Weekend Executive Master of Science - Business Analytics & Information Systems
Coursework (30 credit hours)
The goal of this course is to provide the technical and managerial foundations of software engineering and information systems development. Based on a prerequisite understanding of basic systems concepts, the course teaches how to manage and perform activities throughout the software-intensive systems development life cycle, from the analysis of system requirements through system design to system implementation, testing, and maintenance. The roles of creative and analytic thinking are highlighted throughout the software development processes. Current best practices in systems development are presented and research challenges are discussed.
Software: ICASE Tool – ArgoUM
This course is designed to introduce you to general database concepts, the landscape of emerging technologies, and provide some experience in the design, implementation, and usage of relational databases. This class will comprise a mix of conceptual and hands-on modules and will enable students to explain relational database concepts and tools, develop logical database models using entity-relationship (ER) diagrams, convert ER diagrams to relational tables in normalized form, write effective database queries using structure query language (SQL), and do some basic performance tuning.
Software: Oracle DBMS, SQL Developer (from Oracle), Microsoft Visio, and Gliffy
This course will focus on telecommunications, networks, and distributed applications. All forms of communication will be covered. Students will gain exposure to network management systems, local area networks (LANs), networking basics, network security, distributed computing, cloud services, big data processing and global networks, such as Internet. This course is designed for MS BAIS students and interested MBA students and covers the IT infrastructure layer in an organization.
Software: Microsoft Visual Studio, Azure Platform
The objective of the course is to teach students analytical methods based on statistical techniques for business operations. The course will teach inferential and descriptive statistics, probability theories, interpreting confidence intervals for business decisions, hypothesis testing for business operations, regression analysis, and analysis of variance in the context of business operations.
Software: R, R-Studio, Excel
This MS/MIS capstone course is intended to build among graduating Masters students an appreciation of the key issues concerning the deployment, use, and management of information technology (IT) within enterprises and the ability to think critically and articulate convincing positions on these issues for corporate management. During the period of this course, a wide range of IT issues is discussed such as big data, cloud computing, security breaches, managed IT services, and IT valuation, in a broad range of industry sectors such as healthcare, retail, energy, technology, and consulting. The class will be structured around two core activities: case discussions and debates.
Software: Microsoft Powerpoint/Prezi slide deck
Following electives are chosen by faculty, these may be replaced in the future based on the student profile.
This course introduces students to the key concepts, of Blockchain or distributed ledger technology and its management and governance including legal and regulatory issues. The students learn how this technology achieves and then maintains consensus as well as autonomy, creation of public and private blockchain market, distributed ledger technology innovation, trust establishment and maintenance, iterative consensus development, and autonomy in use. The course discusses in detail the impact of this disruptive technology in financial services sector and emerging applications including business models in Fintech
The past few years have seen an unprecedented explosion in the amount of data collected by businesses and have witnessed enabling technologies such as database systems, visualization tools and statistical and machine learning algorithms reach industrial strength. These trends have spawned a new breed of business intelligence systems that go significantly beyond reporting capabilities, to support predictive modeling and the extraction of business insights from data. These trends have also created a new role of "data scientists" who are professionals with expertise in the concepts and tools necessary for the skilled use of these systems. This course will provide an understanding of fundamental data science concepts, techniques for predictive modeling and discuss how to effectively use these in business applications. In this course, you will learn methodology as well as different methods for data science. Starting with data-exploration and visualization you will learn how to build and evaluate predictive models as well as how to learn interesting clusters and patterns from the data. This course will focus on statistical aspects of data mining and as such have a strong emphasis on data-driven models. The course is hands-on and will involve several data-driven projects that use state-of-the-art data mining software.
Software: RapidMiner, Weka, SAS Enterprise Miner and Python
This course teaches different methods and concepts for discovering and modeling data patterns. Starting with data-exploration and -visualization, students will learn how to build statistical models around the patterns in data. This course focuses on statistical aspects of data mining and as such has a strong emphasis on data-driven models. That is, beginning with basic principles of linear regression, it discusses when linear models are reasonable to employ and when their linearity assumption breaks down. The course then discusses different ways of relaxing the linearity assumption and composing more powerful models. To that end, simple "tricks" (such as interaction terms and data-transformations) that will render the linear model more flexible will also be addressed. The course also discusses data-reduction techniques as well as variable selection methods that will allow the model to handle large amounts of possibly correlated information. Flexible methods for large data sets via nonparametric and semi-parametric regression models will also be discussed. In addition, models that allow for a combination of cross-sectional and (temporally- or spatially-) dependent data will also be explored in this course. The course is very hands-on and will involve several (smaller and larger) data-driven projects.
Present days applications are not confined to a geographical boundary. Online business applications are accessed across the world, support few hundred millions users and sells almost everything. The number of products sold on an online retail application is Big which ranges from few hundreds of millions to several billions. With that massive size, it requires judicious application of Big Data technologies at each and every corner of these systems. In this course students will learn various Big data technologies and how they can be used in such online applications. The course will focus on Big data technologies such as No-SQL database, distributed file system, Map-Reduce programming and Spark based data analytics. Students will also be exposed to other related technologies such as search system, distributed cache and distributed communication systems that are necessary to support Big data technology based applications.
Software: HDFS, Pig, Hive, MongoDB, Cassandra, MapReduce, Spark, Solr, Redis, Memcached
Data analytics techniques, tools and applications have become mainstream in variety of business, scientific, social and policy applications. This course will provide students an in-depth overview of machine learning techniques for analytics using Python as the programming language and students will learn to apply advanced machine learning techniques using Python. Students are expected to be familiar with at least one programming language and will be expected to learn Python independently in the course, as the focus will be on applying machine learning ideas in this platform, and not the language itself. Specific topics will include decision trees, gradient descent methods, support vector machines, dimensionality reduction, neural networks, deep learning and reinforcement learning. The course will focus on advanced understanding of these methods along with implementation of these techniques in Python. Students will be expected to complete an advanced data analytics project using Python and will be encouraged to demonstrate applications embedding the machine learning model within some other information system such as a Web site and mobile app.
Software: Python, Jupyter, Anaconda
Leadership (2 credit hours)
Leadership and Management Concepts
Provides a foundation for the study of processes of leadership in organization and society. Presents an overview of various concepts of management, such as the personal values of leaders and leadership in an organization.
Project/Independent Study (1 credit hours)
This is an 18-month project that lasts over the course of the program. Faculty experts guide students who use this opportunity to develop a prototype technology system or derive some interesting insights out of publicly available datasets. Students may use this opportunity to prepare a paper for submission to an international peer reviewed conference.