MS in Business Analytics & Information Systems
The program requires 33 hours of coursework and may be taken either full-time or part-time. Full-time students who already have taken all required prerequisites may be able to complete the program in one full year (three semesters) of study. Part-time students and full-time students who need prerequisites will typically need from 1.5 - 3 years to complete the degree.
Early in the first semester, a student and the program advisor will work together to complete a formal program of study that will define a coherent sequence of courses to satisfy the student's objectives. A student may have the option to complete a master's thesis or a practicum project, depending upon the availability and approval of a faculty sponsor.
Technical Core (12 credits)
The following four courses provide a solid understanding of state-of-the-art research and practice in technical areas of information systems.
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 – ArgoUML
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 introduces students to the development of distributed applications, using data-intensive web applications as the exemplar. Students will learn how to build web applications using modern architectures, consume and produce REST APIs within these applications, and deploy their applications to the cloud. Students will also learn how to use standard developer tools such as source control systems. Networking concepts will be covered to help students understand how the components of distributed applications discover and communicate with each other.
Software: Visual Studio, Github, Azure, Wireshark
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
Capstone Course (3 credits)
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
Electives (18 credits)
Six elective courses may be selected from additional Information Systems courses or (with prior approval by the MS in Business Analytics / Information Systems program academic advisor) other related areas of specialization such as Management, Decision Science, Computer Science, Entrepreneurship, Logistics, etc. Not every elective is offered every semester. Electives may be cancelled due to low enrollment.
Electives available within the department are as follows:
The purpose of this course is to introduce students to web applications architecture and related concepts. Topics to be covered include Object Oriented concepts, Object-relational mapping using Entity-Framework and SQL server, web page request response cycle, and the MVC paradigm. C# will be used as the programming language to introduce and explain concepts.
Software: Microsoft Visual Studio, LINQ, CSS, MVC
Software Architecture has emerged as a major area of study for software professionals and researchers. In this course, basic concepts and various Architectural styles with case studies will be discussed and the importance of Software Architecture in building the information systems will be stressed. Most of the topics are discussed from the practitioner's approach. This course will:
- Introduce the software architecture elements, principles and practices
- Introduce various Architecture types, Architecture styles and Architecture definition phases etc.
- Provide insight into various activities involved in software architecture modeling
- Enable participants to appreciate the importance of Architecture
- Introduce few Software Architecture Frameworks
- Provide insight into Architecture Analysis
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.
The software industry strives to produce high quality, reliable software system products and services. The processes and techniques of software testing attempt to verify the quality of software systems before they are released into the field. It is well known that one cannot test quality into software. Software quality is predicated on effective development and verification methods for requirements, specification, design, and implementation. Testing must be an integral component of all development processes to ensure an adequate level of quality. This course will survey and analyze the best practices in industrial testing groups. New research ideas for improving testing will be explored. A thorough knowledge of software testing is essential for achieving effective cybersecurity in systems. Automated testing tools will be an important part of the educational experience. The goal is for all students to achieve an in-depth understanding of software testing practice and research.
This course introduces students to business process management and re-engineering in the key functional areas of today's global businesses. Students will learn how to model business processes using BPMN notation. Course content will include analysis and discussion of several business process improvement and ERP implementation cases, to build understanding of how BPM and ERP systems are deployed in organizations. The course employs SAP as the instance of an ERP system. Students will use SAP with a business case to understand both the configuration and use of an ERP as a tool for integration of business across functional units.
Software: Microsoft Visio, Bizagi, SAP ERP Central Component, Tableau.
This course covers the data warehousing and data mining technologies that often play a strategic role in business organizations. Topics include the differences between operational and analytical database systems, dimensional modeling (data cubes) and star schemas, data warehouse performance issues, data quality, data warehouse navigation and visualization (with tools like Tableau), and a brief overview of selected data mining techniques. The Oracle database system will be used to illustrate many of the concepts covered in class, as well as providing a platform for hands-on projects. As a prerequisite, students should have had at least two courses covering relational database systems (usually including ISM 6218: Advanced Database Systems), or significant work experience.
Software: Oracle DBMS, Tableau Software, Microsoft Visio, Gliffy
This course provides an overview of key issues related to the management of information systems development projects. The course touches on the challenges and changing realities in today's world. It provides approaches, techniques, and frameworks from a variety of disciplines to facilitate discourse about professional IS project management. Students will finish the course with a clear recognition that there is no "one right way" to manage an information systems project, that an information systems design project can be approached from different perspectives, and that a variety of disciplines can be brought to bear on the project management challenge. Students would able be to have a command of the core techniques and practices in SCRUM, be able to meaningfully discuss state-of-the-art project management practices and understand leadership roles and responsibilities in project-based information systems development.
The goal of the course is to introduce skills and knowledge on Information Security
and IT Risk Management in businesses. Course objectives will be accomplished through
two categories of information – (1) introducing a general framework to help organizations
minimize their cybersecurity risks; and (2) helping students develop technical skills
to secure computer networks by implementing common IT controls.
The course explores major categories of information security threats, basic information security controls, important legal provisions regarding information security, standard methodologies for complying with legal requirements for IT general controls and basic understanding of IT risk management in organizations.
Software: Shell Scripting on a Linux virtual machine specifically designed for this class.
The thesis must make a well-defined contribution to research and development in an area of Information Systems.
In addition, the following Special Topics are being offered:
In this course students will learn various Big data technologies and how they can be used in such Ecommerce sites. The first half of the course will focus on Big data technologies such as No-SQL database, distributed file system and Map-Reduce programming. 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. In the second half of the course, students will learn various components of an Ecommerce application and how these components can be scaled to support Big number of users and products across the world. The course will introduce students to recommendation systems, user management, application analytics and user tracking from the traditional perspective. Students will explore how these systems can be scaled with the help of Big data technologies. Mobile apps and digital advertisement are playing critical role for Ecommerce systems. Mobile apps are allowing E-commerce applications to reach to users who do not have access to traditional desktop computers. Digital advertisement over internet is the key way to build the brand name and acquire new users. The course will cover both these systems and explain how Big Data technologies are helping here.
Software: Eclipse, Cassandra, DynamoDB, HDFS, Spark, MapReduce, Solr, Redis, Memcached, Hazelcast, ehCache, Aerospike, Kafka, Kinesis, NodeJS, CDN-Flurry, Quantcast, Bluekai, Google Analytics
Business analytics encompasses the collection, analysis, presentation, and use of data to assist in the decision-making process. Statistics can be thought of as the science and art of making sense of numerical data. Computer hardware and software has given the ability to analyze immense amounts of data. Thus statistics have emerged as one of the essential keys to good management.
This course introduces you using SAS for statistical programming. SAS is used heavily in the industry these days, so it will be beneficial for you to master the basic concepts.
Software: SAS 9.4
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 and related tools in the Anaconda distribution
This course provides students an in-depth experience with storytelling and visualization. In the analytics journey, we start with the chaos of data and conclude with insights to produce better decisions. Data/Information visualization is widely used in several industries, including business, engineering, and media disciplines to help people analyze and understand what the data is telling us. The visualization field has grown exponentially over the last few years, and thus there are more tools available to help us quickly and efficiently create compelling ways to understand data.
This course provides an overview of the data/information visualization discipline. Using a hands-on approach, readings and lectures will cover various visualization principles and tools. Our labs will focus on practical introductions to tools and frameworks, with plenty of time to explore & utilize additional applications. We will discuss existing visualizations (e.g. what we find in various publications and government data sources) and critique their effectiveness in conveying information. All students are expected to participate in class discussion, complete lab assignments, and create & critique many data visualization examples throughout the term.
The purpose of this course is to encourage you to think differently about most of the value exchange activities engaged in by businesses, public organizations, and individuals. Then do it. We consumers, business people, developers, and citizens have been conditioned to be centralization thinkers. We have taken a distributed technology called the internet, and decided to centralize many services, including search, social and professional networking, and purchasing.
Cryptocurrency uses distributed, peer-to-peer technology. Cryptocurrency is a use case of distributed ledger technology, more popularly known as blockchain. Blockchain is a data structure that overcame some nagging obstacles that dogged previous attempts to create more convenient forms of currency and value exchange. In this course, we will learn how to build and manipulate public and private permissioned blockchains for use in multiple patterns of applications.
IoT is often bundled with Blockchain as it provides a tamper-proof data structure for persisting data. In this course, we will consider them as separate technologies, but also show how they can be used together.
Blockchain technology is an open digital ledger system for recording transactions and events. It creates significant business opportunities in industries such as healthcare, financial services, life sciences, manufacturing, content industries, consumer product industries and the public sector including governmental services. Information technology professionals as well as academics are beginning to explore, develop, experiment and seek opportunities to use this technology to create new products, services and business models that have potential to disrupt many well-established industries including financial services. Investors including venture capitalists and banks have been actively exploring opportunities leading to explosive growth of 'Fintech' (financial technology) start-ups. Many of these efforts extend a wide range of applications and uses of a secure 'anonymity' technology initially developed to underlay the virtual/crypto-currency Bitcoin. On the other hand, there has been significant concern among the policy makers about the potential impact of these technologies on governance including security and traceability of 'money flows'. Governments and regulators have started to explore appropriate regulations and law for these technologies and new markets which may inhibit the growth of these technologies if public concerns about the 'usefulness' of these technologies enabling distributed ledger ecosystem are not adequately addressed.
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.
In this course students participate in various research projects along with faculties. The number of credit for independent study/research is restricted to only 2.
MS BAIS program gives you the opportunity to develop deep expertise in an area by taking few courses in the area in a sequence in 2/3 consecutive semesters. You will get the most by following one or more of these sequences depending on your expertise and career focus.
- Data Analyst / Data Analytics / Data Science in Machine Learning Track:
- Data Mining -> Data Science Programming -> Big Data for Business Applications.
- Application Development Track:
- Distributed Information Systems -> Big Data for Business Applications.
- Data Analytics using Statistics Track:
- Analytical Methods in Business-> Statistical Data Mining
- Blockchain and Cryptocurrency Track(Auditing/Supply Chain/FinTech):
- Blockchain Fundamentals -> Blockchain Programming
The master's thesis option requires six credits of ISM 6971, which count as six of
the 18 MIS elective credits. The thesis must make a well-defined contribution to research
and development in an area of Information Systems.
The practicum option requires an investigation or development of a new information technology artifact. The project typically occurs in the student's place of employment and is jointly supervised by a faculty member and a manager in the company. Based upon the magnitude of the project, either three or six hours of credit in ISM 6905 would be taken, which would count for three or six hours of the 18 hours of BAIS electives.