topics will include the construction of comprehensive valuation models (including capital structure and dividend policy modifications), use of precedent transactions and comparable companies in valuation, strategic industry examination, and application of valuation in the context of mergers and acquisitions. Students will make extensive use of Microsoft Excel.

Digitization and Agile Decision Making

Units: 3

This course will prepare students to understand the main elements of the digital transformation process and apply their knowledge to a case company. The course will cover academic and practitioner insights and frameworks relating to business analytics and digitization. This includes key data analytics tools, major decision support tools, data visualization, and digitization.

Customer Analytics

Units: 3

This course will take an applied, data driven, approach to understand how firms make various marketing decisions such as measuring the effectiveness of their promotions, pricing strategy, and market segmentation. Students will learn how different types of data and analytical methodologies can be used to solve these problems. Students will use IBM SPSS to apply regression, cluster analysis/factor analysis, and conjoint techniques.

Operations & Supply Chain Analytics

Units: 3

This course will develop advanced ability to use quantitative methods and Excel spreadsheet to build effective models for operational decisions. In the first part of the course, students will learn several analytical tools for operational decision making, including inventory management, demand forecast and capacity analysis. Students will apply those skills in a simulation to manage a company. In the second part of the course, students will focus on advance spreadsheet modeling that integrate optimization, simulation and decision analysis.