Texas McCombs MSBA
Curriculum
The MSBA curriculum is designed to let students complete their 36 credit hours and receive their STEM-eligible MSBA degree in just 10 months.
Keeping Curriculum Current
Capstone in Business Analytics
All MSBA students participate in the Business Analytics Capstone course. This unique course brings together database management, data analysis techniques, and business decision-making to solve a problem for a real-world client.

Summer Term

6 credit hours

Introduction to Machine Learning and Neural networks

This hands-on course introduces core machine learning concepts, algorithms, and applications, covering supervised and unsupervised learning, model evaluation, and overfitting. Real-world business examples are used to build a foundation for more advanced MSBA coursework.

3 Credits | Core

Data Science and agentic Programming

This course teaches the tools and programming skills needed to extract insights from business data, using Python and Pandas. Topics progress from introductory Python through data wrangling, visualization, classification, and clustering.

3 Credits | Core

Fall Term

Average of 15 credit hours taken in fall

Optimization for decision making

This is the first course in a two-course optimization sequence, covering quantitative techniques for decision-making in business contexts including finance, marketing, statistics, and revenue management. Topics include linear programming, integer programming, nonlinear programming, and neural networks, implemented in Python with Gurobi.

2 Credits | Core

Information Management

Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

3 Credits | Core

Analytics for Unstructured Data

Unstructured data – text, images, video, and voice – is everywhere, and yet businesses have started leveraging these newer forms of data only recently. This 2-credit hour course largely focuses on the analytics of text and images and their business applications. Starting with basics, students learn the cutting edge in natural language processing and computer vision analytics. All assignments and the final project are designed to apply technical concepts and principles to solving real-world problems and creating new opportunities. Specifically, students learn to:

  • Use Python to conduct analysis of text and images to improve business outcomes
  • Build text and image-based recommender systems
  • Derive insights about customers, brands, products, and features
  • Perform advanced sentiment analysis
  • Use generative models for text
  • Use computer vision to increase engagement in social media

2 Credits | Core

Supply Chain Analytics

Supply Chain Management (SCM) is the management of activities governing the flow and transformation of resources from initial suppliers to ultimate consumers to make goods and services available at the right time, place, price, and condition in the most profitable and cost-effective manner. In this course, we will consider analytics applied to important problems found in the management of supply chains. The first half of the course introduces the context for the application of analytics in operations. The second half of the course addresses the use of analytics in supply chain management.

3 Credits | Elective

Advanced DEEP learning and Machine Learning

This course builds upon introductory machine learning to study advanced predictive analytics techniques with particular emphasis on deep understanding of models and scalability to large datasets, using Python (Scikit-Learn and PyTorch). The central goal is to convey the pros and cons of different predictive modeling techniques for real-world business problems.

3 Credits | Core

Marketing Analytics I

This course introduces students to the data, models, and analytical techniques that businesses use to make marketing decisions, covering the full process of transforming data into actionable insights using Excel and R. Students work through five thematic units — understanding markets, market demand, data communication, customers, and opportunities.

3 Credits | Elective

Spring Term

Average of 15 credit hours taken in spring

Unsupervised Learning

Unsupervised statistical learning techniques and their role in creating actionable information. Measures of information, principal components analysis, factor analysis, cluster analysis, dimensionality reduction and related techniques.

 2 credits | Core

dynamic Optimization and reinforcement learning

This is the second course in a two-course optimization sequence, covering advanced quantitative techniques including neural networks (transformers), simulation, bandit problems, dynamic programming, and reinforcement learning. Applications span finance, marketing, statistics, and revenue management, with Python used throughout.

2 Credits | Core

Advanced Data Analytics in Marketing

Introduction to the data and tools used to analyze the business environment and enable marketing decision-making. Uses real-world data and problems to evaluate strategic market opportunities and assess the impact of marketing decisions in the marketplace. Discusses analytical and empirical tools that address strategic issues of market sizing, market selection, and competitive analysis, as well as product management, customer management, and marketing function management decisions.

3 Credits | Elective

Artificial Intelligence Ethics

The course will provide students with the tools to critically analyze the ethical implications of applying AI to business problems, and to quantitatively measure and mitigate risks such as algorithmic bias. Through the analysis of recent cases in revenue management, retail operations, human resources, and healthcare, students will learn to reason about AI ethics from a managerial perspective at the problem formulation stage. Furthermore, the course will build on students’ quantitative and programming skills, and train them in the emerging fields of algorithmic fairness and responsible AI.

2 Credits | Elective

Business Analytics Capstone

This industry-sponsored practicum course has students solve real-world analytics problems by applying skills acquired throughout the MSBA program on behalf of a business sponsor. Students develop skills across data modeling, functional business knowledge, technology, and analytical storytelling through iterative team-based project work.

3 Credits | Core

Demand Analytics/Pricing

Strategic problems, policies, models, and concepts for the design and control of new or existing operations systems.

3 Credits | Elective

Financial Technology

The course provides an overview of the most recent technological advances that are radically changing the financial services industry. Technological breakthroughs offer new ways for people to save, invest, borrow, and transact. We will analyze how new technologies create value in the financial industry, from reducing unit cost, increasing transparency, increasing competition, creating network effects, leveraging economies of scale, and lowering asymmetric information. We will also study the competitive landscape and the market opportunities and threats for incumbents and new entrants.

2 Credits | Elective

Social Media Analytics

This course is designed to showcase the virtually unlimited opportunities that exist today to leverage the power of social media. It focuses on a gamut of questions ranging from strategic to operational matters pertaining to a firm’s social media initiatives, metrics to capture relevant outcomes, and predictive analysis to link social media chatter to business performance.

1 Credit | Elective

Time Series Analysis

Survey of important time series models and methods. The two primary tasks of time series analytics: forecasting and explanation. Confirmatory models such as regression, random walks, autoregression, ARIMA, and state space. Exploratory methods such as neural nets, trees, random forests, and other ensemble methods.

2 Credits | Elective

Financial Analysis Elective Track
The MSBA Financial Analytics Elective Track has a distinctive focus that combines education in empirical methods in finance and advanced data science.

Students interested in pursuing the Financial Analytics elective track must have exposure to finance coursework. Those interested in pursuing the Financial Analytics elective track that have not had previous exposure to finance coursework are expected to complete a specific online course for admitted students, titled “Principles of Financial Analysis.” 

Summer Term

7 credit hours required in summer

Introduction to Machine Learning and neural networks

This hands-on course introduces core machine learning concepts, algorithms, and applications, covering supervised and unsupervised learning, model evaluation, and overfitting. Real-world business examples are used to build a foundation for more advanced MSBA coursework.

3 Credits | Core

Data Science and agentic Programming

This course teaches the tools and programming skills needed to extract insights from business data, using Python and Pandas. Topics progress from introductory Python through data wrangling, visualization, classification, and clustering.

3 Credits | Core

Intro to Finance Analytics

1 Credit | Elective

Fall Term

Average of 15 credit hours taken in fall

Advanced DEEP LEARNING and Machine Learning

This course builds upon introductory machine learning to study advanced predictive analytics techniques with particular emphasis on deep understanding of models and scalability to large datasets, using Python (Scikit-Learn and PyTorch). The central goal is to convey the pros and cons of different predictive modeling techniques for real-world business problems.

3 Credits | Core

Optimization for decision making

This is the first course in a two-course optimization sequence, covering quantitative techniques for decision-making in business contexts including finance, marketing, statistics, and revenue management. Topics include linear programming, integer programming, nonlinear programming, and neural networks, implemented in Python with Gurobi.

2 Credits | Core

Analytics for Unstructured Data

Unstructured data — text, images, video, and voice — is everywhere, and yet businesses have started leveraging these newer forms of data only recently. This 2-credit hour course largely focuses on the analytics of text and images and their business applications. Starting with basics, students learn the cutting edge in natural language processing and computer vision analytics. All assignments and the final project are designed to apply technical concepts and principles to solving real-world problems and creating new opportunities. Specifically, students learn to:

  • Use Python to conduct analysis of text and images to improve business outcomes
  • Build text and image-based recommender systems
  • Derive insights about customers, brands, products, and features
  • Perform advanced sentiment analysis
  • Use generative models for text
  • Use computer vision to increase engagement in social media

 

2 Credits | Core

Information Management

Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

3 Credits | Core

Spring Term

Average of 15 credit hours taken in spring

Unsupervised Learning

Unsupervised statistical learning techniques and their role in creating actionable information. Measures of information, principal components analysis, factor analysis, cluster analysis, dimensionality reduction and related techniques.

2 credits | Core

dynamic Optimization and reinforcement learning

This is the second course in a two-course optimization sequence, covering advanced quantitative techniques including neural networks (transformers), simulation, bandit problems, dynamic programming, and reinforcement learning. Applications span finance, marketing, statistics, and revenue management, with Python used throughout.

2 Credits | Core

Financial Modeling/Testing

3 Credits | Elective

Business Analytics Capstone

This industry-sponsored practicum course has students solve real-world analytics problems by applying skills acquired throughout the MSBA program on behalf of a business sponsor. Students develop skills across data modeling, functional business knowledge, technology, and analytical storytelling through iterative team-based project work.

3 Credits | Core

Financial Technology

The course provides an overview of the most recent technological advances that are radically changing the financial services industry. Technological breakthroughs offer new ways for people to save, invest, borrow, and transact. We will analyze how new technologies create value in the financial industry, from reducing unit cost, increasing transparency, increasing competition, creating network effects, leveraging economies of scales, and lowering asymmetric information. We will also study the competitive landscape and the market opportunities and threats for incumbents and new entrants.

2 Credits | Elective

Fixed Income Analysis

2 Credits | Elective

Supply Chain Management & Marketing Elective Track
The MSBA Supply Chain & Marketing Elective Track has a distinctive focus that combines education in Supply Chain and Marketing analytics.

Summer Term

6 credit hours required in summer

Introduction to Machine Learning and neural networks

This hands-on course introduces core machine learning concepts, algorithms, and applications, covering supervised and unsupervised learning, model evaluation, and overfitting. Real-world business examples are used to build a foundation for more advanced MSBA coursework.

3 Credits | Core

Data Science and agentic Programming

This course teaches the tools and programming skills needed to extract insights from business data, using Python and Pandas. Topics progress from introductory Python through data wrangling, visualization, classification, and clustering.

3 Credits | Core

Fall Term

Average of 15 credit hours taken in fall

Advanced DEEP LEARNING AND Machine Learning

This course builds upon introductory machine learning to study advanced predictive analytics techniques with particular emphasis on deep understanding of models and scalability to large datasets, using Python (Scikit-Learn and PyTorch). The central goal is to convey the pros and cons of different predictive modeling techniques for real-world business problems.

3 Credits | Core

Optimization for decision making

This is the first course in a two-course optimization sequence, covering quantitative techniques for decision-making in business contexts including finance, marketing, statistics, and revenue management. Topics include linear programming, integer programming, nonlinear programming, and neural networks, implemented in Python with Gurobi.

2 Credits | Core

Marketing Analytics I

This course introduces students to the data, models, and analytical techniques that businesses use to make marketing decisions, covering the full process of transforming data into actionable insights using Excel and R. Students work through five thematic units — understanding markets, market demand, data communication, customers, and opportunities.

3 Credits | Elective

Analytics for Unstructured Data

Unstructured data – text, images, video, and voice – is everywhere, and yet businesses have started leveraging these newer forms of data only recently. This 2-credit hour course largely focuses on the analytics of text and images and their business applications. Starting with basics, students learn the cutting edge in natural language processing and computer vision analytics. All assignments and the final project are designed to apply technical concepts and principles to solving real-world problems and creating new opportunities. Specifically, students learn to:

  • Use Python to conduct analysis of text and images to improve business outcomes
  • Build text and image-based recommender systems
  • Derive insights about customers, brands, products, and features
  • Perform advanced sentiment analysis
  • Use generative models for text
  • Use computer vision to increase engagement in social media

2 Credits | Core

Information Management

Explore various concepts of data management and develop expertise in managing data from the design and modeling of a database to data querying and processing. Learn big data storing principles that can be applied to various database products, such as Hadoop, Map Reduce, and Spark.

3 Credits | Core

Supply Chain Analytics

Supply Chain Management (SCM) is the management of activities governing the flow and transformation of resources from initial suppliers to ultimate consumers to make goods and services available at the right time, place, price, and condition in the most profitable and cost-effective manner. In this course, we will consider analytics applied to important problems found in the management of supply chains. The first half of the course introduces the context for the application of analytics in operations. The second half of the course addresses the use of analytics in supply chain management.

3 Credits | Elective

Spring Term

Average of 15 credit hours taken in spring

Unsupervised Learning

Unsupervised statistical learning techniques and their role in creating actionable information. Measures of information, principal components analysis, factor analysis, cluster analysis, dimensionality reduction and related techniques.

2 Credits | Core

dynamic Optimization and reinforcement learning

This is the second course in a two-course optimization sequence, covering advanced quantitative techniques including neural networks (transformers), simulation, bandit problems, dynamic programming, and reinforcement learning. Applications span finance, marketing, statistics, and revenue management, with Python used throughout.

2 Credits | Core

Advanced Data Analytics in Marketing

Introduction to the data and tools used to analyze the business environment and enable marketing decision-making. Uses real-world data and problems to evaluate strategic market opportunities and assess the impact of marketing decisions in the marketplace. Discusses analytical and empirical tools that address strategic issues of market sizing, market selection, and competitive analysis, as well as product management, customer management, and marketing function management decisions.

3 Credits | Elective

Business Analytics Capstone

This industry-sponsored practicum course has students solve real-world analytics problems by applying skills acquired throughout the MSBA program on behalf of a business sponsor. Students develop skills across data modeling, functional business knowledge, technology, and analytical storytelling through iterative team-based project work.

3 Credits | Core

Demand Analytics/Pricing

Strategic problems, policies, models, and concepts for the design and control of new or existing operations systems.

3 Credits | Elective

Time Series Analysis

Survey of important time series models and methods. The two primary tasks of time series analytics: forecasting and explanation. Confirmatory models such as regression, random walks, autoregression, ARIMA, and state space. Exploratory methods such as neural nets, trees, random forests, and other ensemble methods.

2 Credits | Elective

Social Media Analytics

This course covers the strategic, analytical, and technical aspects of leveraging social media data for business value, focusing on network analysis, influence measurement, community detection, and predictive modeling. Students develop expertise in analyzing social media chatter, building network-based models, and linking social media activity to business performance outcomes.

1 Credit | Elective

MSBA Faculty

Industry-led Curriculum