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 (3 semesters) of study. Part-time students and full-time students who need prerequisites will typically need from 1 1/2 to 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 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 MIS students and interested MBA students and covers the IT infrastructure layer in an organization.
This course gives an overview of the field of Operations Management, both in manufacturing and services industries. Topics include inventory management, demand dependency, queuing analysis, scheduling, and mathematical optimization. A strong grounding in inferential statistics is also provided, as well as practical experience with basic simulation analysis.
Software: Microsoft Excel, SAS, @Risk and accompanying tools.
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.
Elective 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
The focus of the course is on the synthesis of information and knowledge as it concerns current business topics related to the management of information technology (IT) resources. Specific objectives are:
- to acquaint students with the concepts relevant to the management of information technology (IT) resources in contemporary organizations;
- to introduce students to the impact of IT at the individual, organizational, and global levels; and
- to have interesting and fun discussions.
Particular attention will be given to several skills that are key to the success of business professionals:
- ability to research a managerial IT topic and present the results in written and presentation formats;
- ability to discuss managerial IT topics in group settings; and
- ability to synthesize materialand write extended text on your understanding of a managerial IT issues (i.e., essays).
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.
This elective course focuses on the role of information technology in global business organizations and the challenges in building information systems to enable global operations. Topics to be presented and discussed include:
- why businesses need information technologies to operate globally
- what information technologies and systems support international and global operations
- specific examples of successful global information systems
- barriers that must be overcome to build and maintain global information systems
- the tension between common, company-wide needs for information and the unique needs of some local business units
- the special challenges in working on project teams when team members are located around the world
- important enterprise-wide packaged applications for global operations, such as SAP R/3
- specific strategies and techniques that IS professionals can use when deploying global information systems
This course teaches student to:
- understand the role of quantitative decision making tools in business.
- understand at a fundamental level the application of several classic quantitative tools.
- see how such tools can be used relatively easily with commonly available software.
Software: Microsoft Excel
Technology is a vital input to the process of wealth creation in an increasingly networked world. This course introduces important technology enablers that facilitate electronic commerce and discusses the evolving e-commerce business models and landscape that has developed around these technologies. The course discusses several new and established businesses that exploit these enablers.
Electronic commerce has clearly presented new opportunities and challenges for the MIS function within enterprises. However, it has also presented new opportunities for other key functional areas such as Marketing (which is being done in a far more targeted manner online than ever before) and Operations (which is both seeing new challenges due to e-commerce but also benefiting from the underlying technology infrastructure that is facilitating e-commerce). Hence, the course will be useful for students in a variety of business functions, but particularly those interested in pursuing careers in Information Systems, Marketing and Operations within a firm. The course is also highly recommended for students with interest in careers in the technology sector, or technology related consulting, banking or venture capital. Those with entrepreneurial interests in the tech sector will also find the class useful.
The course will take a layered approach to understanding the technology enablers - and associated business landscape - that facilitate electronic commerce. The layers are:
- Network infrastructure: technologies and firms that enable the current generation networks that facilitate e-commerce.
- Data infrastructure (Web content): technologies and firms that enable the design (HTML, XML, authoring tools), access (http, cookies, Web search algorithms), secure transmission (encryption) and mining (online advertising models, personalization of content, online recommendation algorithms) of content.
- Applications (software) infrastructure – technologies that enable the development and deployment of software applications over these networks (Web services, grid computing and the open-source paradigm).
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
Multimedia continues to be an emerging technology that integrates audio, video, text and graphics to enhance the human computer interface. This course will address the development of multimedia concepts, hardware and software components, cognitive and pedagogical issues, and the planning, design and implementation of effective multi-hyper media interfaces. Students will be involved in multimedia authoring, and presenting on various components of multi and hyper media applications.
Software: Arcsoft, 360-Panorama, Tourweaver, Adobe Director
This course is an introduction to mainframe technologies (via z/os) and business programming and design (via COBOL). Students will apply problem solving using programming in a Mainframe development environment. By the end of the semester, students should be able to develop and implement solutions to problems using IBM Mainframe tools within an IBM Mainframe development environment.
Software: ISPF Editor
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.
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.
Responsible Conduct of Research
The Coursework Completion Form must be completed on the ECE secure website, prior to the submission of the Ph.D. Proposal.
Students who wish to count course credit earned at other universities toward their Ph.D. degree requirements should request the class evaluation during their second term or later. The classes have to be graduate or senior undergraduate level. No classes that were used to satisfy an undergraduate degree can be used toward the Ph.D. degree. Up to 30 hours of credits earned toward a graduate degree with a grade of C and above at a different institution can be used towards the Ph.D. degree. M.S. Thesis can be used in Group IV (electives) for a maximum of 12 credit hours. The approved classes will only count toward the Ph.D. degree course requirements and will not be transferred on the Georgia Tech transcript.
The following package needs to be submitted to the Academic Office for evaluation and approval:
Copy of the Coursework Completion form, filled out entirely. The classes have to show the institution name, number, and title. If the coursework is not completed at the time of the submission, future classes need to be included for a total of 43 hours. The future classes can be later changed to other eligible classes. Please use the classes taken at Georgia Tech in Groups I, II, and III as much as you can.
Non-official transcript from the institution where the classes have been taken. The transcript needs to show that the classes have been used toward a graduate degree and whether the institution is on the semester or quarter system.
Syllabi/course descriptions for all the classes to be used in Groups I, II, and III.
A soft copy of the M.S. thesis (if used in the coursework plan).