Seven required courses (21 credits) build a foundation of analytical skills and technical knowledge
Quantitative Analysis (6 credits)
Statistical Methods for Decision Making (OPMT 600)
This course examines statistical and analytical methods including sampling concepts, regression analysis, hypothesis testing, forecasting, quality control, simulation and database management.
Foundations of Data Analysis (R-Environment) (SEIS 631)
This course provides a broad introduction to the subject of data analysis, focusing on relevant methods for performing data collection, representation, transformation and data-driven decision making. You will also develop proficiency in the widely-used R language, used through the course.
Prerequisite OPMT 600 or SEIS 631
The primary goal of this course is to develop a better understanding of data analysis for business research, emphasizing the interpretation of data rather than calculations. Building upon the groundwork provided by the preliminary statistics course, topics will include techniques commonly used in business such as logistic regression, two-way analysis of variance and statistics for scale development. These skills are relevant for students involved in marketing research and survey development. Course deliverables will include a project, potentially based on a situation or analysis from your workplace or industry.
Analytics Tools (6 credits)
This course is focused on developing the quantitative and analytical skills needed to gain insight into the resolution of practical business problems. Learn to analyze and solve management problems using spreadsheet-based methods. Specific methods of clarifying objectives, developing alternatives, addressing trade-offs and conducting a defensible quantitative analysis will be presented. Topics include spreadsheet modeling, linear programming, transportation modeling, decision analysis, project management and simulation. You will also be introduced to building decision support models using Visual Basic Applications (VBA).
The course provides an introduction to concepts and techniques used in the field of data analytics and visualization. Insights discovered from the data are then communicated using data visualization. Topics covered in the course include predictive analytics, pattern discovery and best practices for creating effective data visualizations. Through practical application of the above topics, you will also develop proficiency in using analytics tools.
Database Management (6 credits)
This course focuses on database management system concepts, database design and implementation. Conceptual data modeling using Entity Relationship (ER) is used to capture the requirements of a database design. Relational model concepts are introduced and mapping from ER to relational model is discussed. Logical database design (Normalization) and indexing strategies are also discussed to aid in system performance. Relational algebra and Structured Query Language (SQL) are used to work with a database. From a system perspective, the course focuses on query optimization and execution strategies, concurrency control, locking, deadlocks and database back-up and recovery concepts. Database security and authorization are also discussed. You will use Oracle and SQL Server to design a database and complete an application using SQL as your project.
Prerequisite SEIS 630
This course covers the technical concepts of managing vast amounts of unstructured, semi-structured and structured data, collectively called “Big Data.” Due to the sheer volume of Big Data, traditional approaches to managing databases do not work well and do not perform as expected. A distributed architecture for both the file system and the operating system is needed. Some of the techniques used in managing Big Data have the origins in the research and the developments that have been going on for decades in the area of parallel processing and distributed database management systems.
This course focuses on why big data sets must be distributed and the issues that distribution introduces. The basic concepts on which distributed data sets are handled are discussed first. Once a foundation is defined, software tools that we use to work with big data sets are studied to provide an in-depth analysis of the concepts introduced.
Communication Skills (Choose one, 3 credits)
This course begins with a framework for understanding managerial communication and a general model for employing skills. The focus is on best practices for relatively formal written and spoken communication in the workplace. Participants respond to assigned reading and instructor perspectives with writing samples and classroom performances. They respond to feedback from guest experts, their peers and the instructor.
This course will introduce students to principles that effectively link storytelling to influencing business outcomes. Students will explore the meaning of information and its effect on organizational strategy and culture; be able to build a structured thinking process or informational dashboard to tell a compelling story; and gain skills in confidently understanding and using information to influence outcomes. The two core purposes for the course are to: (1) learn the principles of presenting information that makes an emotional connection to the listener framed by organization strategy and culture; and (2) refine the student’s own storytelling capabilities.
Learn the fundamentals of written and oral communication as practiced by IT professionals. The course emphasizes product descriptions, instructions, informative and persuasive oral presentations, the role of graphics and teamwork on projects. In addition, the course introduces managerial strategies and tactics, such as planning and evaluation, which are critical for meeting an intended audience's needs, as well as communication issues related to business analysis and project management. After completing this course, you will be more confident about your ability to communicate effectively in the workplace.