MSDA Curriculum

First, choose your program:

100% Online MSDA

• MSDA curriculum will be taught 100% online and you are given registration priority for online classes.

Flexible MSDA

• Offers a mix of face-to-face courses (taught during the evening hours) and online course options.

Learn more about our Master of Science in Data Analytics curriculum as well as any prerequisites for an MSDA that may be required.

Core Courses

Program Coursework: Required Core Courses

Core Courses
(7 total courses; 21 credit hours total required)

DATA 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.

DATA 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. 

DATA 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: DATA 5175 Programming Languages for Data Analytics.

DATA 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.  Prerequisites: One semester of business stats with a C or better or at least 80% in Statistics module or MGT 5835, or department head approval.

DATA 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: DATA 5175 Programming Languages for Data Analytics.

DATA 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: DATA 5190 Data Mining and Analytics.

DATA 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: DATA 5140 Databases and Data Warehouses.

Practicum/Project/Internship

(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.

DATA 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.

DATA 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.

DATA 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.

 

Elective Courses

Students can take courses in a variety of subjects as part of the MS Data Analytics program.

Elective Course Options

Students choose 3 or 4 courses from following depending on internship or practicum project selected

ACC 5855 Accounting for Managers:  The purpose of this course is to provide students with a thorough exposure to the basic elements of financial and managerial accounting from a manager’s perspective.  Emphasis is placed on the application of accounting information both from an external user’s perspective and for internal decision making. Contemporary topics that might affect the use of accounting information are surveyed, including in depth discussion of current events in business and financial news.  The course draws upon the collective business experiences of the participants during classroom discussions that demonstrate the application of key concepts.

ECON 5015 Economics for Managers:  Economics for Managers uses real-­world issues and examples to illustrate how economic principles impact business decisions. Emphases on agency and contract theory, managerial behavioral economics, game theory, and pricing are especially valuable to decision making by managers. In this course, cases use actual data to illustrate the use of basic economic models to solve managerial and economic problems.

FIN 5820 Financial Management:  The goal of this course is to acquaint students with the primary concepts and techniques of financial analysis. The course will build upon the skills obtained in accounting and economics and use those skills for making decisions regarding a firm’s use of capital toward the goal of maximizing the value of the firm. It is assumed that all students are familiar with financial statements and basic statistical and economic principles. The first part of the course will develop the tools used in modern financial analysis, including financial statement analysis and valuation techniques. Latter portions will apply these tools to decision-making for long-term (capital budgeting and cost of capital) financial management for both large and small firms.

MGT 5050 - Evidence Based Management & Improvement in Healthcare: This course examines the role of managers in leading change and supporting improvement efforts within their organization. Special focus will be placed on the importance of working with professionals from across the healthcare environment to maintain improvements over time while balancing the inherent conflict between rising costs and patient expectations. Students will examine the use of different improvement techniques including: lean management, six sigma, customer satisfaction, quality control, high reliability performance, and evidence based protocols. Students will evaluate professional literature and external industry standards to benchmark internal organizational performance against as well as inform improvement effort designs. Students will design a quality/process improvement project to address issues in healthcare delivery and present their quality improvement plan.

MGT 5060 - Healthcare Management: This course explores the challenges that managers experience within complex healthcare organizations including the impact of diverse employee and professional groups on work. Readings and class discussions focus on understanding key management functions (such as planning, staffing, and organization) balanced with mission, financial, and regulatory constraints. Students will examine current issues faced by hospitals, clinics and other healthcare organizations. Current events and cases will be used by students to apply concepts discussed in class.

MGT 5070 - Health Informatics Research Methods: Healthcare informatics is critical for enabling better collaboration and coordination among healthcare providers, streamlining medical quality assurance processes, improving cost-efficiency in healthcare delivery and increasing accuracy and efficiency in facility/practice management.

MGT 5180 Prescriptive Analytics:  This course provides students with a comprehensive overview of the theory and practice of prescriptive analytics which involve large-scale optimization models and provide operational benefits to organizations. Prescriptive analytics can be used to identify the best possible action to take given resource constraints and organizational objectives. The techniques include mathematical programming, simulation, and large-scale optimizations. The course also explores practical challenges encountered in implementing real-life application models such as planning, scheduling, resource allocation, supply chain management, logistics, and marketing. Students will learn the fundamental ideas behind optimization technology, utilize this knowledge to build solvers and transform a given optimization problem into actionable business intelligence.  This course complements descriptive and predictive analytics and connects data driven approaches with their optimum decision-making counterpart.

MGT 5250 Organizational Behavior and Leadership: An examination of the theoretical and research foundations that explain behavior within the context of organizations. The focus will be on how organizational behavior theory is translated into practice such that students will acquire the knowledge and skills necessary to become an effective manager.

MGT 5860 Marketing Management: The goal of this course is to provide a decision-oriented overview of marketing management. This course focuses on the management challenge of designing and implementing marketing strategies that maximize customer satisfaction and firm profitability.