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STATE COUNCIL OF HIGHER EDUCATION FOR VIRGINIA
Program Proposal Cover Sheet
1.
Institution
George Mason University
2. Program action (Check one):
New program proposal ___X__
Spin-off proposal
_____
Certificate proposal
_____
3. Title of proposed program
Data Analytics
5.
Degree designation
Master of Science (M.S.)
4. CIP code
11.0101
6. Term and year of initiation
Fall 2014
7a. 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 2015
9. 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 e-mail 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, 703-993-1505,
[email protected]
Robin Parker, Director, Compliance, Provost’s Office, 703-993-6220, [email protected]
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 .......................................................................................................................................... 11
EXISTING PROGRAMS .............................................................................................................................................. 11
COLLABORATION OR STANDALONE ......................................................................................................................... 12
JUSTIFICATION FOR THE PROPOSED PROGRAM ....................................................................................... 12
RESPONSE TO CURRENT NEEDS ............................................................................................................................... 12
EMPLOYMENT DEMAND .......................................................................................................................................... 14
STUDENT DEMAND .................................................................................................................................................. 15
DUPLICATION .......................................................................................................................................................... 17
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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 2014.
Program Curriculum
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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 1
1
http://admissions.gmu.edu/grad/international_students/english_proficiency/
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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:
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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
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o IT 445 (Advanced Networking Principles) or TCOM 515 (Internet Protocol
Routing).
Prescriptive Analytics
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):
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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 659 - 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 550 - Database Systems
 CS 580 - Introduction to Artificial Intelligence
 CS 650 - Advanced Database Management
 CS 688 - Pattern Recognition
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CS 775 - Advanced Pattern Recognition
CS 780 - Data Mining on Multimedia
CS 782 - Machine Learning
CS 787 - Decision Guidance Systems
INFS 623 - Web Search Engines and Recommender Systems
INFS 740 - Database Programming for the World Wide Web
SYST 664 - 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 2013-14 George Mason Catalog. Courses marked ** are
new, and will appear in the 2014-15 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
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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 663 - 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
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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
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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
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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.
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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.
Program Assessment
Existing assessment measures include:
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•
•
•
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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 2014. Finally, the Board of Visitors will conduct its initial
review of the program in 2018.
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.
Spin-Off
This is not a spin-off program.
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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 2013. 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.
2
http://cra.org/ccc/whitepapers.php
http://cra.org/ccc/docs/init/bigdatawhitepaper.pdf
4
http://www.analytics-magazine.org/
5
http://hbr.org/archive-toc/BR1210
3
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In “From Data to Knowledge to Action: A Global Enabler for the 21st 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 21st 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
6
http://cra.org/ccc/docs/init/From_Data_to_Knowledge_to_Action.pdf
www.bls.gov
8
IDC, 2010 State of the U.S. Business Analytics Market
7
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•
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, 2010–2020 : 15%
o Employment Change, 2010–2020: 9,400
• Statisticians
o Job Outlook, 2010–2020 : 14%
o Employment Change, 2010–2020: 3,500
• Database Administrators
o Job Outlook, 2010–2020 : 31%
o Employment Change, 2010–2020: 33,900
9
IDC, Predictive Analytics and ROI: Lessons from IDC’s Financial Impact Study
Forrester Research, The State of Business Intelligence Software and Emerging Trends – 2010
11
http://www.bls.gov/ooh/ [accessed 04/04/13]
10
E-14
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, 2010–2020 : 34.1%
o Annual Avg. % Change: 3.0%
o Employment Change, 2010–2020: 67,976
• Database Administrators
o Total % Change, 2010–2020 : 39.3%
o Annual Avg. % Change: 3.4%
o Employment Change, 2010–2020: 2,310
• Industrial Engineers
o Total % Change, 2010–2020 : 13.8%
o Annual Avg. % Change: 1.3%
o Employment Change, 2010–2020: 589
• Mathematical Scientists
o Total % Change, 2010–2020 : 26.3%
o Annual Avg. % Change: 2.4%
o Employment Change, 2010–2020: 1,636
• Operations Research Analysts
o Total % Change, 2010–2020 : 26.1%
o Annual Avg. % Change: 2.3%
o Employment Change, 2010–2020: 1,152
• Statisticians
o Total % Change, 2010–2020 : 19.8%
o Annual Avg. % Change: 1.8%
o Employment Change, 2010–2020: 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.
12
https://www.vawc.virginia.gov/analyzer/default.asp [accessed 04/04/13]
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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)
2014 – 2015
2015 – 2016
2016 – 2017
2017- 2018
2018 – 2019
HDCT FTES HDCT FTES HDCT FTES HDCT FTES
20
11
38
20
52
28
49
26
GRAD
--
HDCT
79
FTES
41
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
GRAD
18
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 University13 (CIP code 52.0101)
• The M.S. program in Information Systems and Virginia Commonwealth University14
(CIP code 11.0401)
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
13
http://www.pubapps.vcu.edu/bulletins/prog_search/?did=20172
http://www.pubapps.vcu.edu/bulletins/prog_search/?did=20176
15
http://www.schev.edu/students/DegreeInventory.asp [accessed 04/04/13]
14
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(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
2008
2009
2010
2011
2012
VCU: CIP 52.0101*
58
67
70
118
108
VCU: CIP 11.0401
54
54
47
45
54
17
Degrees Awarded
2007-08 2008-09 2009-10 2010-11 2011-12
VCU: CIP 52.0101*
15
17
18
30
75
VCU: CIP 11.0401
31
36
38
30
42
* We were not able to obtain separate data for the concentration in Decision Sciences and
Business Analytics
16
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. http://research.schev.edu/enrollment/E16_Report.asp. (Accessed 04/04/13).
17
State Council of Higher Education for Virginia (SCHEV). Completion, Program Detail C1.2.
http://research.schev.edu/Completions/C1Level2_Report.asp. (Accessed 04/04/13).
E-18
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 20182019.
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 2018-2019.
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 2018-2019.
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
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?
Has or will the institution submit an addendum budget request
to cover operating costs?
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)?
Will each type of space for the proposed program be within
projected guidelines?
Will a capital outlay request in support of this program be
forthcoming?
Yes
No
Yes
No
Yes
No
Yes
No
Yes
No
Part B: Fill in the number of FTE positions needed for the program
Full-time faculty FTE*
Part-time faculty FTE**
Adjunct faculty
Graduate assistants
Classified positions
TOTAL
Program Initiation Year
2014-2015
On-going and
Added
reallocated
(New)
1.20
0.00
0.00
0.00
0.00
0.25
1.45
0.00
0.00
0.00
0.00
0.00
Expected by
Target Enrollment Year
2018-2019
Added
Total FTE
(New)***
positions
0.00
0.00
0.00
0.00
0.25
3.55
0.00
0.00
0.00
0.00
5.00
* Faculty dedicated to the program. **Faculty effort can be in the department or split with another unit.
***Added after initiation year.
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Part C: Estimated resources to initiate and operate the program
Expected by
Target Enrollment Year
2018-2019
Program Initiation Year
2014-2015
Full-time faculty
1.20
0.00
3.30
4.50
$108,000
$0
$297,000
$405,000
$33,145
$0
$91,149
$124,295
0.00
0.00
0.00
0.00
salaries
$0
$0
$0
$0
fringe benefits
$0
$0
$0
$0
0.00
0.00
0.00
0.00
salaries
$0
$0
$0
$0
fringe benefits
$0
$0
$0
$0
0.00
0.00
0.00
0.00
salaries
$0
$0
$0
$0
fringe benefits
$0
$0
$0
$0
0.25
0.00
0.25
0.50
salaries
$7,500
$0
$7,500
$15,000
fringe benefits
$3,158
$0
$3,158
$6,317
$115,500
$0
$304,500
$420,000
$36,303
$0
$94,308
$130,611
$151,803
$0
$398,808
$550,611
$0
$0
$0
$0
$3,000
$0
$0
$3,000
Telecommunication costs
$0
$0
$0
$0
Other costs (specify)
$0
$0
$0
$0
$154,803
$0
$398,808
$553,611
salaries
fringe benefits
Part-time faculty (faculty
FTE split with unit(s))
Adjunct faculty
Graduate assistants
Classified Positions
Personnel cost
salaries
fringe benefits
Total personnel cost
Equipment
Library
TOTAL
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Part D: Certification Statement(s)
The institution will require additional state funding to initiate and sustain this program.
Yes
Signature of Chief Academic Officer
X
No
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
Program initiation year
2014-2015
Target enrollment year
2018-2019
$0
$0
$151,803
$550,611
$3000
$3000
$0
$0
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.)
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.
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Reallocation within the institution
The University Libraries routinely commits $3000 to new master’s degree programs for the
purchase of related materials.
Other funding sources
No other funding is required to support the proposed MS in Data Analytics. George Mason
University will not request any additional funds to launch or maintain the program.
3. Secondary Certification.
If resources are reallocated from another unit to support this proposal, the institution will not
subsequently request additional state funding to restore those resources for their original purpose.
Agree
Signature of Chief Academic Officer
Disagree
Signature of Chief Academic Officer
E-23