Curriculum for Business Analytics and Information Technology (BAIT) Major

Management Information Systems (MIS) and Business Analytics (BA) Concentrations are also available for non-BAIT majors.

 

Curriculum

School Core Courses

 

Required Courses
(12 credits)

Course # Title Credits
33:136:470 Business Data Management* 3
33:136:400 Business Decision Analytics under Uncertainty** 3
33:136:388 Foundations of Business Programming*** 3
33:136:485 Time Series Modeling for Business** 3

*Note: Offered in the Spring semester.

**Note: Offered in the Fall semester.

***Note: Offered in the Fall semester. Students with multiple prior programming courses may petition to replace this course with an additional elective from the list below; contact the department for details.

 

Elective Courses
(9 credits)

Group 1

Course # Title Credits
33:136:494 Data Mining for Business Intelligence 3
33:136:465 Enterprise Architecture  3
33:136:471 Information System Security 3
33:136:487 Large-Scale Business Data Analysis 3
33:136:486 Optimization Modeling 3
33:136:405 Risk Modeling 3
33:136:455 Introduction to ERP 3

Group 2

At most two courses can be chosen from the following list of approved electives in other business majors, computer science, economics, mathematics, statistics, and supply chain management. Please note that most of these courses have prerequisites in their respective departments.

Course # Title Credits
01:960:467 Applied Multivariate Analysis 3
01:198:425 Computer Methods in Statistics 4
01:198:419 Computer Security 4
01:198:344 Design and Analysis of Computer Algorithms 4
01:220:481 Economics of Uncertainty 3
33:799:450 Fundamentals of Supply Chain Solutions with SAP 3
01:220:482 Game Theory 3
01:198:352 Internet Technology 4
01:198:440 Introduction to Artificial Intelligence 4
01:960:476 Introduction to Sampling 3
01:640:354 Linear Optimization 3
01:220:485 Mathematical Economics 3
01:220:487 Operations Research II 3
01:198:336 Principles of Information and Data Management 4
01:198:314 Principles of Programming Languages 3
01:960:463 Regression Methods 3
01:198:431 Software Engineering 4
01:640:424 Stochastic Models in Operations Research 3
01:198:213 Software Methodology 4

 

Course Descriptions

33:136:470 - (3 cr)
Business Data Management

Introduces principles and techniques for managing corporate data resources. Techniques for managing the design and development of large database systems, including data models, concurrent processing, data distribution, database administration, and data warehousing; demonstrates their use in business applications.  Discusses principles of database systems, database design, database schemas, and database manipulation using SQL.  Surveys advanced database management topics such as transaction control, distributed databases, data warehouses, database e-commerce applications, and object-oriented databases.  In addition to conceptual material, provides significant hands-on experience using current database technologies.  

Prerequisite: Management Information Systems (33:136:370) and Foundations of Business Programming (33:136:388)
Note: This course is offered in the Spring semester

33:136:400 - (3 cr)
Business Decision Analytics under Uncertainty

This class introduces students to methods for planning problems that include both time evolution and uncertainty.  It covers the ideas of dynamic programming, starting with classical decision trees, and shows how to apply Bayesian methods to derive tree probabilities. Students solve small problems by hand, and then write simple computer programs to solve more complicated problems.  The course includes fundamental computer programming techniques for numerical calculations, including loops and arrays.  For problems too complex to analyze exhaustively, the class will introduce sampling-based simulation techniques.  Students write simple programs to implement Monte Carlo simulations (which can be much more efficient than using spreadsheets), and learn the basics of specialized packages for discrete-event simulation.  

Prerequisite: Operations Management (33:136:386)
Note: This course is offered in the Fall semester

33:136:494 - (3 cr)
Data Mining for Business Intelligence

The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world business applications. The core topics covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. The course consists of about twelve weeks of lecture, followed by two weeks of project presentations by students applying data mining techniques to applications such as fraud detection, Web usage analysis, customer churn analysis, customer segmentation, blog mining, text mining, and other business data analysis.  

Prerequisite: None except completion of RBS eligibility courses

33:136:465 - (3 cr)
Enterprise Architecture

(Note – the syllabus for this course is still under development and this description is preliminary.)  Information Architecture refers to the way in which responsibility for information storage and processing is distributed throughout an organization, possibly in different and sometimes partially overlapping information systems; understanding this topic is critical when making additions or changes to an organization's computing resources.  This course covers this topic, introducing models, techniques and tools for integrating enterprise applications, data, technologies and infrastructure with business capabilities and processes.  Specifically, it covers methods and models for Enterprise Information Architecture (EIA); reference architecture, open group architecture, service-oriented architecture, operational models, viewing information as a service, and conceptual frameworks such as TOGAF and Zachman.  Other specific topics include data domains, information governance, management of metadata, master data, enterprise content, analytical applications, business performance, enterprise information integration, mashups, and connectivity and interoperation.  The course will explore new delivery models for IT services: cloud computing, intelligent utility networks, and dynamic warehousing.

Prerequisite: Management Information Systems (33:136:370)

33:136:388 - (3 cr)
Foundations of Business Programming

Business-oriented programming accounts for the vast majority of all programs written today.  This course covers the principles of programming and software development in depth, with an emphasis on an object-oriented (OO) programming style, using an OOP language such as C++ or Java.  The course will study the principles of object-oriented design using the UML modeling language. Also covers fundamental data structures and algorithm development for solving business problems.  

Prerequisite: None except completion of RBS eligibility courses
Note: This course is offered in the Fall semester. Students with multiple prior programming courses may petition to replace this course with an additional BAIT-qualified elective; contact the department for details.

33:136:471 - (3 cr)
Information System Security

The purpose of this course is to provide the student with an overview of information security and assurance in e-business and other cyber-environments. This course provides the foundation for understanding the key issues associated with protecting information assets, determining levels of protection and response to security incidents, and designing a consistent, reasonable information security system, with appropriate intrusion detection and reporting features. The fundamentals of threats, vulnerabilities, firewalls, secure access, intrusion detection, cryptography, disaster recovery techniques, and secure programming are covered.  

Prerequisite: Management Information Systems (33:136:370)

33:136:487 - (3 cr)
Large-Scale Business Data Analysis

This course introduces students to fundamental statistical techniques for analyzing large-scale business data. The main goal is to provide systematic training in statistical models for massive datasets as well as programming, data management, and exploratory data analysis in real-world settings.  The course equips students to develop context-sensitive models and perform model checking and diagnosis. Topics include parametric inference, logistic regression, nonlinear regression, causal inference, graphical models, dimension reduction, and model selection. Students are provided the opportunity to learn a comprehensive set of data analysis techniques through lessons, demonstrations, and programming labs.  

Prerequisite: Statistical Methods for Business (33:136:385)

33:136:486 - (3 cr)
Optimization Modeling

This class introduces optimization modeling beyond the confines of a two-dimensional spreadsheet.  Students learn appropriate mathematical notation for formulating realistic, complex optimization models, and how to translate this notation into a modern modeling language. Students learn to represent given problem data symbolically and separate it from the fundamental model structure.  They learn to use set-theoretic and network ideas in formulating models and representing data.  The course surveys when it is appropriate to use nonlinear models or integer variables.  Students learn how to formulate models to maximize the chance of efficient and correct solution, by formulating models in a linear or convex manner when possible, even at the cost of increasing model size, and by avoiding highly symmetric integer formulations.  The course will introduce the concept of Lagrange multipliers and their economic interpretation.  Interfacing modeling languages to relational databases is another possible topic, time permitting.  

Prerequisite: Operations Management (33:136:386)

33:136:405 - (3 cr)
Risk Modeling

The course introduces the main concepts and models of decision-making under uncertainty when risk aversion plays a major role. The first topic is expected utility models, including their economic background and business applications. Next, mean-risk models are presented, along with their applications to finance. Then, the course covers the concepts of value at risk and average value at risk, including their applications. These topics serve as a foundation for models involving measures of risk. Finally, we the course presents some of the most fundamental dynamic models, with applications to insurance, finance, and inventory management.  

Prerequisite: Operations Management (33:136:386)

33:136:485 - (3 cr)
Time Series Modeling for Business

The analysis of time-dependent data is critical to industries such as finance, marketing, retail, and accounting. This course introduces time-series models with emphasis on their practical applications in business. The goal is to show how dynamic financial and economic data can be modeled and analyzed using proper statistical techniques. The topics include methods for trend and seasonal analysis and adjustment, modeling and forecasting with autoregressive moving average (ARMA) processes, and model identification and diagnostics for time series. Other subjects include volatility and state space models.  This course provides hands-on experience by pairing lectures on methodology with lab sessions using high-performance statistical software to perform real-world data analyses.

Prerequisite: Statistical Methods for Business (33:136:385)
Note: This course is offered in the Fall semester

 

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