The 12-course curriculum of the Master of Science in Information Systems and Analytics prepares you to be a responsible leader in the fast-growing information systems and analytics fields.
Courses are offered in online, hybrid, and in-class formats. Students can complete all 12 courses in one year and completely online (see the schedule of online courses).
Students with previous information systems coursework and part-time students should contact Nenad Jukić, the program director, for more information on how their courses would be sequenced.
Group 1 (Take all 7)
This course uses database systems as the focus for studying concepts of data modeling and data manipulation. Procedures for creating, managing, sorting, and processing data are discussed. Concepts of relational database methods are covered as well as the issues that arise in managing information in a database and using it to support business processes.
Outcome: Understanding the development and use of business database systems.
The fundamentals of managerial statistics are presented. Topics may include descriptive statistics, random variables, probability distributions, estimation, hypothesis testing, regression, and correlation analysis. Excel is used to assist in the analysis of these problems.
Outcome: Students will be able to demonstrate understanding of statistical thinking and data analysis technique for decision-making purposes.
The components and design issues related to data warehouses and business intelligence techniques for extracting meaningful information from data warehouses are emphasized. Oracle tools will be used to demonstrate design, implementation, and utilization issues.
Outcome: Students will learn how data warehouses are used to help managers successfully gather, analyze, understand and act on information stored in data warehouses.
The amount of data that our world generates is growing at a torrid pace. Sifting through & making sense of these humongous mountains of data is crucial to ensuring business growth, success and to making scientific discoveries & advancements. Data visualization plays an important role in this process.
Outcome: Students will be able to process & visualize large amounts of data in order to enable efficient & effective analysis using industry standard software.
Data Mining involves the search for patterns in large quantities of data. The fundamental techniques used in data mining include, but are not limited to, clustering, decision trees, neural networks, and association analysis.
Outcome: The student will be able to build models using an industry-standard package and interpret the results.
This course focuses on how to effectively use a computer programming language to support decision making in business. Examples include using Python for manipulating and analyzing data. In addition to covering the concepts of programming using the specified language, this course covers developing user interfaces, working with external data and debugging code. By the end of this course, the student will be able to build custom procedures and create user-defined functions in the programming language used
Business analytics is the practice of using methodically collected data to drive decisions about business and in business applications. The goal of the course is to introduce students to the current approaches, tools, and techniques involved in this practice. Because many topics and concepts in business analytics are best learned through hands-on work, time will be spent obtaining, processing, analyzing and visualizing data that pertain to different business cases. Students will use R, arguably the most popular analytical software used by data scientists. During this course, students will learn to use R, as well as gain and help improve business insight through data-driven analytics.
Outcomes: Explain the key factors differentiating business intelligence from business analytics. Frame a problem in a business analytics context to drive insightful decisions and gain the competitive edge.
Some courses may be substituted based on previous coursework with the permission of the program director.
Group 2 (Take up to 4 courses)
This course focuses on information systems requirements and related skills. Students learn techniques for translating between business needs and requirements for analytics systems and related processes. Students will learn how to elicit, analyze, specify, prioritize, and validate requirements for analytics that enable an organization's business goals. The course reviews primary processes, e.g., transaction processing, that collects and processes the information the business uses as inputs into analytics.
This course will focus on the R language. Students will learn to use R for data analysis and processing. They will write functions and scripts for repeatable analysis, build models, and perform statistical analysis.
This course will develop a vocabulary and framework for discussing, critiquing, assessing, and designing visual displays of quantitative data. This entails awareness of human perception and cognition, the use of best design practices in visualization of quantitative data, and interacting and storytelling with data.
This course focuses on practical methods for in-database data preparation and manipulation to extract analytical insights out of a large or a big data repository. It uses an open source database called PostgreSQL and provides extensive hands-on programming practice towards using advanced SQL, procedural extension to SQL, writing user defined functions, using in-database analytical functions, and manipulating strings, numbers, dates, etc. within a database. The concept of big data, distributed computing frameworks like Apache Hadoop and massively parallel processing databases are also covered.
Group 3 (Take 0 to 4 courses)
The purpose of this course is to help students understand feasible econometric techniques in order to mine information to understand economic and financial patterns and to forecast. A rigorous exposition of the theory behind econometric techniques will help students understand the issues raised in different published papers. Topics of econometric techniques covered in this course include panel data analysis, time-series models, discrete choice models, and methods to identify causality between variables. Practical applications will prepare students to use these methods in their own projects.
Techniques of forecasting and model building are introduced. Methods covered are simple and multiple regression, introduction to time series components, exponential smoothing algorithms, and AIRMA models - Box Jenkins techniques. Business cases are demonstrated and solved using the computer.
Outcome: To be able forecast business and economic variables to enhance business decisions.
The art and science of project management as applied to a variety of business and technical projects in commercial, public, and private sectors. Covers: project life cycle and methodology; teambuilding; project organization, stakeholders and leadership; proposals and contracts; techniques for project planning, estimating, scheduling, and control; PMO.
Outcome: Understanding of the broader role of the project manager with regard to all project stakeholders, and of methods, tools, and procedures for initiating, defining, and executing projects.
This course develops an understanding of the marketing research process and the role of survey research in it.
Outcome: Students formulate research problems and a design research study, including the development of a questionnaire, selection of an appropriate sample, and data analysis.
This course develops an understanding of the Internet as part of an overall marketing strategy by considering the ways in which the Internet has changed marketing and business. The course covers topics such as online consumer behavior, web analytics, online advertising, email, social media, mobile, and search engine marketing (paid and organic). In addition to learning fundamental principles of digital channels, students will apply the learned principles in a class project; example projects include creating a paid search campaign for a client, running a digital marketing simulation, writing a digital marketing plan, or conducting a social media audit.
Outcome: Students develop the power to act effectively by using technology in increasingly complex buying environments.
This course develops an understanding of the development and use of databases for marketing, retrieval of appropriate data and analysis of that data to increase marketing effectiveness.
Outcome: The student will perform database manipulation and analysis of data. Analysis includes at least univariate analysis, cross-tabulation, creation of new variables, regression analysis and recency-frequency-monetary analysis.
In this course the students will study how to use data analytics to learn about customer needs and improve targeting individual consumers. The course will encourage students to apply scientific methods and models to predict and respond to customer choices. This is the key part of learning Big Data. The term Big Data is viewed in the broad sense as it relates to various aspects of the consumer behavior, which may be captured, measured, and transformed to the digital form.
Through applications of statistical models to the analysis of the real-world databases, the students will learn how firms may use customer data to serve customers better.
This course is designed with marketing managers in mind. As profession marketing is evolving, it is no longer based primarily on the conceptual content. Marketers get exposed to thousand times the volume of data she(he) saw five years ago. More data cannot lead to better decision making unless managers learn how to use that data in meaningful ways. In this course, the students will be introduced to the analytical decision models that assist modern managers in making marketing decisions related to the targeting, product design, communications, etc.
Outcomes: The objectives of this course are the following: 1. To learn analytical techniques and decision models for enhancing marketing decision making in the modern organizations 2. Improve skills to viewing marketing processes and relationships systematically and analytically 3. To learn power of decision models applied in the real managerial contexts 4. To provide students with toolkit that may be used to assess and measure return on marketing investments in organizations
A study of the design, development, and use of decision models for analysis of supply chain problems. This course provides an example-driven approach to learn about important supply chain models, problems, and solution methodologies. The objectives of this course are to develop valuable modeling skills that students can appreciate and use effectively.
Outcomes: Students will have developed an understanding of the issues involved in the use of decision support tools for analysis of supply chain problems.
Additional courses may be approved by the program director.
Ethics Requirement (Take 1)
In the field of data analytics, the rapid advance of technology necessitates an equally rapid advance in the ethics of analytics. We currently possess the technical capability to capture real-time location data, medical stats, transitional records, and infinitely more bits and pieces of information about an individual. However, there are ethical considerations that need to be addressed surrounding how this data is collected, how it is interpreted, how it is applied, and with whom it is shared. In this course, we will explore ethical questions within the field of data through the use of business case studies. We will also look at examples of ethical codes of conduct. analytics
This course examines the ethical aspects of individual and corporate decision making in business and provides resources for making ethical decisions within the context of managerial practice.
Outcome: Students will be acquainted with the concepts and principles of ethical reasoning that have been developed in ethical theory; be aware of the specific ethical issues that arise in management and of the ways in which these issues are commonly analyzed; and be able to make sound ethical and managerial decisions and to implement those decisions within the context of an organization in a competitive marketplace.