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.
Business Analytics Options
General MSBA Curriculum
Financial Analysis Elective Track
Supply Chain & Marketing Elective Track
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

Selected topics in the applications of data science to business problems. Topics include regression analysis, including linear, logistic, and multinominal regression; tree models for regression and classification; concepts surrounding model building and model validation, including the bias-variance trade-off, cross-validation, and variable selection; basic data wrangling and data visualization; factor models, including principal components analysis and partial least squares regression; clustering; networks; and text.

3 Credits | Core

Data Science Programming

Data-driven analysis has wrought a quiet revolution in business. As disk storage and computing power have become cheaper, companies have started maintaining detailed logs of inventories, sales, and customer activity, among others. Yet, this is only half the job; the real need is for insights, and this course teaches you the tools for that. This course uses Python & Pandas.

3 Credits | Core

Fall Term

Average of 15 credit hours taken in fall

Optimization I

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include linear, quadratic, nonlinear, and integer programming. The course will use python extensively.

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

Financial Management

Recent advances in cost accounting, inputs into the design of cost systems, maximizing shareholder value through the investment decision and the financing decision; time value of money to value projects, bonds, stocks, and an entire firm.

3 Credits | Core

Advanced Machine Learning

This course will involve the study of a variety of machine learning techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data. Many of these capabilities are essential for handing BIG DATA. Connections to relevant business problems shall be made via example studies. We will mostly be using Python (especially Scikit-Learn). The central goal of this course is to convey an understanding of the pros and cons of different predictive modeling techniques so that you can (i) make an informed decision on what approaches to consider when faced with real-life problems requiring predictive modeling, (ii) apply models properly on real datasets so to make valid conclusions. This goal will be reinforced through both theory and hands-on experience.

3 Credits | Core

Marketing Analytics I

Introduction to marketing strategy; topics include company, customer, competitive analysis, segmentation, targeting and positioning, the marketing mix, and mix response analysis.

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

Optimization II

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making under uncertainty. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include advanced simulation methods, stochastic programming, dynamic programming, and reinforcement learning. The course will use Python extensively.

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

Data Driven Health Care Operations

Federal and state legislatures’ efforts to reign in health care costs have created strong incentives for service providers to reduce costs and improve quality. Many have invested in IT infrastructure that makes operations data available in digital form. However, many service providers lack the expertise to apply analytic methods to this data to bring about a transformation in health care. This class will focus on key operations challenges and on learning how data may be leveraged to improve operations.

The three largest categories for US health care expenditures are hospital services, physician services, and prescription drugs. Therefore, this class focuses on operational challenges arising in outpatient clinics, hospitals, and drug development and production. Within each category, we will examine a combination of business cases and representative data sets. The class will utilize R as a supporting tool. However, prior knowledge of R is not assumed, nor is it necessary for completing any assignments.

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

Explores foundations of business analytics related to database management, data analysis techniques, and business decision-making to solve a business problem of a client.

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

Selected topics in the applications of data science to business problems. Topics include regression analysis, including linear, logistic, and multinominal regression; tree models for regression and classification; concepts surrounding model building and model validation, including the bias-variance trade-off, cross-validation, and variable selection; basic data wrangling and data visualization; factor models, including principal components analysis and partial least squares regression; clustering; networks; and text.

3 Credits | Core

Data Science Programming

Data-driven analysis has wrought a quiet revolution in business. As disk storage and computing power have become cheaper, companies have started maintaining detailed logs of inventories, sales, and customer activity, among others. Yet, this is only half the job; the real need is for insights, and this course teaches you the tools for that. This course uses Python & Pandas.

3 Credits | Core

Intro to Finance Analytics

1 Credit | Elective

Fall Term

Average of 15 credit hours taken in fall

Advanced Machine Learning

This course will involve the study of a variety of machine learning techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data. Many of these capabilities are essential for handing BIG DATA. Connections to relevant business problems shall be made via example studies. We will mostly be using Python (especially Scikit-Learn). The central goal of this course is to convey an understanding of the pros and cons of different predictive modeling techniques so that you can (i) make an informed decision on what approaches to consider when faced with real-life problems requiring predictive modeling, (ii) apply models properly on real datasets so to make valid conclusions. This goal will be reinforced through both theory and hands-on experience.

3 Credits | Core

Optimization I

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include linear, quadratic, nonlinear, and integer programming. The course will use python extensively.

2 Credits | Core

Adv. Corp. Fin./Investments

6 Credits | FA 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

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

Optimization II

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making under uncertainty. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include advanced simulation methods, stochastic programming, dynamic programming, and reinforcement learning. The course will use Python extensively.

2 Credits | Core

Financial Modeling/Testing

3 Credits | Elective

Business Analytics Capstone

Explores foundations of business analytics related to database management, data analysis techniques, and business decision-making to solve a business problem of a client.

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

Selected topics in the applications of data science to business problems. Topics include regression analysis, including linear, logistic, and multinominal regression; tree models for regression and classification; concepts surrounding model building and model validation, including the bias-variance trade-off, cross-validation, and variable selection; basic data wrangling and data visualization; factor models, including principal components analysis and partial least squares regression; clustering; networks; and text.

3 Credits | Core

Data Science Programming

Data-driven analysis has wrought a quiet revolution in business. As disk storage and computing power have become cheaper, companies have started maintaining detailed logs of inventories, sales, and customer activity, among others. Yet, this is only half the job; the real need is for insights, and this course teaches you the tools for that. This course uses Python & Pandas.

3 Credits | Core

Fall Term

Average of 15 credit hours taken in fall

Advanced Machine Learning

This course will involve the study of a variety of machine learning techniques for predictive analytics. Particular emphasis will be given to approaches that are scalable to very large data sets and/or those that are relatively robust when faced with a large number of predictors, and algorithms for heterogeneous or streaming data. Many of these capabilities are essential for handing BIG DATA. Connections to relevant business problems shall be made via example studies. We will mostly be using Python (especially Scikit-Learn). The central goal of this course is to convey an understanding of the pros and cons of different predictive modeling techniques so that you can (i) make an informed decision on what approaches to consider when faced with real-life problems requiring predictive modeling, (ii) apply models properly on real datasets so to make valid conclusions. This goal will be reinforced through both theory and hands-on experience.

3 Credits | Core

Optimization I

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include linear, quadratic, nonlinear, and integer programming. The course will use python extensively.

2 Credits | Core

Financial Management

Recent advances in cost accounting, inputs into the design of cost systems, maximizing shareholder value through the investment decision and the financing decision; time value of money to value projects, bonds, stocks, and an entire firm.

3 Credits | Core

Marketing Analytics I

Introduction to marketing strategy; topics include company, customer, competitive analysis, segmentation, targeting and positioning, the marketing mix, and mix response analysis.

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

Optimization II

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making under uncertainty. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include advanced simulation methods, stochastic programming, dynamic programming, and reinforcement learning. The course will use Python extensively.

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

Explores foundations of business analytics related to database management, data analysis techniques, and business decision-making to solve a business problem of a client.

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 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

MSBA Faculty
Dr. Carlos Carvalho
Dr. Carlos Carvalho
Professor
View Carlos' Profile
Dr. Deepayan Chakrabarti
Dr. Deepayan Chakrabarti
Assistant Professor
View Deepayan's Profile
Dr. Jonathan Cohn
Dr. Jonathan Cohn
Associate Professor
View Jonathan's Profile