Full-Time MBA concentration in Analytics and Information Management

Discussion of representative algorithms and data structures encountered in applications. Familiarity with Prim and Kruskal minimum spanning tree algorithms and Dijkstra shortest path algorithm.

Quality management philosophies, Deming, Juran; quality planning, control, and improvement; quality systems, management organizations for quality assurance. Role of operations research.

Modern methods of data analysis with an emphasis on statistical computing: univariate statistics, data visualization, linear models, generalized linear models (GLM), multivariate analysis and clustering methods, tree-based methods, and robust statistics. Expect to use statistical software packages, such as SAS (or SPSS) and Splus (or R) in data analysis.

Prerequisite: Level IV statistics.

Recommended: Regression Analysis (16:960:563) OR Data Analysis and Decision Making (22:960:575)

Databases and data warehousing, exploratory data analysis and visualization, an overview of data mining algorithms, modeling for data mining, descriptive modeling, predictive modeling, pattern and rule discovery, text mining, Bayesian data mining, observational studies.

Prerequisite: Applied Multivariate Analysis, Interpretation of Data II, or permission of instructor

Examines management's need for advanced information technology in an organization, focusing on the systems and technology that are developed to supply this information. Does not primarily focus on the technical aspects of data processing and computer operations, though these topics will be discussed largely in the context of case examples. Thus, the emphasis is on the management of systems development rather than on specific tools and techniques. Students taking this course are expected to gain survey-level knowledge of advanced technological tools of managerial information and the ways these tools can be used.

Prerequisite: Design and Development of Information Systems

Our goal in this course is to learn the principles of programming for business analytics using the Python and R programming languages. Programming is the fundamental background skill based on which all Information Systems are built. Even if it is not your goal to become a software developer, it is essential for an MBA graduate with a concentration in Analytics and Information Management to possess a working knowledge of programming and fundamental insights into what a programmer does. This course provides you with this essential knowledge.

Prerequisite: No previous knowledge of programming languages is required. However, those of you who are familiar with some other language, particularly C or a C derivative, will have an easier ride in the first few weeks. You need to have access to a personal computer (Windows, Mac or Unix will all work.) You need to be able to download and install software on this machine. You also need to have access to the internet.

An in-depth study of machine learning, to impart an understanding of the major topics in this area, the capabilities and limitations of existing methods, and research topics in this field. Inductive learning, including decision-tree and neural-network approaches, Bayesian methods, computational learning theory, instance-based learning, explanation-based learning, reinforcement learning, nearest neighbor methods, PAC-learning, inductive logic programming, genetic algorithms, unsupervised learning, linear and nonlinear dimensionality reduction, and kernels methods.

TBA

TBA

Provides a technical overview of Enterprise Resource Planning Systems and their role within an organization. It introduces key concepts of integrated information systems and explains why such systems are valuable to businesses. SAP ECC is introduced to illustrate the concepts, fundamentals, framework, general information, technology context, technological infrastructure, and integration of enterprise-wide business applications. In addition to lectures, students will be guided through several hands-on activities of various business processes in SAP ECC. The objective of this course is to help students: 1) master the basic concepts, architecture and terminology of an ERP system; 2) understand the need and examine the capabilities of ERP systems; and 3) illustrate how integrated information systems can help a company prosper.

The goal of this year-long sequence of courses is to give a rigorous introduction to computer programming and software engineering with special emphasis on applications to financial engineering. Our primary programming language will be C++. This programming language is fast enough to accommodate the performance demanded in financial environments. At the same time C++ is an object oriented language and, as such, is suitable for modern software design. In this course the assumption is that students have had no background in computer programming, although even people who are familiar with some programming language will hopefully benefit and learn new material. In part I in the Fall semester the course will start with basic concepts of programming, but we quickly get into topics in object oriented programming, UML diagrams, and basic patterns. We will also include introduction to basic algorithms and data structures. In part II in the Spring semester, more advanced topics will be covered, including advanced algorithms and data structures especially through introduction to STL and boost libraries, numerical algorithms and introduction to BLAS and LAPACK libraries, design of graphical user interfaces, and concurrent programming (also known as multiprogramming).

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Innovative businesses are using data to make better predictions about their business environment, their business future, and the future of their global competitors. “Big Data” is a business term frequently used these days. Businesses are storing and collecting more data than ever before to gain a competitive edge. McKinsey predicts that data will grow 10-fold by 2015 and 100-fold by 2020. This will result in businesses looking for better data scientists to help them leverage “Big Data” and gain a competitive edge.

In this class, students will use the level R programming language to become data scientists and business forecasters. Specifically, students will learn how to:

• Understand Data
• Analyze Data
• Apply various forecasting methods
• Leverage forecasts to make decisions
• Communicate forecasts and recommendations to management

No prior knowledge of R programming is required. You will learn and become proficient in R and obtain hands-on experience of its forecasting package through case studies and real-life examples during each lecture. You will also learn to better communicate your forecast and strengthen your analytical skills. The practical knowledge gained upon completion of this course will help in careers ranging from business analytics to marketing, accounting, financial services, and more.

This course is intended for business students of data mining[1] techniques with these goals: 1) To provide the key methods of classification, prediction, reduction, and exploration that are at the heart of data mining; 2) To provide business decision-making context for these methods; 3) Using real business cases, to illustrate the application and interpretation of these methods. The course will cover Classification (e.g. helps banks to determine who will default on a loan, or email filters to determine which emails are spam), Clustering (like classification, but groups are not predefined, as in legitimate vs. spam email, so the algorithm will try to group similar email together for instance), Regression (e.g. how ad campaigns in offline media such as print, audio and TV affect online interest in the advertiser's brand), Association Rule Learning (enables merchants, for example Amazon, to determine which items customers tend to buy together and make suggestions for further purchase, otherwise known as "market basket analysis"); and Neural Nets (helps financial agents to model complex markets for high frequency trading; helps Pandora adapt to your personal radio station). The pedagogical style will use business cases so the student can follow along and implement the algorithms on his or her own with a very shallow learning curve. In addition, students will work in teams to mine their own data. Individual students may request to work on their own company data. The computation platform with be the R Programming language and the specialized packages in data mining.

TBA

Recent years have witnessed widespread use of computers and interconnecting networks, raising demands for security measures to protect the information and relevant systems. This course prepares the students to meet the new challenges in a world of increasing threats to computer security by providing them with an understanding of the various threats and countermeasures. Specifically, students will learn the theoretical advancements in information security, state-of-the-art techniques, standards and best practices. The topics covered in this course include: Study of security policies, models and mechanisms for secrecy, integrity and availability; Mechanisms for mandatory and discretionary controls; Data models, concepts and mechanisms for database security; Basic cryptology and its applications; Security in computer networks and distributed systems; Identity threat; Control and prevention of viruses and other rogue programs. 

TBA

This course covers applied statistical methodologies pertaining to time series, with en emphasis on model building and accurate prediction. Completion of this course will provide students with enough insights and modeling tools to analyze time series data in the business world. Students are expected to have basic working knowledge of probability and statistics including linear regression, estimation and testing from the applied perspective. We will use R throughout the course so prior knowledge of it is welcome, but not required.