STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA Program Proposal Cover Sheet

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1 STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA Program Proposal Cover Sheet 1. Institution George Mason University 3. Title of proposed program Data Analytics 2. Program action (Check one): New program proposal X Spin-off proposal Certificate proposal 4. CIP code Degree designation Master of Science (M.S.) 6. Term and year of initiation Fall a. For a proposed spin-off, title and degree designation of existing degree program 7b. CIP code (existing program) 8. Term and year of first graduates Fall Date approved by Board of Visitors 10. For community colleges: date approved by local board date approved by State Board for Community Colleges 11. If collaborative or joint program, identify collaborating institution(s) and attach letter(s) of intent/support from corresponding chief academic officers(s) 12. Location of program within institution (complete for every level, as appropriate). Departments(s) or division of Dean s Office School(s) or college(s) of Volgenau School of Engineering Campus(es) or off-campus site(s) Fairfax Distance Delivery (web-based, satellite, etc.) Not applicable 13. Name, title, telephone number, and address of person(s) other than the institution s chief academic officer who may be contacted by or may be expected to contact Council staff regarding this program proposal. Dr. Stephen G. Nash, Senior Associate Dean, Volgenau School of Engineering, , snash@gmu.edu Robin Parker, Director, Compliance, Provost s Office, , rparkera@gmu.edu

2 TABLE OF CONTENTS DESCRIPTION OF THE PROPOSED PROGRAM... 1 PROGRAM OVERVIEW (BACKGROUND)... 1 MISSION... 2 ADMISSIONS CRITERIA... 2 TARGET POPULATION... 4 CURRICULUM... 4 STUDENT RETENTION AND CONTINUATION PLAN... 8 FACULTY... 8 STUDENT ASSESSMENT... 8 PROGRAM ASSESSMENT EXISTING PROGRAMS COLLABORATION OR STANDALONE JUSTIFICATION FOR THE PROPOSED PROGRAM RESPONSE TO CURRENT NEEDS EMPLOYMENT DEMAND STUDENT DEMAND DUPLICATION i

3 Description of the Proposed Program Program Overview (Background) The Volgenau School of Engineering proposes a new interdisciplinary Master of Science program in Data Analytics that integrates several of our areas of expertise. Data analytics (i.e., the process of acquiring, extracting, integrating, transforming, and modeling data with the goal of deriving useful information) is becoming an important quantitative methodology in a wide variety of applications. The need for data analytics is due to the massive accumulation of Big Data in all industries to include but not limited to healthcare, finance, government (federal, state, and local), and cyber defense. Although the Volgenau School has for many years offered courses that address individual aspects of data analytics, these courses are spread across several of our departments, and have not been integrated into a single program suitable for students interested in studying this topic. New courses, based on existing ones, have been specifically developed for this new degree. The degree provides an overview of many aspects of data analytics, integrating technical areas into a unified program accessible to students from a variety of backgrounds. It also provides students the opportunity to make a more detailed study of specialized aspects of data analytics. To complete the degree the students will take part in a team project that integrates the educational topics and applies them to a significant applied problem. The proposed M.S. degree is an expansion of our already-approved graduate certificate in Data Analytics. Our School has long offered specialized training in information technology, statistics, computer science, and operations research. Each of these areas contributes to the field of data analytics. But students would have to enroll in at least four separate Master s programs to accumulate the relevant knowledge for all of these disciplines. Our goal in proposing the new M.S. in Data Analytics is to make available within one degree the necessary context and specialized knowledge necessary in this rapidly growing field. The sponsoring unit is the Volgenau School of Engineering. The program will be managed through the Dean s office. The concentrations are associated with individual departments, and advisors will be chosen from those departments based on the student s academic focus. The correspondence between concentrations and departments is: Applied Information Technology concentration: Applied Information Technology Department Computer Science concentration: Computer Science Department Digital Forensics concentration: Electrical & Computer Engineering Department Predictive Analytics concentration: Systems Engineering & Operations Research Department Statistics concentration: Department of Statistics The planned implementation date for the proposal degree is Fall Program Curriculum E-1

4 The proposed M.S. in Data Analytics will consist of a set of five core courses along with a set of five concentrations: computer science, prescriptive analytics, statistics, digital forensics, and applied information technology. We expect most students will elect one of the concentrations, but we also allow students, with approval of their advisor, to choose a coherent set of electives from several of the concentrations. The core courses are designed to provide an overview of the main technical areas of Data Analytics. A capstone course will require students to conduct an applied team project that integrates an array of topics from the degree. The core courses will address the following areas: Analytics and Decision Analysis Data Mining Big Data and Advanced Analytics Statistical Modeling & Visualization Mission The mission of George Mason University is A public, comprehensive, research university established by the Commonwealth of Virginia in the National Capital Region, we are an innovative and inclusive academic community committed to creating a more just, free, and prosperous world. The proposed M.S. degree in Data Analytics is related to several elements of the university s mission. First, the degree is innovative. This is an emerging discipline and, although its importance has been recognized in many application areas, there are few universities in the nation offering graduate education focused on data analytics. Second, because of the many application areas that could benefit from data analytics, it has the potential to contribute to regional and national prosperity as the graduates apply their training to important real-world problems. Admissions Criteria All applicants to graduate programs at George Mason University must submit: A completed application for graduate study A nonrefundable application fee Two official transcripts from all previous institutions attended Application for Virginia In-State Tuition Rates, if claiming entitlement to these rates In addition, students who have not earned a degree in the U.S. must submit: Official English translations of all diplomas, certificates, and transcripts that are not already in English. Also, documents from foreign institutions must meet the university s guidelines for international transcript submission. Proof of English proficiency: either TOEFL, IELTS academic exam, or Pearson Test of English meeting the minimum requirements E-2

5 Applicants to graduate programs in the Volgenau School of Engineering must have a minimum overall GPA of 3.0 for admission. Applicants to the M.S. in Data Analytics should have an undergraduate degree from an accredited institution, with a GPA of at least 3.0 in their last 60 credits of study. While no specific undergraduate degree is required, a background in engineering, business, computer science, statistics, mathematics, or information technology, is desirable, or alternatively strong work experience with data or analytics may be used. Students must submit the following Three letters of recommendation from former professors or supervisors Self-evaluation form Resume Goals statement For some of the concentrations, there are additional admission requirements. These are listed below. Computer Science In addition to the above, the admission requirements for the Computer Science concentration are: Hold a baccalaureate degree, preferably with a major in a technical field such as computer science, mathematics, physics, engineering, or information systems; Have taken Data Structures (CS 310), Formal Methods and Models (CS 330), and Computer Systems Architecture including Assembly Programming (CS 367 and CS 465) or the equivalents of these George Mason undergraduate courses. Have completed one year of mathematics beyond first-year calculus, including a substantial course in discrete mathematics (e.g., MATH 125). Students with some deficiencies in preparation may be admitted provisionally pending completion of foundation courses in mathematics or computer science. Undergraduate credit earned for this purpose may not be applied toward the graduate degree. Submit an official GRE General test score report. Digital Forensics In addition to the above, for the Digital Forensics concentration, the student must satisfy certain prerequisites in the areas of computer operating systems and computer networking. These are: In computer operating systems, the student must show course work equivalent to: o IT 342 (Operating Systems Fundamentals) In computer networking, the student must show work equivalent to: o IT 441 (Network Servers and Infrastructures) or TCOM 501/502 (Modern Telecommunications / Data Communications and Local Area Networks), o IT 341 (Data Communications and Network Principles) or TCOM 509/529 (Internet Protocols / Advanced Internet Protocols), and E-3

6 Prescriptive Analytics o IT 445 (Advanced Networking Principles) or TCOM 515 (Internet Protocol Routing). In addition to the above, for the Prescriptive Analytics concentration, the requirements for admission are as follows: Evidence of satisfactory completion of courses in calculus, applied probability and statistics, and a scientific programming language. Students who have not earned a degree in the U.S. must also achieve satisfactory scores on the GRE. Familiarity with analytical modeling software, such as spreadsheets or math packages, is also expected. Students should acquaint themselves with these software packages before beginning classes. Statistics In addition to the general requirements above, students in this concentration must have completed three semesters of calculus, a calculus-based probability course, and matrix algebra. Target Population The proposed M.S. in Data Analytics aims to provide students with the skills necessary to develop techniques and processes for data-driven decision-making. Students study topics such as data mining, information technology, statistical models, predictive analytics, optimization, risk analysis, and data visualization. It is aimed at students who wish to become data scientists and analysts in finance, marketing, operations, business intelligence and other information intensive groups generating and consuming large amounts of data. Curriculum The proposed M.S. in Data Analytics is a 30 credit-hour program, and consists of a set of five core courses (15 credit-hours total) along with a set of five concentrations (15 credit-hours each). The five concentrations correspond to specialized technical areas: Applied Information Technology Computer Science Digital Forensics Prescriptive Analytics Statistics We expect most students will elect one of the concentrations. If a student is not interested in a concentration, the student can work with an advisor to select 15 credits of electives from the courses allowed for the concentrations. Core courses (15 credits): E-4

7 The core course work covers the basic elements of data analytics at the graduate level. The core courses (and the alternative core courses for some of the concentrations) are listed below: AIT 580 Analytics: Big Data to Information CS 504 Principles of Data Management and Mining o Computer Science concentration: substitute CS Theory and Applications of Data Mining OR 531 Analytics & Decision Analysis STAT 515 Applied Statistics & Visualization for Analytics o Statistics concentration: substitute STAT 554 Applied Statistics IT 690 Data Analytics Project AIT 580 provides an overview of Big Data and its use in commercial and government contexts. STAT 515 introduces relevant topics in statistics including multivariate regression, random forests for modeling data, model diagnostics, and the foundations for visual thinking. OR 531 focuses on prescriptive analytics with some parts focused on predictive analytics. CS 504 teaches techniques to store, manage, and use data including databases, relational model, schemas, queries and transactions. Finally, IT 690 is the capstone project course. Each of these courses is 3 credits. Applied Information Technology Concentration Requirements (15 credits) The concentration in Applied Information Technology focuses on the practical elements of adapting big data approaches to common analytic problems and to government protocols. Students in this concentration must complete the following courses: AIT 581 Problem Formation and Solving in Big Data AIT 582 Applications of Metadata in Complex Big Data Problems AIT 665 Managing Information Technology Programs in the Federal Sector AIT 679 Law & Ethics of Big Data AIT 697 Leading Organizations Through Change Computer Science Concentration Requirements (15 credits) The Computer Science concentration is aimed at students who are interested in understanding data mining, advanced database systems, MapReduce programming, pattern recognition, decision guidance systems, and Bayesian inference as they relate to data analytics. All students in Computer Science track must take: CS 757 Mining Massive Datasets Using MapReduce In addition, students must select four courses from the following list (prerequisites must be respected): CS Database Systems CS Introduction to Artificial Intelligence CS Advanced Database Management CS Pattern Recognition E-5

8 CS Advanced Pattern Recognition CS Data Mining on Multimedia CS Machine Learning CS Decision Guidance Systems INFS Web Search Engines and Recommender Systems INFS Database Programming for the World Wide Web SYST Bayesian Inference and Decision Theory Digital Forensics Concentration Requirements (15 credits) Digital forensics (DFRS) deals with the process of acquiring, extracting, integrating, transforming, and modeling data with the goal of deriving useful information that is suitable for presentation in a court of law. Digital forensics is a key component in criminal, civil, intelligence, and counterterrorism matters. Students in the DFRS concentration will be able to apply data analytics to such areas as digital media, intercepted (network) data, mobile media, unknown code, and leverage that analysis in order to determine, intent, attribution, cause, effect, and context. All students in this concentration must take: CFRS 500 Introduction to Forensic Technology and Analysis (3 credits) CFRS 660 Network Forensics (3 credits) In addition, students must select three courses from the following: CFRS 510 Digital Forensic Analysis (3 credits) CFRS 661 Digital Media Forensics (3 credits) CFRS 663 Operations of Intrusion Detection for Forensics (3 credits) CFRS 664 Incident Response Forensics (3 credits) CFRS 698 Independent Reading and Research (3 credits) CFRS 761 Malware Reverse Engineering (3 credits) CFRS 762 Mobile Device Forensics (3 credits) CFRS 763 Windows Registry Forensics (3 credits)* CFRS 764 MAC Forensics (3 credits)* CFRS 767 Penetration Testing in Computer Forensics (3 credits)* CFRS 768 Forensic Attribution and Context (3 credits)** CFRS 780 Advanced Topics in Computer Forensics (3 credits) Courses marked * will appear in the George Mason Catalog. Courses marked ** are new, and will appear in the George Mason Catalog. Prescriptive Analytics Concentration Requirements (15 credits) The ultimate goal of analytics of Big Data is to derive value by suggesting effective actions for the future. Prescriptive analytics focuses on the methods for deciding on the best course of action, taken into account possible constraints and risks. The concentration in prescriptive analytics will provide students with skills that drive effective decision making and optimization. Students will learn the techniques to analyze both structured and unstructured data to derive E-6

9 meaningful knowledge, which will be useful for developing effective strategies and making optimal decisions. The concentration emphasizes both analytical and practical aspects of prescriptive analytics. Students are expected to master the practical aspects of modeling and methods for optimization. Students are also expected to demonstrate proficiency in decision making, design of decision support systems, and risk analysis..the program prepares students for careers in big data analytics with a focus on strategic decision making in practical applications including financial engineering, healthcare, transportation, and intelligence. All students in the prescriptive analytics track must take: SYST 542 Decision Support Systems Engineering SYST 573 Decision and Risk Analysis OR 604 Practical Optimization In addition students must select two courses from the following: SYST 508 Complex Systems Engineering Management OR/SYST 538 Analytics for Financial Engineering and Econometrics SYST 584 Heterogeneous Data Fusion SYST/OR 670 Metaheuristics for Optimization SYST 664 Bayesian Inference and Decision Theory STAT Statistical Graphics and Data Exploration I Statistics Concentration Requirements (15 credits) The M.S. in Data Analytics with a Concentration in Statistics will provide students with skills necessary for gaining insight from data. This concentration allows students to evaluate large datasets from a rigorous statistical perspective, including theoretical, computational, and analytical techniques. More specifically, emphasis will be placed on developing deep analytical talent in the two areas of statistical modeling and data visualization. Big Data are well-known to encompass high levels of uncertainty and complex interactions and relationships. To gain knowledge from these data and hence inform decisions, elucidation of the core interactions and relationships must be done in a manner that acknowledges uncertainties in order to both minimize false signals and maximize true discoveries. Statistical modeling does exactly this it accounts for uncertainty while identifying relationships. Visualization is often a critical component of modeling, but visualization also stands alone as an important tool for presentation of information, decision analysis, and process improvement. All students in the Statistics concentration must take the following 5 courses: STAT 544 Applied Probability STAT 652 Statistical Inference STAT 656 Regression Analysis STAT 663 Statistical Graphics and Data Exploration I STAT 772 Statistical Learning E-7

10 Student Retention and Continuation Plan Upon admission to the M.S. program, students will be assigned an academic advisor corresponding the student s chosen concentration. Advisors will be chosen from among the faculty active in the program, and the advisor for each student will be active in the student s concentration. Each student will be required to consult with the advisor to develop a plan of study. (Student s not choosing a concentration will be required to submit a tentative plan of study at time of admission, and this will be used to identify an appropriate advisor that matches the student s interests.) In addition there will be a staff person associated with each concentration who will monitor student progress, and provide administrative assistance to students in the program. Faculty This is an interdisciplinary program that involves faculty from five departments in the Volgenau School of Engineering. Here is a list of the departments participating in the degree, along with the number of faculty from each department who are involved. Applied Information Technology: four Computer Science: seven Electrical & Computer Engineering: two Systems Engineering & Operations Research: five Statistics: five These faculty members have the scholarly expertise to teach the range of courses in the proposed curriculum. Appendix B provides a brief biography of faculty committed to the program. Student Assessment Student learning will be assessed through each course by the qualified instructor. Students will be assessed in each course through various mechanisms that include (1) class participation, (2) homework assignments, (3) term papers, and (4) exams. The program is subject to George Mason University s overall program review. A more comprehensive assessment of student learning will be conducted in connection with the capstone project course. Faculty associated with the degree will attend final presentations and, based on these, will assess whether the program is providing appropriate breadth and depth of training. Specific learning outcomes for the proposed M.S. in Data Analytics have been identified, including outcomes measuring what graduates will know and what they will be able to do. These outcomes are grouped based on the relevant concentration. Graduates of the Applied Information Technology concentration will demonstrate knowledge of: Legal and ethical issues associated with large data sets Use of metadata in data analytics Software and analysis tools for studying large data sets E-8

11 Graduates of the Computer Science concentration will demonstrate knowledge of: Data mining theory and applications Programming in MapReduce Data mining on massive datasets Depending on the electives selected by the student: database management, artificial intelligence, pattern recognition, mining multimedia data, decision guidance systems, information retrieval for the Web and recommender systems, database programming for the Web, and Bayesian inference and decision theory. Graduates of the Digital Forensics concentration will demonstrate knowledge of: Data analytics to include collecting, analyzing, and reporting on stored digital media (i.e. hard drives, flash drives, and optical media) Data analytics to include collecting, analyzing, and reporting on network based data (i.e. full content packet collection and netflow (metadata)) Collecting, analyzing, and reporting on mobile media based data (i.e. cell phones and tablets) Data analytics as it pertains to artifacts generated and stored by operating systems (Windows, Linux, Mac) Data analytics as it pertains to unknown executable code Graduates of the Predictive Analytics concentration will demonstrate knowledge of: Both analytical and practical aspects of data analytics Several quantitative modeling methods. Issues relevant to practical aspects of decision making, data visualization, optimization, and data management. Techniques to analyze both structured and unstructured big data to derive meaningful knowledge, which will be useful for developing effective business strategies and making optimal decisions. Graduates of the Statistics concentration will demonstrate knowledge of: The impact of uncertainty on inferential thinking, and probabilistic mechanisms for modeling uncertainty. Strengths and weaknesses of a large number of methods for estimation and hypothesis testing, and, given a set of data, the ability to choose and correctly execute an appropriate method. Meaning of important terms related to estimation and hypothesis testing. Theory and application of model building in the presence of uncertainty, including assessing model quality and diagnosing adequacy of assumptions. Foundations of visual thinking. Construction and interpretation of a variety of graphical techniques, ranging from among the most common to a number of advanced techniques, with a focus on identifying patterns. The use of statistical software packages to analyze large datasets. To evaluate workplace competencies and employment skills we have examined employment announcements for positions in the area of data analytics, restricting our attention to those that E-9

12 require an M.S. degree. Because this is an emerging discipline, the employment announcements do not ask explicitly for an M.S. in Data Analytics. Instead, the job titles specify analytics, and the educational requirements will specify, for example, M.S. in Statistics, Econometrics, Operations Research, Data Mining, Biostatistics or other data analytics field. (Examples of these employment announcements are included in an appendix.) The employment announcements identify the following competencies and employment skills that are relevant (grouped according to the concentrations in the proposed M.S. program). Applied Information Technology concentration: Ability to access, analyze and draw insight from large datasets Experience with data warehousing concepts, designs, and project implementations. Computer Science concentration: Experience with database software (e.g., SQL) Data file transfer. Dataset and database management. Artificial intelligence. Knowledge of Hadoop technology stack (e.g., Map Reduce) Digital Forensics concentration: Data mining. Collecting, analyzing, and reporting on stored digital media. Collecting, analyzing, and reporting on network based data. Predictive Analytics concentration: Knowledge of optimization principles and concepts. Development and use of forecasting models. Experience with optimization and simulation software. Decision trees. Predictive models. Decision analysis. Building, implementing and/or maintaining predictive models on large datasets. Financial Data Analytics. Bayesian analysis. Statistics concentration: Experience with statistical software (e.g., SAS, R) Statistical modeling capabilities. Regression. Model assessment and selection. Visualization techniques. Multivariate analysis - Clustering, factor, principal component, etc. E-10

13 Almost all the employment announcements emphasize the importance of applying the methodologies, working in teams, writing reports, communication skills, and presentation skills. These requirements are part of the motivation for our including a capstone project as a core course in the M.S. program. The capstone project will require students to work in a small team to apply their training to an applied problem. Existing assessment measures include: Program Assessment Annual reviews of students academic performance by the student s advisor Annual review of graduates academic outcomes Annual exit interviews to assess satisfaction with the program Bi-annual alumni satisfaction survey Bi-annual survey of employers satisfaction with program graduates The proposed M.S. in Data Analytics will be reviewed on the seven-year cycle typical of programs within the Volgenau School of Engineering. Program review takes place under the guidance of the Office of Institutional Assessment and requires four semesters to complete. The outcomes of the process are a series of deliverables a self-assessment report and academic plan written by program faculty and a report by a review team external to the program and changes made to enhance the program. Each core course will have its own Learning Outcomes Assessment Form, which is a summary of aggregate assessment of the specific learning objectives of the subset of students in the class that are currently enrolled in the M.S. in Data Analytics program. The proposed M.S. in Data Analytics will be included in the university s ongoing assessment cycle starting in Finally, the Board of Visitors will conduct its initial review of the program in Benchmarks of Success The program expects to enroll 20 students per year during the initial years of operation. We expect 90% of graduating students to have a full time job offer or promotion within 6 months of graduation. We expect that 80% of employers of these graduate students will express their satisfaction with the preparation of the graduate students for employment with their organization. We believe these Benchmarks are achievable. The faculty will review the program annually to track the progress of the program and to adjust as necessary. If the benchmarks of success are not being met as anticipated, the faculty will discuss what is not being achieved and determine strategies to reach the benchmarks of success. For example, if enrollments are failing to meet the desired benchmark of 20 enrolled students per year, a potential strategy may be to increase the marketing of the program. This is not a spin-off program. Spin-Off E-11

14 Existing Programs This program is an expansion of the graduate certificate in Data Analytics, to be offered by the Volgenau School of Engineering starting in Fall The graduate certificate consists of four of the core courses for the proposed M.S. degree in Data Analytics (AIT 580, CS 504, OR 531, and STAT 515). Collaboration or Standalone This is a standalone program. No other organization was involved in its development, and no other organization will collaborate in its operation. Justification for the Proposed Program Response to Current Needs (Specific Demand) The importance of data analytics is widely recognized. It is a central tool in many businesses (e.g., Google and Amazon), and it is widely used by business and government. It is expected that data analytics will play an important role in economic planning, health care, business modeling, traffic planning, national security, and many other areas. It is only in the last few years that the term analytics has come to be associated with the study of large, unstructured data sets. But it is now developing quickly, with the development of business divisions, professional organizations, and academic programs using this name to describe their activities. Analytics has also been recognized as a critical discipline in contemporary society. There have been many articles and reports that discuss the importance of data analytics. For example: A series of white papers on the theme of Data Analytics: From Data to Knowledge to Action 2, including a white paper on the topic Challenges and Opportunities with Big Data 3. The professional magazine Analytics Magazine 4 devoted to the topic, and published by the Institute for Operations Research and Management Science. The October 2012 issue of the Harvard Business Review 5 which was devoted to the topic of Big Data. These articles make a persuasive case for the importance of data analytics. They also argue that it is important to provide appropriate training in the various aspects of this interdisciplinary topic E-12

15 In From Data to Knowledge to Action: A Global Enabler for the 21 st Century 6 the authors write: A confluence of advances in the computer and mathematical sciences has unleashed unprecedented capabilities for enabling true evidence-based decision making. These capabilities are making possible the large-scale capture of data and the transformation of that data into insights and recommendations in support of decisions about challenging problems in science, society, and government. These advances have come together to create an inflection point in our ability to harness large amounts of data for generating insights and guiding decision making. The shift of commerce, science, education, art, and entertainment to the web makes available unprecedented quantities of structured and unstructured databases about human activities much of it available to anyone who wishes to mine it for insights. To date, we have only scratched the surface of the potential for learning from these largescale data sets. Opportunities abound for tapping our new capabilities more broadly to provide insights to decision makers and to enhance the quality of their actions and policies. The articles in the October 2012 issue of the Harvard Business Review demonstrate the importance of data analytics to the business community. The articles also explain that although this topic is of immense importance, there are now relatively few academic programs that prepare workers in this field. In the article Data Scientist: The Sexiest Job of the 21 st Century, the authors Thomas H. Davenport and D.J. Patil point to the M.S. program in Analytics at North Carolina State. Another existing program is the M.S. program in Analytics at Northwestern University. Its website makes the following comments: The use of data to inform decision-making and strategy is not new but with new technologies and new analytic tools and techniques, never before has such a high volume of quality data been available to businesses and other organizations. As a result, the field of analytics will continue a positive, upward trend, outpacing many other occupational fields. The demand for analytic skills is supported by research from the U.S. Bureau of Labor Statistics, which predicts that by 2018 there will be a 13 percent increase in the need for statisticians, a 22 percent increase in demand for operations research analysts and a 24 percent increase in management analysts. 7 And while the demand for analytic skills crosses multiple industries, fields such as marketing, health care and finance are expected to experience a particularly strong need for analytics professionals. According to IDC, a provider of market intelligence consulting services, the business analytics market will grow by at least 6.1 percent in 2010 alone. 8 The growth may IDC, 2010 State of the U.S. Business Analytics Market E-13

16 stem, in part, from a growing awareness of the power of data-driven strategies: another IDC study showed that businesses that used predictive technologies experienced a much higher return on investment (145 Percent) than those that did not (89 percent). 9 A Forrester Research study showed that business analytics is the fastest growing category of global IT software expenditures, and approximately 69 percent of businesses are interested in using analytics. 10 As these quotations and citations indicate, data analytics is an important tool for modern society, and its importance and influence are growing rapidly. There is a need for trained professionals to support business, governmental, scientific, and societal demands in this area. Currently, activities in this area are being conducted by individuals trained in individual disciplines such as statistics, computer science, and operations research, but going forward there is recognition that focused training in data analytics could provide enormous benefits. The Volgenau School of Engineering at George Mason University is well positioned to provide such training. Data Analytics integrates a mix of disciplines, including computer science, statistics, operations research, and information technology. Ours is perhaps the only engineering school in the nation that includes all these disciplines. We have departments of Statistics, Computer Science, Applied Information Technology, and Systems Engineering & Operations Research. All these departments have faculty who have extensive teaching and research activities in these disciplines. Thus we have the relevant personnel and experience to provide an interdisciplinary M.S. degree in Data Analytics. Employment Demand The labor market information from the U.S. Department of Labor Statistics and the Virginia Employment Commission indicates an increasing demand for individuals with the skills that form the focus for the M.S. in Data Analytics. The data from these organizations complements the discussion in the previous section. The Occupational Outlook Handbook at the U.S. Bureau of Labor Statistics 11 does not have specific data for analytics but it does include reports for the following related disciplines: Operations Research Analysts o Job Outlook, : 15% o Employment Change, : 9,400 Statisticians o Job Outlook, : 14% o Employment Change, : 3,500 Database Administrators o Job Outlook, : 31% o Employment Change, : 33,900 9 IDC, Predictive Analytics and ROI: Lessons from IDC s Financial Impact Study 10 Forrester Research, The State of Business Intelligence Software and Emerging Trends [accessed 04/04/13] E-14

17 As we have mentioned, data analytics is an emerging field, so it is difficult to obtain employment projections that focus precisely on this field. Similarly, at the Virginia Workforce Connection website 12 there are no data specifically for data analytics but we were able to obtain data for the following categories: Computer and Mathematical Occupations o Total % Change, : 34.1% o Annual Avg. % Change: 3.0% o Employment Change, : 67,976 Database Administrators o Total % Change, : 39.3% o Annual Avg. % Change: 3.4% o Employment Change, : 2,310 Industrial Engineers o Total % Change, : 13.8% o Annual Avg. % Change: 1.3% o Employment Change, : 589 Mathematical Scientists o Total % Change, : 26.3% o Annual Avg. % Change: 2.4% o Employment Change, : 1,636 Operations Research Analysts o Total % Change, : 26.1% o Annual Avg. % Change: 2.3% o Employment Change, : 1,152 Statisticians o Total % Change, : 19.8% o Annual Avg. % Change: 1.8% o Employment Change, : 103 In an appendix we provide detailed information on specific employment opportunities. Student Demand Financial services, healthcare and national intelligence & defense are exemplar global sectors with vital needs driving exploration and development of approaches to operate with truly enormous data sets, from which the finest detail can be extracted, tagged at the smallest possible level and processed in fractions of time. Scope, scale and especially the complexity of this wicked problem redefine the terms of everything. Beyond the capacity, efficiency and speed of computers and networks, it changes the very nature of the analytics needed to produce results. It even changes the management challenges of contracting, project control, finance and budgeting and, in the leadership arena, organization structures and professional development [accessed 04/04/13] E-15

18 The Volgenau School of Engineering is proposing a new M.S. in Data Analytics in response to demand from businesses and government agencies. Some of the material in the degree program has been presented to more than three hundred U.S. Government staffers attending two separate Big Data Lecture Series featuring marquee speakers on these same topics delivered by Mason s Volgenau School of Engineering. The attendees emphasized keen interest in individual courses, a Graduate Certificate, and a full Master of Science degree program in Data Analytics. The Master of Science in Data Analytics degree program proposed by George Mason University offers graduate level preparation needed to drive mission success for industry professionals. Its broad program of courses addresses learning needs across organization, not simply each functional vertical. Its structure offers a truly rare opportunity for the entire organization to learn to acquire, design, build and engage tools in ways that will permit the acquisition of data that delivers and displays information heretofore unattainable. The plans for an M.S. degree in Data Analytics have been presented to our industrial advisory boards (both the board for the Volgenau School of Engineering, as well as boards for individual departments). The members of the boards have expressed strong interest in the development of a Master s program in this area. We are in the process of conducting a survey of undergraduate students and external communities to provide more detailed data on the potential interest in the proposed degree. STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA SUMMARY OF PROJECTED ENROLLMENTS IN PROPOSED PROGRAM Projected enrollment: Year 1 Year 2 Year 3 Year 4 Target Year (2-year institutions) Year 5 Target Year (4-year institutions) HDCT 20 FTES 11 HDCT 38 FTES 20 HDCT 52 FTES 28 HDCT 49 FTES 26 GRAD -- HDCT 79 FTES 41 GRAD 18 Assumptions: 90% Retention 10% Full-time students/ 90% Part-time students Full-time students: 9 credit hours Part-time students: 6 credit hours Full-time students graduate in 2 years E-16

19 Part-time students graduate in 3 years Duplication We have not found any existing degree programs at public colleges and universities in the Commonwealth that duplicate the proposed M.S. in Data Analytics. There are, however, two graduate programs that we have identified that have some overlap with the proposed program. They are: A concentration in Decision Sciences and Business Analytics within the M.S. in Business at Virginia Commonwealth University 13 (CIP code ) The M.S. program in Information Systems and Virginia Commonwealth University 14 (CIP code ) We did searches based on program names and CIP codes 15, but we were not able to identify other directly relevant graduate programs at public colleges and universities in the Commonwealth. Neither of these duplicates the proposed M.S. in Data Analytics, although both include some topics that are part of the proposed M.S. in Data Analytics. The concentration in Decision Sciences and Business Analytics is part of a degree in Business, not Engineering. It considers some of the same topics, but at a less technical and quantitative level. The concentration has the following required courses: INFO 664 Information Systems for Business Intelligence MGMT 632 Statistical Analysis MGMT 645 Management Science OPER 528 Stochastic Simulation Students also must complete six credit hours by taking two of the following four courses: INFO 614 Data Mining MGMT 643 Applied Multivariate Methods MGMT 669 Forecasting Methods for Business MGMT 677 Quality Management and Six Sigma Two of the four required courses have overlap with the proposal M.S. (MGMT 632 and OPER 528). Two of the four electives also have overlap (INFO 614 and MGMT 643). But many topics in the proposed M.S. program are not represented in the concentration (e.g., predictive analytics and visualization). The M.S. program in Information Systems focuses on the design of information systems, not on the mathematical and quantitative tools of data analytics. This M.S. program as the following core courses: INFO 610 Analysis and Design of Database Systems INFO 620 Data Communications INFO 630 Systems Development INFO 640 Information Systems Management [accessed 04/04/13] E-17

20 (Only the first of these courses overlaps the proposed M.S.) Students must also select six electives from the following list INFO 611 Data Re-engineering INFO 614 Data Mining INFO 616/CISS 616 Data Warehousing INFO 622/CISS 622 Network Security and Administration INFO 632 Business Process Engineering INFO 641 Strategic Information Systems Planning INFO 642 Decision Support and Intelligent Systems INFO 643 Information Technology Project Management INFO 644/CISS 644 Principles of Computer and Information Systems Security INFO 646 Security Policy Formulation and Implementation INFO 654 Systems Interface Design INFO 658 Electronic Commerce INFO 691 Topics in Information Systems INFO 693 Field Project in Information Systems INFO 697 Guided Study in Information Systems Of these requirements, only the first core course and the first three electives overlap the proposed M.S. Many other topics in the proposed M.S. are not part of this degree (e.g., statistical visualization and modeling, predictive analytics). Enrollments and Degrees Awarded for these programs are listed in Table 1. Table 1. Enrollments and Degrees Awarded at Comparable Programs in the Commonwealth Fall Fall Fall Fall Fall Enrollments VCU: CIP * VCU: CIP Degrees Awarded VCU: CIP * VCU: CIP * We were not able to obtain separate data for the concentration in Decision Sciences and Business Analytics Projected Resource Needs The Volgenau School of Engineering has the faculty, staff, equipment and space to launch and maintain the proposed MS in Data Analytics. The following paragraphs detail the resources required to operate and sustain the proposed program. Assessments of need are based on the 16 State Council of Higher Education for Virginia (SCHEV). Fall Headcount Enrollment by Race/Ethnicity, Gender and Program Detail. (Accessed 04/04/13). 17 State Council of Higher Education for Virginia (SCHEV). Completion, Program Detail C (Accessed 04/04/13). E-18

21 following ratio of student enrollment to faculty effort for graduate programs: 9.0 FTE requires 1.0 FTE of instructional support. Therefore, the proposed program will require a total of 1.20 FTE of instructional support to begin in fall 2014, rising to 4.50 FTE by the target year of Full-time Faculty We project that the proposed MS in Data Analytics will require 1.20 FTE of full-time faculty effort, rising to 4.50 FTE by the target year of Part-time Faculty from Other Academic Units The proposed program will not require part-time faculty to launch or maintain the proposed program. Adjunct Faculty The proposed program will not require adjunct faculty to launch or maintain the program. Graduate Assistants No graduate students are required to support the MS in Data Analytics. Classified Positions The proposed program will require 0.25 FTE of classified support to launch, rising to 0.50 FTE of classified support by the target year of Targeted Financial Aid No targeted financial aid is required to support the proposed program. Equipment Because no new faculty are required to launch or maintain the proposed program, there are no new equipment costs. Library George Mason University libraries routinely commit $3000 to the purchase of research journals and books for new master s degree programs. Telecommunications Because no new full-time faculty or staff are required to launch or maintain the program, we project no new telecommunication costs. Space No additional space is required to launch or maintain the proposed program. Other Resources No other resources will be needed. E-19

22 PROJECTED RESOURCE NEEDS FOR PROPOSED PROGRAM Part A: Answer the following questions about general budget information. Has or will the institution submit an addendum budget request to cover one-time costs? Yes No Has or will the institution submit an addendum budget request to cover operating costs? Yes No Will there be any operating budget requests for this program that would exceed normal operating budget guidelines (for example, unusual faculty mix, faculty salaries, or resources)? Yes No Will each type of space for the proposed program be within projected guidelines? Yes No Will a capital outlay request in support of this program be forthcoming? Yes No Part B: Fill in the number of FTE positions needed for the program Expected by Program Initiation Year Target Enrollment Year On-going and reallocated Added (New) Added (New)*** Total FTE positions Full-time faculty FTE* Part-time faculty FTE** Adjunct faculty Graduate assistants Classified positions TOTAL * Faculty dedicated to the program. **Faculty effort can be in the department or split with another unit. ***Added after initiation year. E-20

23 Part C: Estimated resources to initiate and operate the program Expected by Program Initiation Year Target Enrollment Year Full-time faculty salaries $108,000 $0 $297,000 $405,000 fringe benefits $33,145 $0 $91,149 $124,295 Part-time faculty (faculty FTE split with unit(s)) salaries $0 $0 $0 $0 fringe benefits $0 $0 $0 $0 Adjunct faculty salaries $0 $0 $0 $0 fringe benefits $0 $0 $0 $0 Graduate assistants salaries $0 $0 $0 $0 fringe benefits $0 $0 $0 $0 Classified Positions salaries $7,500 $0 $7,500 $15,000 fringe benefits $3,158 $0 $3,158 $6,317 Personnel cost salaries $115,500 $0 $304,500 $420,000 fringe benefits $36,303 $0 $94,308 $130,611 Total personnel cost $151,803 $0 $398,808 $550,611 Equipment $0 $0 $0 $0 Library $3,000 $0 $0 $3,000 Telecommunication costs $0 $0 $0 $0 Other costs (specify) $0 $0 $0 $0 TOTAL $154,803 $0 $398,808 $553,611 E-21

24 Part D: Certification Statement(s) The institution will require additional state funding to initiate and sustain this program. X Yes No Signature of Chief Academic Officer Signature of Chief Academic Officer If no, please complete items 1, 2, and 3 below. 1. Estimated $$ and funding source to initiate and operate the program. Funding Source Reallocation within the department (Note below the impact this will have within the department.) Reallocation within the school or college (Note below the impact this will have within the school or college.) Reallocation within the institution (Note below the impact this will have within the institution.) Other funding sources (Please specify, to include extramural funding and philanthropy, and note if these are currently available or anticipated.) Program initiation year Target enrollment year $0 $0 $151,803 $550,611 $3000 $3000 $0 $0 2. Statement of Impact/Other Funding Sources. Reallocation within the department Because the proposed MS in Data Analytics will be administered from the Dean s Office, the costs of supporting the program will be distributed throughout the school. Reallocation within the school or college Because the proposed program will primarily utilize existing faculty and classified staff from various departments, the costs incurred can be accommodated through reallocation of existing resources. E-22

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