(7 total courses; 21 credit hours total required)
CPSC 5175 Programming Languages for Business Data Analytics: This course introduces students to the fundamental computing skills via a variety of programming languages for effective data analysis. Through this course students will learn several business/statistical programming languages (Python, R). Develop programs to read data, write functions, make informative graphics, and apply modern statistical methods to complex data sets. Prerequisite: CPSC 5000 or department head approval.
CPSC 5185 Data Visualization for Business: This course covers development of effective visualization to facilitate the understanding of complex organizational data. Topics include human perception & attention, visualization software & toolkits, visualization techniques for spatial data, geospatial data, time-oriented data, multivariate data, trees, graphs, networks, maps and text. Evaluate good design practices for visualization. Review cutting-edge research in data visualization. Prerequisite: CPSC 5175 Programming Languages for Data Analytics.
CPSC 5195 Machine Learning for Business: Machine learning uses interdisciplinary techniques such as statistics, linear algebra, optimization, and computer science to create automated systems that can sift through large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications in business, government and nonprofit. This class will familiarize students with a broad cross-section of models and algorithms for machine learning, and prepare students for commercial application of machine learning techniques using cloud computing platforms such as Amazon Web Service and Google Cloud Platform. Prerequisite: CPSC 5175 Programming Languages for Data Analytics.
MGT 5210 Big Data Management & Analytics: This course covers the core concepts behind big data problems, applications, and systems. It introduces one of the most common frameworks, Hadoop, that has made big data analysis easier and more accessible. Topics include a discussion of the Big Data landscape, examples of real world big data problems, architectural components and programming models used for structured and unstructured big data analysis, HDFS file system, MapReduce, YARN, PIG, HIVE, NOSQL, and other Big Data programming techniques or platforms. Prerequisite: MGT 5140 - Databases and Data Warehouses.
MGT 5140 Databases and Data Warehouses: The course covers both operational and analytical databases and provide knowledge integral to being successful data analyst in today’s business environment. The fundamental concepts related to operational databases include conceptual design (entity relationship diagram), logical design (normalization) and physical schema. The analytical data warehouse topics include star schema design for data warehouses and data marts. Extract, transform, and load (ETL) is also covered as a technique that ties operational data and data warehouses. The course discusses several database management systems and uses SQL to create and query databases and data warehouses.
MGT 5190: Data Mining and Analytics: This course focuses on hands-on learning of how to use analytical techniques and data mining algorithms to support business decision making. It focuses on the essential exploratory and visualization techniques to maximize insight into a dataset, uncover the underlying structure and determine optimal factor setting. It incorporates extensive use of data, quantitative analysis, statistical and predictive models, and fact-based management to drive decisions and actions. This class uses a real-life data project. Prerequisite: MGT 5835 Quantitative Decision Analysis for Business or department head approval.
MGT 5200: Advanced Data Analytics: This course covers advanced topics related to data analytics. It focuses on practical applications of advanced data mining and machine learning algorithms. Operationalization of analytics in organizations. Major part of the course will focus on analysis of textual data from web, blogs and social media. Natural language processing and text mining algorithms. The focus of this course is hands-on learning of how to use statistical and algorithm-based techniques to solve business problems. The course uses real-life data project. Prerequisite: MGT 5190: Data Mining and Analytics.
(3 to 6 credit hours):
Students can choose to do the following:
Students can choose an Internship with a company for either three or six credits. The internship should not be from the job they are performing with their current employer (if applicable). A faculty member must approve the internship.
Students may choose to do a Practicum Project where a group of students work on a substantial, semester-long project with a company under the guidance of a faculty member with set deliverables. The Practicum Project can be for three or six credits.
Students can complete a Capstone Project involving data collection, cleaning, analysis, model development, and evaluation. The capstone project can be individual or a group of up to three students. The Capstone Project can be for three credit hours.
MGT 5940r (3 credit) Practicum Project (Can be repeated up to two times): The student can work on a significant practicum project provided by a company to gain real life experience. The project will be closely supervised by a UTC faculty member and should represent significant work of value to the organization. A group of two to three students may be involved in a large project.
MGT 5930 (3 Credit) Capstone Project: This course provides students with an opportunity to integrate all the knowledge learned in the Master of Science in Data Analytics program by working on a comprehensive project. The project will involve data collection, cleaning, analysis, model development, and evaluation.
MGT 5920r (3 Credit) Internship (Can be repeated up to two times): Students can do a one or two semester internship with a company to gain real world experience in data analytics. The internship should represent significant work and will be jointly supervised by a faculty member from UTC and a company representative.