Texas McCombs MSBA Program: Complete Overview

Rankings and Reputation

The Texas McCombs School of Business, which is part of the University of Texas at Austin, is well-regarded and has a strong reputation in the business education community. It consistently ranks among the top business schools in the United States. Texas McCombs is known for its rigorous curriculum, accomplished faculty, and strong emphasis on experiential learning opportunities. 

The Texas McCombs MSBA program was ranked 8th worldwide by QS World University Rankings. Texas McCombs MSBA program had a solid reputation and was highly regarded in the field of business analytics. It was consistently ranked among the top business analytics programs in the United States. 

The program’s reputation was bolstered by its strong faculty, innovative curriculum, and its connections with the industry. Additionally, the school’s specialized programs, such as the Master of Science in Finance and the Master of Science in Technology Commercialization, have received recognition for their quality and relevance in their respective fields.

Curriculum

In 10 months, students can complete the 36 credit hours required for the Texas McCombs MSBA Curriculum and graduate with a STEM-eligible MSBA degree.

General MSBA Curriculum

Here is a detailed insight on the General MSBA Curriculum:

Summer Term (6 credit hours)

• Introduction to Machine Learning

Selected themes in the use of data science to business problems are covered in this core course. Regression analysis, including linear, logistic, and multinomial regression; tree models for regression and classification; model building and validation concepts, such as the bias-variance trade-off, cross-validation, and variable selection; fundamental data manipulation and data visualization; factor models, including principal components analysis and partial least squares regression; clustering; networks; and text are among the topics covered.

• Data Science Programming

A subtle revolution in business has been brought about by data-driven analysis. Companies have started keeping thorough logs of inventory levels, sales, and consumer behavior among other things as disc storage and computing power have grown more affordable. However, this just completes the task; the actual need is for insights, which this core course equips you with. Python and Pandas are used in this class.

Fall Term (15 credit hours)

• Optimization I

The optimisation techniques that aid in decision-making are covered in this core course. A wide range of pertinent quantitative decision-making strategies will be covered. Each method will be introduced, examined, and motivated using significant applications.

Some pertinent theories will also be covered. But issue formulation and resolution will be the main emphasis. Linear, quadratic, nonlinear, and integer programming are some of the specific subjects and methods covered. Python will be used extensively in the course.

• Financial Management

Inputs into the design of cost systems, maximizing shareholder value through investment and financing decisions, and using the time value of money to value projects, bonds, stocks, and an entire company are all topics covered in this core course.

• Information Management

Explore numerous data management principles in this core course and gain experience in handling data, from database architecture and modeling to data querying and processing. Learn the fundamentals of large data storage that can be used with a variety of database tools, including Hadoop, Map Reduce, and Spark.

• Advanced Machine Learning

The study of several machine learning methods for predictive analytics will be covered in this core course. A lot of these skills are necessary for handling BIG DATA. By using case studies, connections to pertinent business issues will be established. Most of the training will be conducted in Python, particularly Scikit-Learn.

The main objective of this course is to provide knowledge of the advantages and disadvantages of various predictive modeling approaches. This objective will be emphasized by both classroom instruction and practical application.

• Analytics for Unstructured Data

This core course mainly focuses on the business applications of text and image analytics. Students explore the most recent developments in computer vision analytics and natural language processing starting with the fundamentals. The goal of each assignment and the final project is to use technological concepts and principles to address problems in the real world and open up new opportunities.

• Marketing Analytics I

Introduction to marketing strategy is covered in this elective course, which also covers company, customer, competitive analysis, segmentation, targeting, and positioning, as well as the marketing mix and mix response analysis.

• Supply Chain Analytics

You will study the use of analytics to solve significant supply chain management issues in this elective course. The backdrop for the use of analytics in operations is introduced in the course’s first half. The application of analytics in supply chain management is covered during the second half of the course.

Spring Term (15 credit hours)

• Unsupervised Learning

Unsupervised statistical learning methods and their function in producing useful information are covered in this core course. Information measures, principal component analysis, factor analysis, cluster analysis, dimensionality reduction, and other related approaches are also offered.

• Business Analytics Capstone

In order to answer a client’s business problem, this core course examines the fundamentals of business analytics in relation to database management, data analysis methods, and corporate decision-making.

• Optimization II

This required course covers decision-supporting optimisation techniques. It covers a wide range of pertinent quantitative methods for making decisions in the face of uncertainty. Each technique will be explained, together with some pertinent theory, and will be motivated through significant applications.

The formulation and resolution of problems will be the main focus, nevertheless. Advanced simulation approaches, stochastic programming, dynamic programming, and reinforcement learning are some of the specific subjects and methodologies covered. Python will be used extensively in this course.

• Demand Analytics/Pricing

This elective course focuses on the design and control of new or current operational systems, including strategic problems, policies, models, and concepts.

• Advanced Data Analytics in Marketing

The information and resources needed to analyze the business environment and facilitate marketing decision-making are introduced in this elective course. It assesses strategic market prospects and the effects of marketing initiatives in the marketplace using real-world facts and issues.

The course covers analytical and empirical approaches for managing product, customer, and marketing function decisions as well as strategic challenges of market sizing, market selection, and competition analysis.

• Financial Technology

The most recent technological developments that are fundamentally altering the financial services sector are covered in this elective course. Technological innovations give people new options for saving, investing, borrowing, and conducting business.

This course will examine the ways in which new technologies add value to the financial sector by lowering unit costs, raising transparency, boosting competitiveness, generating network effects, using economies of scale, and reducing asymmetry in information. Additionally, it will research the market’s prospects and risks for both established players and fresh competitors.

• Data Driven Health Care Operations

This elective course will concentrate on the major operational concerns as well as how data can be used to enhance operations. Hospital services, physician services, and prescription medications make up the three major subcategories of US health care spending.

The operational issues that arise in hospitals, outpatient clinics, drug discovery, and production are the main topics of this course. It will look at a variety of business scenarios and typical data sets within each area. R will be used in the course as a supplementary tool. But prior R experience is not required, and finishing any homework is not expected.

• Social Media Analytics

This elective course aims to demonstrate the practically limitless ways in which social media can be used nowadays. It focuses on a variety of issues, including metrics to record pertinent outcomes and predictive research to connect social media buzz to company performance. These issues range from strategic to operational issues connected to a firm’s social media endeavors.

• Time Series Analysis

An overview of significant time series models and techniques is provided in this elective course. Forecasting and explanation are the two main functions of time series analytics that are covered. Regression, random walks, autoregression, ARIMA, and state space are examples of confirmatory models that are discussed. There is also discussion of exploratory techniques including neural nets, trees, random forests, and other ensemble methods.

Financial Analytics Elective Track

The Financial Analytics Elective Track has a unique focus that blends training in sophisticated data science with instruction in empirical approaches in finance. Students that are interested in this Program need to have taken finance courses.

The free online course “Principles of Financial Analysis” is required of accepted candidates who are interested in this track but have not previously taken any finance coursework. Prior to the start of the Program, this course will be offered during the first part of the summer session.