The request to establish a Professional Science Master’s in Data Science and Business Analytics
Date: December 2, 2013
To: Belk College of Business
To: College of Computing & Informatics
From: Office of Academic Affairs
Approved On: November 5, 2013
Approved by: Graduate Council
Implementation Date: Summer 2014
Note: Deletions are strikethroughs. Insertions are underlined.
Summary
Approved on-campus 12/2/2013 and received approval of UNC General Administration and the Board of Governors 4/21/2014. The program will enroll its first class in Fall 2014.
Catalog Copy
Data Science and Business Analytics
- M.S. in Data Science and Business Analytics
- Graduate Certificate in Data Science and Business Analytics
Professional Science Master’s Degree in Data Science and Business Analytics
dsba.uncc.edu
Graduate Certificate in Data Science and Business Analytics
dsba.uncc.edu
The program in Data Science and Business Analytics is a joint venture between the Belk College of Business, College of Computing and Informatics and the Graduate School. The program offers both a Certificate and a Master of Science degree designed to prepare students for the complex and rapidly changing data science and business analytics environment.
Faculty Director
Dr. Mirsad Hadzikadic
343-A Woodward Hall
Belk College of Business
belkcollege.uncc.edu
College of Computing and Informatics
cci.uncc.edu
Graduate School
graduateschool.uncc.edu
Deans
Dr. Steven Ott, Belk College of Business
Dr. Yi Deng, College of Computing and Informatics
Dr. Tom Reynolds, Graduate School
M.S. in Data Science and Business Analytics
The Professional Science Master’s (PSM) program in Data Science and Business Analytics (DSBA) is an interdisciplinary program at the intersection of business, computer and information sciences, statistics and operations research. The program is a unique blend of business acumen, data understanding, exposure to a diverse set of advanced analytics methods, and hands-on experience designed to help students apply learned knowledge on representative business problems. DSBA graduates will be well equipped for employment in a wide variety of data intensive industries, such as financial services, energy, retail/supply chain, or health care, where the need for business analysts with quantitative, computational, and sophisticated analytical skills is growing at an explosive pace.
Admission Requirements
Applicants must meet the general Graduate School requirements for admission to Master’s Degree programs. Applications must include all of the materials listed by the Graduate School as typical for Master’s Degree application submissions. In addition to the general requirements for admission to the Graduate School, the following are the minimum admissions requirements for study toward the M.S. in Data Science and Business Analytics:
- An earned undergraduate degree in any scientific, engineering or business discipline or a closely related field;
- An undergraduate GPA of 3.0 or better;
- Acceptable scores on the verbal, quantitative, and analytical sections of the GRE;
- Positive letters of recommendation;
- A statement of purpose outlining the goals for pursuing a graduate education
- A minimum TOEFL score of 220 (computer-based), 557 (paper-based), or 83 (internet based) or a minimum IELTS band score of 6.5 is required from any applicant whose native language is not English.
In addition, the program requires a current working knowledge of at least one higher-level (procedural) language; and a familiarity with computer applications. The following minimal background in mathematics is also required: two semesters of calculus and one semester of statistics. Individuals who have worked at a high professional level in the computer industry or business may be able to substitute work experience for specific subject area admission requirements. Individuals without a business degree or business experience will be required to complete an online business fundamentals course prior to enrolling in the program.
Degree Requirements
Thirty-three graduate credit hours are required for the DSBA PSM. Of the 33 graduate credit hours, 24 credit hours are required core courses inclusive of 3 hours for the internship, and 9 credit hours of electives. A minimum of 24 credit hours contributing to the M.S. in Data Science and Business Analytics must be from courses numbered 6000 or higher. A maximum of 6 hours of graduate credit may be transferred. Students may apply all of the credits earned in the Graduate Certificate in Data Science and Business analytics toward the M.S. in Data Science and Business Analytics with the approval of the DSBA program director. All students will take the following courses:
Core Requirements
DSBA 6100 Big Data Analytics for Competitive Advantage
ITCS 6160 Database Systems
MBAD 6201 Business Intelligence and Analytics
ITCS 5122 Visual Analytics
ITCS 6156 Machine Learning
MBAD 6211 Advanced Business Analytics
MBAD 6276 Consumer Analytics
DSBA 6400 DSBA Internship
Elective Courses
In addition students will choose 3 elective courses from a growing list of data science and business analytics courses or propose a three-course specialization for approval by the DSBA program director. In choosing their electives (3 courses) students must select at least one course from each of the following areas:
Data Science Electives:
ITIS 5510 Web Mining
ITCS 5121 Information Visualization
ITCS 6155 Knowledge Based Systems
ITIS 6500 Complex Adaptive Systems
ITIS 6520 Network Science
ITCS 6190 Cloud Computing for Data Analysis
Business Analytics Electives:
MBAD 6122 Decision Modeling and Analysis via Spreadsheets
MBAD 6208 Supply Chain Management
MBAD 6207 Project Management
MBAD 6277 Social Media Marketing and Analytics
MBAD 6278 Innovation Analytics
ECON 6112 Graduate Econometrics
Student-Structured Electives Option:
Students may propose a three-course specialization (9 credit hours) in a significant area of interest for approval by the Director of the PSM DSBA Program. In addition to the courses listed in the Data Science and Business Analytics specializations listed above, this specialization may include graduate courses from MS in CS, MS in IT, MBA, MS in Applied Statistics, MS in Mathematical Finance, MS in Economics, and other programs or Departments within the University with approval of the related Department.
Graduate Certificate Program in Data Science and Business Analytics
The purpose of the Graduate Certificate in Data Science and Business Analytics is to provide post-baccalaureate students with the opportunity to reach a demonstrated level of competence in the area of data science and business analytics. The certificate requires fifteen (15) graduate credit-hours of coursework. The certificate may be pursued concurrently with a related graduate degree program at UNC Charlotte.
Admission Requirements
The certificate in DSBA is open to all students who hold a B.S. or M.S. degree in any scientific, engineering or business discipline and either
- are enrolled and in good standing in a graduate degree program at UNC Charlotte or
- complete their undergraduate degree with a minimum 3.0 GPA.
In addition, the program requires a current working knowledge of at least one higher-level (procedural) language; and a familiarity with computer applications. The following minimal background in mathematics is also required: two semesters of calculus and one semester of statistics. Individuals who have worked at a high professional level in the computer industry or business may be able to substitute work experience for specific subject area admission requirements. Individuals without a business degree or business experience will be required to complete an online business fundamentals course prior to enrolling in the program.
Transfer credit from another institution will not be accepted into this proposed certificate program.
Students pursuing the MS in Computer Science, MS in Information Technology and MBA degrees will have priority on space in the corresponding CS, SIS and MBA classes should demand for the proposed certificate exceed expectations.
Program Requirements
The certificate will be awarded upon completion of five graduate level courses (15 credits) in the area of data science and business analytics. A cumulative GPA of 3.0 will be required and at most one course with a grade of C may be allowed towards the certificate.
Students must take five courses, as outlined below, to receive the Graduate Certificate in Data Science and Business Analytics:
Core requirements:
DSBA 6100 Big Data Analytics for Competitive Advantage (3)
ITCS 6160 Data Base Systems (3)
MBAD 6201 Business Intelligence and Analytics (3)
One of the following courses:
ITCS 5122 Visual Analytics (3)
IT IS 6520 Network Science (3)
One of the following courses:
MBAD 6122 Decision Modeling and Analysis (3)
MBAD 6211 Advanced Business Analytics (3)
MBAD 6276 Consumer Analytics (3)
Courses in Data Science and Business Analytics
DSBA 6100. Big Data Analytics for Competitive Advantage. (3) This course provides an introduction to the use of big data as a strategic resource. A focus is placed on integrating the knowledge of analytics tools with an understanding of how companies leverage data analytics to gain strategic advantage. A case approach will be used to emphasize hands-on learning and real-world view of big data analytics. (Fall, On Demand).
ITCS 6160. Database Systems. (3) Cross-listed as HCIP 6160. Prerequisite: Full graduate standing in Computer Science or consent of the department. This course covers modeling, programming, and implementation of database systems. It focuses on relational database systems but may also address non-relational databases or other advanced topics. Major topics are (1) modeling: conceptual data modeling, ER diagram, relational data model, schema design and refinement; (2) programming: relational algebra & calculus, SQL, constraints, triggers, views; (3) implementation: data storage, indexing, query execution, query optimization, and transaction management; and (4) advanced: semi-structured data model, XML, and other emerging topics. (Fall, Spring)
ITCS 5122. Visual Analytics. (3) Prerequisites: Graduate Standing and undergraduate course in statistics, or permission of the instructor. This course introduces the new field of visual analytics, which integrates interactive analytical methods and visualization.. Topics include: critical thinking, visual reasoning, perception/cognition, statistical and other analysis techniques, principles of interaction, and applications. (Fall)
ITCS 6156. Machine Learning. (3) Prerequisite: ITCS 6150 or permission of the instructor. Machine learning methods and techniques including: acquisition of declarative knowledge; organization of knowledge into new, more effective representations; development of new skills through instruction and practice; and discovery of new facts and theories through observation and experimentation. (Fall, Odd years)
MBAD 6201. Business Intelligence and Analytics (3) Prerequisite: MBAD 5121 or equivalent. An overview of the business approach to identifying, modeling, retrieving, sharing, and evaluating an enterprise’s data and knowledge assets. Focus is on the understanding of data and knowledge management, data warehousing, data mining (including rule-based systems, decision trees, neural networks, etc.) and other business intelligence concepts. Covers the organizational, technological and management perspectives. (Fall, On demand)
MBAD 6211 Advanced Business Analytics (3) Pre-requisite: MBAD 6201 or ITCS 6162 or consent of the department. An in-depth study of applications of data analytics techniques to discover non-trivial relationships that are useful and actionable to decision makers. A case approach will be used to emphasize hands-on learning and real-world deployment of business analytics. (Spring, On Demand)
MBAD 6276. Consumer Analytics. (3) Prerequisite: MBAD 6270 or permission of the department. The utilization of analytics techniques in marketing decision-making and consumer strategy. This involves the extraction of hidden insight about consumers from structured and unstructured Big Data, and the translation of that insight into a market advantage. Applications in areas such as consumer targeting, product innovation and promotion strategy (Fall)
DSBA 6400. Internship. (3) Prerequisite: Completion of 21 credit hours of core course requirements. A data science or business analytics project is chosen and completed under the guidance of an industry partner. Each student’s internship project program must be approved by the program director. A proposal form must be completed and approved prior to registration and the commencement of the internship. A mid-term report and a final report to be evaluated by the industry partner and supervising faculty. Grading will be by the supervising faculty in consultation with off-campus supervisor at the internship organization. Graded on a Pass/No Credit basis. (Fall, Spring, Summer)
MBAD 6277. Social Media Marketing and Analytics. (3) Prerequisite: MBAD 6270 or permission of the department.. The utilization of social media in marketing strategy and tactics. Topics include the use of social media in building brand strength and equity, as a customer acquisition tool and as a customer relationship management tool. The utilization of analytics in effective social media marketing. (Summer)
MBAD 6278. Innovation Analytics. (3) Prerequisite: MBAD 6270 or permission of the department.. The comprehension and application of text analytics as a tool to examine unstructured qualitative information to generate innovations. Identifying the various sources of consumer insight and using them in innovation strategy. Understand how to differentiate between what consumers want versus what they say. (Spring)
MBAD 6122. Decision Modeling & Analysis via Spreadsheets. (3) Prerequisite: MBAD 5141 or equivalent. This course focuses on the role operations research/management science plays in the decision making process. Specific topics covered in this course include fundamental techniques such as linear, integer, goal and multi objective programming, queuing theory and applications, decision support via Monte Carlo simulation, decision making under uncertainty and risk, decision trees, and multi-criteria decision making. The emphasis is on models that are widely used in all industries and functional areas, including operations, supply chain management, finance, accounting, and marketing. (Spring, On Demand)
MBAD 6208. Supply Chain Management. (3) Prerequisites: MBAD 6141 or permission of the department. Supply chain management is concerned with all of the activities performed from the initial raw materials to the ultimate consumption of the finished product. From a broad perspective, the course is designed to examine the major aspects of the supply chain: the product flows; the information flows; and the relationships among supply chain participants. The course content is interdisciplinary in nature and will cover a variety of topics such as supply chain information technologies, supply chain design, strategic alliances between supply chain participants and supply chain initiatives. (Spring, On demand)
MBAD 6207. Business Project Management. (3) Project management is widely used in a variety of business environments to manage complex, non-routine, endeavors. Examples of projects include consulting and process improvement projects, advertising projects, and technology projects. This course focuses on tools, techniques and skills for business project management, with attention to both the quantitative and the qualitative aspects of project management. Major topics include project evaluation, estimation, monitoring, risk management, audit, managing global projects, outsourcing and project portfolio management. Students will also gain experience using Project Management Software. (Spring)
ECON 6112. Graduate Econometrics. (3) Prerequisites: Admission to graduate program and permission of program coordinator. Advanced study of the theory and application of statistics to economic problems. Topics include: derivation of least-squares estimators; maximum likelihood estimation; and problems of multicollinearity, heteroskedasticity, and autocorrelation. (Fall, Spring)
ITCS 6155. Knowledge Based Systems. (3) Prerequisite: ITCS 6162 or permission of the instructor. Knowledge systems; knowledge discovery; association rules; action rules, hierarchical classifiers, cascade classifiers, query languages and their semantics; cooperative and collaborative systems; ontology and metadata; flexible query answering; chase algorithms and data sanitization methods; decision support systems in medicine, automatic indexing of music. (Spring)
ITIS 5510. Web Mining. (3) Pre- or corequisites: ITIS 5160. (3) and full graduate standing, or permission of department. Topics include: measuring and modeling the Web; crawling, Web search and information retrieval; unsupervised learning, supervised learning, semi-supervised learning in Web context; social network analysis and hyperlink analysis; text parsing and knowledge representation. (Spring)
ITIS 6500. Complex Adaptive Systems. (3) Cross-listed as ITCS 8500, ITIS 6500, and ITIS 8500. Prerequisite: Permission of instructor. Complex adaptive systems (CAS) are networked (agents/part interact with their neighbors and, occasionally, distant agents), nonlinear (the whole is greater than the sum of its parts), adaptive (the system learns to change with its environment), open (new resources are being introduced into the environment), dynamic (the change is a norm), emergent (new, unplanned features of the system get introduced through the interaction of its parts/agents), and self-organizing (the parts organize themselves into a hierarchy of subsystems of various complexity). Ant colonies, networks of neurons, the immune system, the Internet, social institutions, organization of cities, and the global economy are a few examples where the behavior of the whole is much more complex than the behavior of the parts. Examples of current research efforts are provided. Topics include: Self-organization; emergent properties; learning; agents; localization affect; adaptive systems; nonlinear behavior; chaos; complexity. (On demand)
ITCS 5121. Information Visualization. (3) Prerequisite: Graduate standing or permission of the instructor. Information visualization concepts, theories, design principles, popular techniques, evaluation methods, and information visualization applications. (Spring)
ITIS 6520. Network Science. (3) Networks are all around us, including natural and man-made systems. Examples include rivers, trees, arteries, highways, brain, economy, social connections, military, energy distribution, cyber attacks, terrorist networks, epidemics, Internet, and Facebook. Network Science helps students design faster, more resilient communication networks; revise infrastructure systems such as electrical power grids, telecommunications networks, and airline routes; model market dynamics; understand synchronization in biological systems; and analyze social interactions among people. It examines the various kinds of networks (regular, random, small-world, influence, scale-free, and social) and applies network processes and behaviors to emergence, epidemics, synchrony, and risk. The course integrates concepts across computer science, biology, physics, social network analysis, economics, and marketing. In this class students will learn (a) the basic principles, concepts, and principles of networks; (b) how and why network structures and properties determine the performance and sustainability of any system; (c) how to measure and evaluate network-based systems; (d) how to utilize networks for the benefit of their organizations and society; and (e) how to utilize and design tools for understanding, visualizing, and applying the principles of networks. (On Demand).
ITCS 6190. Cloud Computing for Data Analysis. (3) Prerequisites: ITCS 6114 or permission of department. Familiarity with Java, Unix, Data structures and Algorithms, Linear Algebra, and Probability and Statistics are expected. Students should have good programming skills and a solid mathematical background. This course will introduce the basic principles of cloud computing for data-intensive applications. It will focus on parallel computing using Google’s MapReduce paradigm on Linux clusters, and algorithms for large-scale data analysis applications in web search, information retrieval, computational advertising, and business and scientific data analysis. Students will read and present research papers on these topics, and implement programming projects using Hadoop, an open source implementation of Google’s MapReduce technology, and related technologies for analyzing unstructured data. (Spring, long form in process).