The Master of Science in Business Analytics (MSBA) degree is a professional degree for students wishing to pursue management careers with strong quantitative and data analysis training. Modern management professionals and business data analysts increasingly need significant mathematical, statistical and computational knowledge to understand and manage data available to business and government enterprises, and to utilize that understanding in making optimal quantitative decisions using mathematical models. The MSBA program is structured to provide and build not only mathematical and statistical skills such as quantitative modeling, operations management, data mining and simulation and relevant computational skills such as big data, network and infrastructure management, but also business and managerial skills and domain knowledge to apply the technical skills in a business environment.
Data Models and Decisions (3)
Database Management Systems (3)
Decision Analytics (3)
Data Mining & Predictive Analytics (3)
Operations Analytics (2)
Data Processing and Analysis in Python (3)
Big Data and Artificial Intelligence for Business (3)
Price Optimization and Revenue Management (3)
Capstone Project in Operations Analytics (3)
Google Online Analytics Challenge (3)
Business Communication (1)
BUDT733: Data Mining & Predictive Analytics
Data mining techniques and their use in business decision making. A hands-on course that provides an understanding of the key methods of data visualization, exploration, classification, prediction, time series forecasting, and clustering.
BUDT758Y: Database Management Systems
Introduction to the conceptual and logical design of relational database systems and their use in business environments. Topics include information modeling and optimization via normalization; Structured Query Language (SQL); Client/Server architectures; Concurrency & Recovery; Data Warehousing.
BUSI758B: Data Models and Decisions
Explores basic analytical principles that can guide a manager in making complex decisions. A good decision uses sound reasoning and takes into account all of the relevant information that is available at the time the decision is to be made. In order to arrive at a good decision, a manager must be able to:
- Identify an underlying analytical structure in a seemingly complex and amorphous decision problem
- Understand the role of uncertainty and risk in the decision-making process
- Analyze available data to understand relationships among variables and to create predictions
- Understand the trade-offs involved in the decision
- Use available computing technology (e.g., spreadsheets) to arrive at optimal solutions.
BUDT 758P: Decision Analytics
This course explores basic analytical principles that can guide a manager in making complex decisions. It focuses on two advanced analytics techniques: optimization, dealing with design and operating decisions for complex systems, and simulation, dealing with the analysis of operating decisions of complex systems in an uncertain environment. The course provides students with a collection of optimization and simulation modeling and solution tools that can be useful in a variety of industries and functions. The main topics covered are linear, integer, and nonlinear optimization applications in a wide variety of industry segments, and Monte-Carlo Simulation and risk assessment. Application-oriented cases are used for developing modeling and analytical skills, and to simulate decision-making in a real-world environment.
BUDT758B: Big Data and Artificial Intelligence For Business
Big data represents unprecedented opportunities for companies to generate insights and create wealth. Huge amount of data is being generated. At the same time, much of the big data is unstructured, in real time and only loosely connected. It defies the traditional ways of managing databases. This creates challenges even to tech-savvy companies on how to leverage the big data to gain competitive advantages. Challenges and opportunities coexist. To extract the great value from the data, we should be equipped with advanced techniques. Artificial Intelligence (AI) is penetrating our daily routines deeply, and shows great promise in exciting areas such as healthcare and autonomous driving cars.
This course uses a hands-on, learning-by-doing approach to understanding the concepts behind Big Data and AI, the strategic drivers of these technologies and the value propositions that they provide to industries. In addition, the course will also serve as an introduction to some of the key technologies within this ecosystem, such as Hadoop, AWS, Pig, Hive, Amazon Web Services and Spark. Examples of AI using Deep Learning will be conducted in class. The focus is on creating awareness of the technologies, allowing some level of familiarity with them through hands-on exercises and projects, and enabling strategic thinking around the use of these technologies in business.
BUDT758A: Business Communications
A study of the standards of business conduct, morals and values as well as the role of business in society. Students will consider the sometimes conflicting interests of and claims on the firm and its objectives.
BUDT 758W: Capstone Project in Operations Analytics
This course gives students an opportunity to apply the knowledge and skills they learned in the program on real world operational data through quantitative analysis with use of statistical models and the application of modeling and optimization techniques. Students form teams of 4-5 members and pursue an operational improvement project under the supervision of the instructor. The project groups are expected to suggest operational and business improvements and solutions based on analytical techniques and methods for the case they are analyzing.
BUDT758X: Data Processing and Analysis in Python
This course provides an introduction to the Python programming language for the purpose of processing, analyzing, and visualizing data. In addition, students will be introduced to developing basic regression, optimization, and simulation models in Python, using highly popular packages. Course emphasis is on mastering basic Python functionality and developing intermediate to advanced skills in working with data, through instruction and active learning.
BUDT758F: Google Online Analytics Challenge
BUDT758V: Operations Analytics
BUDT 758X Price Optimization and Revenue Management
Revenue (or yield) management (RM) first emerged in the post-deregulation US airline industry, and hit the jackpot in the mid 90's with American Airlines RM scoring $1 billion annual incremental revenues. The business strategy reformed the entire transportation and tourism industry, as well as telecommunications, broadcasting, ticketing, healthcare, fashion, manufacturing etc. Recently RM evolved to a new dimension with internet companies practicing dynamic and targeted pricing or auctions for products, services or advertisement slots. This course that specializes on dynamic price optimization and revenue management is meant to provide students with the right bundle of tools and principles, drawn from several disciplines in order to maximize profits. The RM solution integrates pricing with sales and inventory management strategies. The first part of the course addresses pricing issues such as pricing under various constraints, non-linear pricing, markdown pricing. The second part of the course provides tools and methods for combined pricing and capacity management decisions from an operational perspective.
The MS in Business Analytics program is designed to be completed in 16 months. International students who require student (F-1) visas should plan on completing the program in no more than 16 months. Classes will be taught in Van Munching Hall in College Park, MD.