Analytics and Information Management MBA Concentration

The purpose of this course is to provide students with an understanding of database technology and its application in managing data resources. The conceptual, logical, and physical design of databases will be analyzed. A database management system will be used as a vehicle for illustrating some of the concepts discussed in the course.

Prerequisite: Background in a procedurally oriented language (C preferred) or permission of the 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 (22:010:604)

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 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 that 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.

This general concepts course provides an understanding of the hardware, software, and other components of computer systems; it surveys file and database management systems, telecommunications and networks, analysis, design and development of computer-based information systems, and evaluation of computer acquisitions. This course is an alternative to Introduction to Software Development (22:198:605).

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

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.

Information Technology in the Digital Era is a survey of the use and management of information technology in accounting and business. Students will acquire a basic familiarity with information technology, including database technology, telecommunications, the Internet, usage of technology in the accounting profession, basics of risk management, and applications to marketing. They will also study the dynamics of the information technology industry.

No course information.

TBA

TBA

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.

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).

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).

Prerequisite: Object Oriented Programming I (22:839:614)

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.

This course focuses on SAP’s ERP and SCM solutions, as well as their major applications in supply chain management, which not only enable the supply chain visibility, but also support the decision making. The activities that lead to the integration of information and material flows across organizations are discussed. This course will also examine and apply techniques used in SAP ECC and SAP SCM for system configuration and integration with a focus on logistics and finance. The objective of this course is to help students: 1) be able to make reasonable decisions for supply chain management problems using certain decision-support systems; 2) be aware of supply chain practices; 3) identify the business process view of an organization through the process of configuring SAP ECC and SCM systems.

Prerequisite: Supply Chain Solutions with ERP/SAP I (22:799:659)

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.

[1] - http://en.wikipedia.org/wiki/Data_mining

Full course description listed above.

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.

Course description not currently available.

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 (16:960:567), Interpretation of Data II (16:960:587), or permission of instructor

Revenue management (RM) is a modern business practice and its methods dealing with the dynamic pricing of goods and services provide powerful tools that rms employ to increase pro ts. This course will introduce the students to the basic ideas of RM and it will provide a working knowledge of practical dynamic pricing and revenue management approaches and techniques. This course emphasizes real-time price optimization at the operational level. Besides dynamic pricing models and techniques, case studies and examples will be discussed throughout the course to impart a broad understanding of basic techniques. MBA Students taking this course will be able to identify opportunities for revenue management, and to analyze its potential applicability for a specific business or industry.

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.

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)

Introduction to the design, analysis, and interpretation of sample surveys. Sampling types covered include simple random, stratified random, systematical, cluster, and multistage. Methods of estimation described to estimate means, totals, ratios, and proportions. Development of sampling designs combining a variety of types of sampling and methods of estimation, and detailed description of sample size determinations to achieve goals of desired precision at least cost.

Prerequisite: Introduction to Methods and Theory of Probability (16:960:582) or equivalent

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