Data-Science-MS-degree-proposal

Institution: UMass Dartmouth Proposed Degree: M.S in Data Science New Academic Programs - Submission Template Review Guidelines Prior to Submitting Materials http://www.mass.edu/forinstitutions/academic/documents/expeditednprogramapprovalguidelines.pdf Information requested may be typed directly onto form rows Boxes will expand Submit one hard copy and one copy on CD Submit complete application between August 15 – April 15 Proposed Degree Title: Master of Science in Data Science Proposed CIP Code: Date of Trustee Board Vote: Date Letter of Intent (attach copy) submitted to Chancellor: (must be 30 days prior to application submission) Chief Academic Officer (CAO) Name and Title: Mohammad Karim, Provost and Executive Vice Chancellor for Academic and Student Affairs CAO Phone Number: 508-999-8024 CAO Email: mkarim@umassd.edu Has the Chief Academic Officer reviewed this petition? Yes A Alignment with Institution Mission Priorities How does the proposed program align with the institution’s mission priorities? This proposal is to establish a new interdisciplinary Master of Science degree program in Data Science (DSC) The major will provide graduate students with advanced education and training in the rapidly emerging fields of data analytics and discovery informatics, which integrates mathematics and computer science for the quantification and manipulation of information from a cognate area of application (e.g., science, engineering, business, sociology, healthcare, planning) Emphasis is placed on merging strong foundations in information theory, mathematics and computer science with current methodologies and tools to enable data-driven discovery, problem solving, and decision making The proposed major will embody the mission of UMass Dartmouth through education, research, economic development, and public service It will prepare students of diverse backgrounds for success in technologically-oriented careers in the knowledge-based economy It will increase innovation and scientific advancement through research It will provide a pathway for women and men from diverse fields to rapidly transition to information science career paths It will prepare a citizenry capable of bringing insight to complex information in an ethical and socially responsible way It will support the needs of the business community in Massachusetts and the nation Institution: UMass Dartmouth Proposed Degree: M.S in Data Science It will sustain the university's reputation for innovative academic programs and overall excellence in graduate and professional education Interdisciplinary graduate programs are an important component of the UMD long-range plan This program creates such a program directly aligned with industry needs in the rapidly evolving age of 'big data' B Alignment with System Priorities Will this proposed program address a regional/local/state workforce shortage? Explain Research and industrial scientists are generating enormous amounts of data that need management, interpretation, and visualization Data centers with terabytes of scientific, consumer, health, and social/sensor network data are being established nationally and internationally Researchers are inventing new ways to discover knowledge, teachers are developing new ways to help students learn, and communications experts are exploring new ways to explain complex information to the public The era of 'big data' has arrived and all of the science and engineering disciplines and business/information organizations are exploiting these new opportunities Recent articles from the scientific and business community attest to the significance of this transformation, e.g., S Lohr, The Age of Big Data, New York Times, February 11, 2012 and The Economist, Data, Data Everywhere, February 27, 2010 The U.S government has launched a major initiative to advance 'big data' to address national priorities, i.e., Whitehouse Press Release, March 29, 2012 and Massachusetts just announced a Big Data Initiative that joins industry, academia, and government to accelerate growth and innovation in this new domain of information technologies Data science is recognized by economists and financial analysts as one of the leading opportunities for developing the innovation economy in coming decades See, for example, Big Data, Big Impact: New Possibilities for International Development, World Economic Forum (2012) and Analytics: The New Path to Value, MIT Sloan Management Review, (2010) A 2011 report of the McKinsey Global Institute projects a talent gap of 140,000 - 190,000 in this emerging field (see figure below) The Department of Labor also projects significant growth (>20%) in this field over the next ten years McKinsey Global Institute, Big Data: The Next Frontier for Innovation, Competition and Productivity (May 2011) Information technology is also identified as a major component of economic development in Massachusetts by the Executive Office of Labor and Workforce Development, the MA Technology Collaborative and the Economic Development Planning Council A recent study by the Mass Technology Leadership Council, Big Data and Analytics: A Major Market Opportunity for Massachusetts (2012) reports the following job growth potential in the big data sector Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Another study by the University of Massachusetts Donahue Institute, The IT Industry: Hub of the Massachusetts Technology Economy (November 2009), states that "every 100 IT jobs support an additional 163 jobs in the broader Massachusetts economy." The worlds of science and business are changing drastically and rapidly Both now depend upon the ability to analyze and learn from huge amounts of collected and streaming data There is most definitely a shortage of people who can work with these data, especially those who have a solid grounding in science Our graduates will be in the position to work with scientists and engineers as well as organizations, educators, and the public who can benefit from having the data made accessible but don’t have the time or capability to work with the original data themselves Graduates of the program will be prepared to participate on teams developing applications across a wide spectrum of scientific, engineering and business domains The proposed Data Science degree will be a ground-breaking program that specifically challenges students to be leaders in the next stage of the revolution in data analytics, information management, and knowledge engineering In the short-term, the educational opportunities offered by this new degree (and its related activities) will help graduates gain advanced knowledge; hence, improved productivity and enhanced competitiveness for local economies In the long-term, the educational varieties and its related applications well help enterprises in the South Coast (and by extension, the Commonwealth of Massachusetts) to more effectively retain their existing IT infrastructures and promote expansion As the only university in the region to offer this degree, student interest and employer demand for graduates is expected to be significant With what other institutions have articulation agreements been arranged for this program? (attach agreements) Not applicable How will the proposed new academic program broaden participation and completion at the institution by underrepresented and underserved groups? The South Coast of Massachusetts is educationally underserved Only 19% of South Coast residents have a bachelor’s degree or higher which is about half the state average More than 24% of our Institution: UMass Dartmouth Proposed Degree: M.S in Data Science residents lack even a high school diploma [1] We are the only university in the South Coast region that can provide access and opportunity to these students STEM education is at the core of what it will take to thrive in the digital economy The proposed major in a STEM discipline is central to a recently announced UMass System initiative (ABLE STEM) to double the number of underrepresented minority students graduating with degrees in these fields This new degree provides STEM students with a new pathway to a career in IT We will work actively to attract underrepresented students into the program by the following methods: (1) build awareness of the career benefits this degree offers to diverse populations, (2) reach out to minority businesses, (3) increase accessibility through online course offerings, and (4) enhance the learning environment to include best practices to improve retention of diverse student populations C Overview of Proposed Program Context Describe the program’s development, as well as its proposed administrative and operational organizational structure This program was conceived by faculty in the Department of Mathematics to provide new educational opportunities for students in the rapidly emerging field of 'Big Data' The Dean of the College of Engineering began working with faculty to design innovative, interdisciplinary undergraduate and graduate degree programs Ultimately, committees were formed to represent the different academics units that would participate in the program The committees worked together to create a flexible, interdisciplinary curriculum giving students unique qualifications to lead successful careers in the field Since Data Science is a trans-college program, an alternate administrative organization is needed to oversee curriculum design, student recruitment/progress, quality assurance, and program assessment At the outset, the program’s implementation and assessment will be guided and managed by both the Mathematics and the Computer Science Departments through a steering committee with faculty representatives from both departments and other participating academic units (departments/colleges) This committee will administer the program through such procedures as developing and maintaining specific admission and operational procedures, preparing advertising materials, evaluation applications, and making admission decisions The representatives from each department are expected to rotate over time Administrative oversight for the program will be provided jointly by the Dean of Engineering and the Dean of Arts and Sciences A Program Steering Committee (PSC) responsible for program academic admissions/advising/ standards/curriculum/assessment will be comprised of two faculty representatives from each of the participating departments – appointed by the department chairperson and approved by the Dean(s) The PSC will nominate one faculty member to serve as under/graduate program director (GPD), to be approved by the Provost/Dean(s) This structure, similar to the administrative structure of some existing programs (e.g., Women's/Gender Studies; Sustainability Studies), is very effective since the vast majority of the courses are taken in other programs The structure is cost-effective and reduces the startup costs for the program At first there might be two faculty solely associated with the Data Science program (the director and a tenure-track faculty member) The number might increase in time as the program grows An advisory committee of industry representatives in related areas is also planned Any course proposals/changes will follow the standard committee approval process Description What is the intent /purpose of the program? What knowledge and skills will students Institution: UMass Dartmouth Proposed Degree: M.S in Data Science acquire? For what careers will graduates be prepared? Establishment of the proposed MS degree program would directly address the above shortfall in human talent and be advantageous to UMass Dartmouth, the Commonwealth of Massachusetts, and to the South Coast region UMD has the resources in place to support this innovative, interdisciplinary degree, including faculty, library facilities, computer labs, networking, and general technology support This program is designed for professionals and organizational leaders who want to take on greater IT responsibilities and for people who want to transition into a career that uses computer information science to support decision making The purpose of the program is to prepare students for employment in professional fields that require data analysis and representation, and a flexible, broad understanding of informatics It is intended to appeal to students who want to learn technological and analysis tools used by leading science, engineering, business, academic, government and social organizations Further, this new program is expected to accommodate individuals with career or undergraduate degree backgrounds in business, engineering, computer science, physical/life/social sciences, mathematics, liberal arts and education who desire to enhance their data analytics and information science skills and credentials The program is designed to provide students with advanced skills and practice in applied computer science, mathematics, statistics, and a relevant substantive field of study with databases of exceedingly large size, so that students can learn statistical modeling and computer-based operations to index, store, extract, analyze, display and interpret from those computerized databases The growth in size of databases and the need to be able to “analyze and mine” them is one of the chief challenges for knowledge development and discovery in the 21st century Program Goals The new major is intended to be an innovative offering that will attract new students to UMass Dartmouth Programmatic goals are to: Meet the growing regional and national demand for high-level information systems/science skills; Provide a path for individuals from diverse fields to rapidly transition to data science career paths; Enable established information technology and computing professionals to upgrade their technical management and development skills; Prepare graduates to apply data science techniques for knowledge discovery and dissemination to assist researchers or decision makers in achieving organizational objectives; Establish stronger ties to alumni to enhance opportunities for continued learning and leadership; Create innovators, entrepreneurs, business professionals who will lead the development of next generation information systems Data Science is an interdisciplinary area that draws upon the traditionally distinct areas of computer science, applied mathematics and statistics, and applications from natural and social sciences, engineering, and business Graduates from the MS program in Data Science will acquire the skills necessary to manage and analyze massive data sets A Body of Knowledge for the subject is presented below:  Statistics o Exploratory data analysis o Stratified sampling o Regression, linear models o Goodness of fit of statistical models o Analysis of variance o Design of experiments o Digital signal processing Institution: UMass Dartmouth    Proposed Degree: M.S in Data Science Machine learning (quantitative analysis) General programming ability o Python and pandas o R o MATLAB, Maple, Mathematica o MapReduce/Hadoop o High Performance Computing (e.g via MATLAB) o Databases: MySQL Data communication o Data visualization o Web programming: HTML 5, CSS, php, javascript Graduates from the proposed MS program will be highly marketable and have many employment opportunities in both the public and private sectors As the only institution in the region to offer this innovative degree, UMass Dartmouth is well positioned to enjoy significant student demand and in so doing to prepare students for challenging and exciting careers Regional employers that would be interested in recruiting graduates of the proposed MS program in Data Science include NUWC, Raytheon, General Dynamics, Lockheed Martin Sippican, Oracle, EMC, MathWorks, Meditech, Microsoft Research New England and many others Other employers include: Google, IBM, SAS, HP, Twitter, Facebook, Intuit, Splunk, big data startup companies, e.g., Cloudera, Locu, Essess, Coursera, Guavus, BIScience, Quantivo, and Massachusetts notables Hadapt, Paradigm4, VoltDB, Bluefin Labs, Kyruus, and INEX Advisors and government centers/laboratories, e.g., LLNL, NASA, NOAA, NIST, Census, and NIH Graduates of the Data Science MS program will also be very well prepared for advanced study in information science or data analytics Students can pursue the PhD degree in Engineering and Applied Science if they desire to teach at the college or university level or carry out academic or industrial research It is also possible for students who obtain the MS in Data Science degree to teach in secondary schools (with the proper teacher certification) or in community colleges Curriculum, Requirements Provide a complete description of the curriculum Attach curriculum outline (see page 5) and course syllabi Describe procedures and arrangements for independent work, internship or clinical placement arrangements, if applicable Describe role and membership of external advisory committee, if any A successful data scientist must be proficient in quantitative analysis and data processing to aid discovery and decision support General attributes include: technical skills, communication/ teamwork skills, and an understanding of the scientific, business, or organization context of datadriven inquiry, decision making, and problem solving Thus, the Data Science program offers a broad array of learning and discovery opportunities aimed at drawing insights from extremely large amounts of data, including: data collection, preparation and integration, statistical methods and modeling, and other sophisticated techniques for analyzing and displaying complex data Data Science is an integrated curriculum with a unique blend of statistics, applied mathematics, computer science and application domains designed to meet the specific needs of both students and employers Requirements for the MS degree in Data Science include completion of 30 credit hours of coursework and passing a comprehensive examination 24 credits must be earned at UMD; 24 credits must be earned at the 500-level or above credits may be earned at UMD before formal admission The minimum GPA in all coursework applicable to the degree is 3.00 out of 4.00 Entering students may hold a Bachelor's degree from a wide array of disciplines, but will be expected Institution: UMass Dartmouth Proposed Degree: M.S in Data Science to have a prerequisite background in calculus, programming, statistics, and information science They then take a 6-credit foundation in discrete math, computing and data structures, 6-credit core in data science, 15-credits in a student-selected application area such as informatics or analytics, and credits of practical training The latter practicum provides a team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources The program emphasizes a strong foundation, domain depth, and interdisciplinary training to foster effective communication with end users Data Science courses will carry the designation DSC Curriculum details can be found on page 18 and course syllabi are appended At the outset there is no significant research component embedded in this proposed graduate program However, because many of the affiliate faculty conduct research in related areas the expected synergy between research and teaching will enhance the student educational experience Abundant internship opportunities in many business/industry sectors will be available for experiential learning The university Career Development Center and the COE COOP/Internship program can assist with placement Learning Outcomes At the time of graduation, students will:  be able apply contemporary techniques for managing, mining, and analyzing big data across multiple disciplines;  be able to use computation and computational thinking to gain new knowledge and to solve real-world problems of high complexity;  have the ability to communicate their ideas and findings persuasively in written, oral and visual form and to work in a diverse team environment;  apply advanced knowledge of computing and information systems applications to areas such as networking, database, security and privacy, and Web technologies;  be better prepared for career advancement in all areas of information science and technology;  be committed to continuous learning about emerging and innovative methods, technologies, and new ideas, and be able to bring them to bear to help others; and,  have an appreciation for the professional, societal and ethical considerations of data collection and use Learning/Outcomes Assessment Student progress and performance will be monitored on a continuing basis by the Data Science faculty Students in the MS program in Data Science must maintain a GPA of 3.00 or higher to remain in good academic standing An ongoing process of quality control and outcomes assessment will contain the following elements 1) End of Course Evaluations Each course will have an end of course evaluation, which in addition to asking for feedback on the course experience, will ask questions related to the learning goals of the program 2) Small Group Instructional Diagnosis Each year, we will perform small group instructional diagnosis (SGID) in at least one of the core courses This facilitated group feedback often gives more constructive information for the improvement of student learning than course evaluations A rate of one or two SGID’s per year will not be too burdensome for the administrators and instructors of the program 3) Exit Interview and Survey All graduates will be interviewed using a standard consistent format Students will also be asked to fill out a web-based graduate exit survey/evaluation of the program Institution: UMass Dartmouth Proposed Degree: M.S in Data Science 4) Transcript Analysis Especially for the first five years of the program, we will track the paths students took through the program in order to assess the appropriateness and effectiveness of the course sequencing we have put in place and to make adjustments if necessary 5) Long-term Career Tracking With the assistance of the Alumni Office, we will maintain a database of our graduates, keeping track of their careers to the extent possible We will periodically survey them and their employers especially about the program changes to keep the program effective and up to date Data Science is a trans-college degree program administered by a director and faculty representatives (PSC) from both the mathematics and computer science departments (initially) who will be responsible for curricular design/implementation, student recruitment/success, quality assurance, and program assessment The above data will be reviewed annually by the PSC Comparisons to performance criteria will provide a basis for recommended improvements An external advisory board consisting of 10 – 15 professionals from industry/business/government/ academe (see Appendix G) will be convened to advise on recruitment, curriculum/program development, practicum projects, internships, strategic planning, fundraising, and advances in the field The Board will meet twice per year, probably once physically and once virtually Students For first year and transfer students, outline requirements for admission and graduation, expected time from admission to graduation, projected degree completion rates, and transferability of program participants’ credits to other institutions Admissions Criteria A student who meets the University’s general eligibility requirements may apply to the MS program in Data Sciences All students apply through the Office of Graduate Studies Students must be admitted to the DSC graduate degree program by the MS - DSC steering committee Entering students must have a earned a baccalaureate degree from an accredited college or university with a minimum GPA of 3.00 (on a 4.00 scale) and be proficient in mathematics and computing Applications must also include a statement of purpose (including career goals), resume, GRE scores, TOFEL scores (for international students), and three letters of recommendation Graduation requirements include completion of all required courses and earning 30 total credits toward the degree and a minimum cumulative grade point average of 3.00 in course work taken in the program of study In addition, the student must pass an exit comprehensive exam covering core competencies in data science The requirements of the MS program in Data Science can be successfully completed in or fulltime semesters of hours each Part-time students should finish the degree within - years An 85% completion rate expected Plans to deliver many of the courses online should accelerate the graduation rate Feasibility Describe faculty, staffing, library and information technologies, facility (including lab and equipment), fiscal and or other resources required to implement the proposed program Distinguish between resources needed and on-hand Complete faculty form (page 8) Display positions to be filled with qualifications Attach vitae for current faculty This program represents an optimum use of existing resources to attract new students and to provide current students with new career opportunities The program does not duplicate any existing programs at the University and the faculty and infrastructure necessary to support the proposed program are already in place The proposed curriculum utilizes many existing courses and only a Institution: UMass Dartmouth Proposed Degree: M.S in Data Science few new courses that will be taught by UMD faculty An assessment of existing resources and the desire to deliver courses online suggests that the degree program should initially be offered as a coursework degree program with no requirements for a thesis a) Faculty The proposed program draws on existing faculty resources and classes at UMD through cooperation among multiple faculty members from diverse academic disciplines The proposed curriculum creates only four (4) genuinely new courses (DSC510, DSC520, DSC530, DSC550) to be delivered by one FTE faculty All other courses are existing courses in CAS, COE or CCB departments or modifications of existing courses The number of faculty solely associated with the program will be relatively small as identified on the Faculty Form In AY2012-13 the mathematics department added two new faculty In AY2013-14 the computer and information science department added a faculty member with expertise in informatics and crowdsourcing Searches are planned for two additional faculty with data science related expertise Current faculty members have the necessary skills to offer the proposed master's program The program is built around the strength of the faculty, both in terms of their teaching and research As the program grows, and we consider additional faculty positions, we will look for individuals that both complement and augment the current faculty If enrollment grows above the projected yearly enrollment of 50 students per year, it may be necessary to add more course sections and faculty b) Infrastructure A multipurpose Data and Visual Analytics Laboratory is being developed to support instruction and collaborative research in data science Approximately 600sf of floor space is needed to accommodate work/collaboration stations for 12 students (with expansion up to 24) Resources are available to cover space renovations (~$100,000), furnishings, and computer hard/software (~$50,000 est.) costs We also anticipate students will have opportunities to utilize facilities in the Center for Scientific Computing and Visualization Research, which will include an interactive visualization wall c) Program support Program support will consist of a stipend and/or release time for the director and for course development The library will require an expenditure of $8,000 for new materials in order to accommodate the Data Science major Relevant journals include: Journal of Data Science, Data Science Journal, Journal of Database Management, Computational Statistics and Data Analysis, Computers and Graphics, ACM Transactions on Graphics, Data Mining and Knowledge Discovery, and Information Visualization d) Operational support Operating costs such as supplies, computers, telephones, photocopying, etc will be covered by departmental budgets Additional expenses associated with seminars, student activities, recruitment, and publicity are estimated at about $50 per student and will be recovered through major fees The proposed curriculum is computationally intensive Additional IT technician/consulting support from departmental technicians or CITS will be allocated as needed to install/upgrade/maintain computer hardware and software Central support for program marketing and student services will be fulfilled by the Office of Graduate Studies Licensure and Accreditation Is this program intended to prepare students for licensure? If yes, name licensure organization and licensing exam Project student passing rates What professional or specialized accreditation will be pursued for the program? Project accreditation timelines Not applicable Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Program Effectiveness Goals, Objectives, and Assessment Linked to each goal should be measurable objectives – such as job placement rates, faculty additions, facility or programmatic enhancements, etc – timetable, and, if applicable, strategies for achieving them Attach goals table (see page 4) (Please note that this section is intended to focus on overall effectiveness, not student learning, which is addressed elsewhere.) Describe program assessment strategies that will be used to ensure continuing quality, relevance and effectiveness Include plans for program review including timetable, use of assessment outcomes, etc Benchmarks of Success The success of the entire program will be assessed against the following benchmarks:  Meeting enrollment targets (maintaining a minimum of 40 students, beginning in the third year);  Graduation rates and degrees awarded (85% two-year graduation rate);  Satisfaction of students with the program, measured by course evaluations and web-based exit surveys with graduating students (at least 85% satisfied or very satisfied);  Success of graduates in pursuing advanced degrees, obtaining high-quality employment or advancing to higher positions within their present organization; and,  Long-term professional success of the graduates, measured by 5-year alumni and employer surveys Benchmarks will be evaluated annually by the PSC/DSC faculty and used to revise policies, curriculum, and recruitment efforts accordingly Student placement and success will also be monitored and presented annually to the External Advisory Board for review and feedback After the program has been fully implemented for three years, a review of the overall program will be accomplished by reviewers external to the university The external reviewers will be drawn from members of relevant businesses and from other graduate programs The review panel will be chaired by the Associate Provost for Graduate Studies and will work closely with the External Advisory Board The review will include an assessment of whether graduates and employers of graduates have been satisfied with the program and whether the program is self supporting Quantifiable as well as subjective benefits and costs of the program will be fully explored in the report of the external review committee D External Review Attach the review team report and institutional response (obtain BHE approval of reviewers in advance; provide review standards – see appendix - to team) See Appendix E and F E Market Analysis Need for graduates What is the local/regional/state labor market outlook for graduates of the proposed program? Include data and data sources that form the basis for need assessment The demand for graduates of MS-level information/data science programs both in-state and nationally is high and predicted to grow rapidly in the years ahead This degree program is designed to produce professionals prepared to support the needs of information-intensive service industries/businesses/agencies US Department of Labor employment projections in data science related fields are shown in the following table: 10 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science the development of new applications of knowledge within their field Such programs afford the student a broad conceptual mastery of the field of professional practice through an understanding of its subject matter, literature, theory, and methods They seek to develop the capacity to interpret, organize, and communicate knowledge, and to develop those analytical and professional skills needed to practice in and advance the profession Instruction in relevant research methodology is provided, directed toward the appropriate application of its results as a regular part of professional practice Programs include the sequential development of professional skills that will result in competent practitioners Where there is a hierarchy of degrees within an area of professional study, programs differ by level as reflected in the expected sophistication, knowledge, and capacity for leadership within the profession by graduates 4.27 Programs encompassing both research activities and professional practice define their relative emphases in program objectives that are reflected in curricular, scholarly, and program requirements 4.28 Students who successfully complete a graduate program demonstrate that they have acquired the knowledge and developed the skills that are identified as the program's objectives In addition please evaluate and comment on each of the following review questions for graduate level programs In what ways is the proposed program consistent with the academic mission of the campus? How does the proposed program address an area of significant public need in Massachusetts and nationally? Has the College or University presented sufficient documentation (e.g State and federal employment outlooks, regional outlooks, etc.) to support the program’s need? How does the overall program design accomplish the program’s goals and purposes? Specifically, are the content and sequencing of the curriculum appropriate? Does the curriculum achieve appropriate balance among the component disciplines? Are there major omissions? If so, what are they? How are the degree requirements of sufficient rigor to produce graduates who are competitive in the field? Evaluate how the curricula require scholarly and professional activities to advance the student substantially beyond the educational accomplishments of a baccalaureate degree program What experience and expertise does the department possess to undertake the proposed program? Will the program have a significant proportion of faculty who hold an earned doctorate (Ph.D.) in the field or in a closely related discipline? Will there be a sufficient number of full-time faculty in the program to assure the accomplishment of classroom and out-of-classroom responsibilities essential for the fulfillment of program mission and purposes? How will graduates demonstrate that they have acquired the knowledge and developed the skills that are identified as the program’s objectives? Evaluate the process the College has established to assess the effectiveness of the program in achieving its goals and objectives Has the institution committed the necessary and appropriate resources (including faculty, plant and equipment, and library and information resources) to ensure program quality and program improvement? 24 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science APPENDICES TO BE ATTACHED Letter of Intent Review Team Report Institutional Response Faculty Vitae Syllabi Other Link to Guidelines http://www.mass.edu/forinstitutions/academic/documents/expeditednprogramapprovalguidelines.pdf 25 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science List of Appendices Appendix A Enrollment Data for Participating Departments Appendix B Relevant M.S Programs Elsewhere Appendix C Organizational Chart Appendix D Letters of Support from Industry / Business Appendix E External Review Committee Report Appendix F Institutional Response Appendix G Proposed Industry/Business/Academic Advisory Board Forwarded Under Separate Cover Course Descriptions / Syllabi Faculty Curriculum Vitae 26 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix A Enrollment Data for Primary Participating Departments Fall 2012 Enrollment Data College Dept BS CAS MTH 71 COE CIS 177 Total 248 MS – 47 47 PhD – – – Total 71 224 295 Fall 2013 Enrollment Data (unofficial) College Dept BS MS PhD CAS MTH 94 – – COE CIS 218 77 – Total 312 77 – Total 94 295 389 27 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix B Examples of other Academic Programs in Big Data Analytics/Data Science Arizona State University M.S in Business Analytics Bentley University M.S in Marketing Analytics Boston University M.S in Computer Information Systems University of California, Irvine M.S in Informatics Carnegie Mellon University M.S in Information Systems Management; M.S in Information Technology; PhD in Machine Learning Central Connecticut State University M.S in Data Mining City University of New York M.S in Data Analytics University of Cincinnati M.S in Business Analytics Columbia University M.S in Information and Knowledge Strategy Cornell University M.Eng Operations Research and Information Engineering DePaul University M.S in Predictive Analytics Drexel University M.S in Business Analytics; M.S in Information Systems Fordham University M.S in Business Analytics George Mason University PhD in Computational Sciences and Informatics; B.S in Computational and Data Sciences Illinois Institute of Technology Master of Data Science Indiana University-Purdue University Indianapolis School of Informatics PhD in Informatics Louisiana State University M.S in Analytics UMass Boston Graduate Certificate in Database Technology University of Maine M.S in Information Systems Michigan State University M.S in Business Analytics University of Michigan M.S in Information University of Michigan - Dearborn M.S in Business Analytics North Carolina State M.S in Analytics Northeastern University Master of Professional Studies in Informatics; M.S in Information Systems Northwestern University Master of Science in Analytics; Master of Science in Predictive Analytics (Online Program) New York University M.S in Data Science; M.S in Business Analytics University of Notre Dame Masters in Predictive Analytics University of Pittsburgh M.S in Information Science Purdue University MBA in Business Analytics Rensselaer Polytechnic Institute M.S in Business Analytics Rochester University M.S in Business Analytics Rutgers University MBA in Analytics and Information Management University of San Francisco M.S in Analytics Stanford University Mining Massive Data Sets Graduate Certificate Stevens Institute Master of Science in Business Intelligence and Analytics Syracuse University Graduate Certificate of Advanced Studies in Data Science University of Tennessee Masters in Business Analytics University of Texas - Austin M.S in Business Analytics University of Washington Certificate in Data Science 28 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix C Proposed Data Science M.S program oversight 29 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix D Letters of Support from Industry / Business The following companies have expressed partnership support for the proposed program in data science: Barracuda Networks, EMC, IBM, INEX, L-M Sippican, MathWorks, NUWC, Nvidia, and Raytheon 30 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix E External Review Committee Report 31 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix F Institutional Response 32 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Appendix G Proposed Industry/Business/Academic Advisory Board 33 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Forwarded Under Separate Cover Course Syllabi Faculty Curriculum Vitae 34 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Course Descriptions Required courses by discipline Mathematics MTH 522 Computational Statistics (3 credits) Prerequisite: Graduate standing and one course in mathematical statistics Introduction to mathematical concepts and methods essential for computational statistical analysis Topics include Monte Carlo methods for statistical inference; data randomization and partitioning including jackknife methods; bootstrap methods; parametric and non-parametric estimation of probability density functions; principal components analysis; fitting models to data Computer Science CIS 452 - Database Systems (3 credits) Prerequisites: CIS 280 Use of DBMS software in the development of an information system Overview of the ANSI/SPARC Study Group on Database Management Systems model Relational database model techniques Emphasis on user views necessary to support data management and retrieval CIS 552 Database Design (3 credits) Prerequisite: CIS 452 or equivalent, or permission of instructor The relational, hierarchical, and network approaches to database systems, including relational algebra and calculus, data dependencies, normal forms, data semantics, query optimization, and concurrency control on distributed database systems Proposed new courses DSC 530 Data Visualization Workshop (3 credits) Prerequisites: DSC 510 Project-based course on advanced data visualization techniques Topics include: Wordle and data visualization; color in data graphics, luminosity in recovering local density, color animation; visualizing roads and subways; visualizing social networks; visualizing parallel data sets; matrix methods in data visualization; visualization in forensic analysis; visualization software Ethical issues in data science Possible text: Beautiful Visualization: Looking at Data through the Eyes of Experts Julie Steele (Editor), Noah Iliinsky(Editor O’Reilly Media(2010).ISBN-13:978-1449379865 DSC 550 Data Science Practicum (3 credits) Prerequisite: completed 18 credit hours of graduate coursework in data science major A team-based learning experience that gives students the opportunity to synthesize prerequisite course material and to conduct real-world analytics projects using large data sets of diverse types and sources Students work in independent teams to design, implement, and evaluate an appropriate data integration, analysis, and display system Oral and written reports and ethical aspects are highlighted Elective Courses (examples): MTH 463 - Math Modeling (3 credits) Selected topics from the areas of linear programming, dynamic programming, Markov chains and game theory Mathematical model building will be developed through the use of numerous case studies from the natural and social sciences, e.g., ecological models, network models, scheduling models, urban structure, traffic flow, growth, etc MTH 464/564 Simulations (3 credits) 35 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Re-written description: Foundations of computational simulation Pseudo-random number generation; generating continuous random variables; Monte Carlo techniques; simulation of discrete events; statistical analysis of simulated data, including bootstrapping; goodness of fit tests; Markov chain Monte Carlo methods; applications to large data sets MTH 550 Matrix Computations (3 credits) Prerequisites: Proficiency in linear algebra, statistics, and programming The aim of the course is to acquire knowledge and understanding of matrix computations in different application areas, which in turn requires deeper studies in theory, methods, algorithms, and software for different matrix computational problems Examples include, matrix factorizations, iterative methods for solving linear systems and eigenvalue problems and how they are used in applications like information retrieval on the Internet, computer graphics, simulation, signal processing and various engineering applications Computer laboratory assignments are important for acquiring skills and increased understanding MTH 473/573 Numerical Linear Algebra (3 credits) Prerequisites: MTH 353, 361; or permission of instructor An introduction to numerical linear algebra Numerical linear algebra is fundamental to all areas of computational mathematics This course will cover direct numerical methods for solving linear systems and linear least squares problems, stability and conditioning, computational methods for finding eigenvalues and eigenvectors, and iterative methods for both linear systems and eigenvalue problems MTH 474/574 Numerical Optimization (3 credits) Prerequisites: MTH 353, 361; or permission of instructor An introduction to constrained and unconstrained optimization Numerical optimization is an essential tool in a wide variety of applications The course covers fundamental topics in unconstrained optimization and also methods for solving linear and nonlinear constrained optimization problems CIS 430 – Data Mining and Knowledge Discovery (3 credits) Prerequisites: CIS 360 Designed to provide students with a solid background in data mining and knowledge discovery concepts, tools, and methodology, as well as their applicability to real-world problems A variety of data mining techniques will be explored including memory-based reasoning, cluster detection, classification, neural networks, and finding understandable knowledge in large sets of real world examples Some related topics such as web and multimedia mining will be discussed Students will gain hands-on experience in data mining techniques using various data mining software packages and tools CIS 431 - Human and Computer Interaction (4 credits) Prerequisites: CIS 362 or permission of instructor Theory and principles for constructing usable software systems Cognitive and effective aspects of users The impact of user characteristics on design decisions The construction and evaluation of the user interface Sensory and perceptual aspects of interfaces, task structure, input modalities, screen layout, and user documentation Individual concerns for systems such as personal productivity tools, real-time control systems, instructional software, and games CIS 454 - Computer Graphics (3 credits) Prerequisites: At least junior CIS standing Graphics devices Graphics devices Two dimensional and three dimensional image representations and transformations Graphics systems software architecture; graphics standards; packages CIS 455 - Bioinformatics (3 credits) Prerequisites: CIS 360 and CIS 361 or permission of instructor 36 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science Introduction to the field of bioinformatics This course addresses the analysis of information present in biological systems This course presents an overview of the applications of computing technologies such as: analysis of protein sequence, pattern matching, biomodeling and simulation, and biological data visualization It also provides algorithms and methods on a selection of computational problems Hands on experience with tools and data CIS 467- Image Analysis and Processing (3 credits) Prerequisites: CIS 360 Fundamentals in image analysis and processing Topics in image processing such as display and filtering, image restoration, segmentation, compression of image information, warping, morphological processing of images, wavelets, multi-resolution imaging and unitary transforms are discussed CIS 490 Machine Learning (3 credits) Prerequisites: CIS 360 Constructing computer programs that automatically improve with experience is the main task of machine learning The key algorithms in the area are presented Learning concepts as decision trees, artifical neural networks and Bayesian approach are discussed the standard iterative dichotomizer (ID3) is presented, the issues of overfitting, attribute selection and handling missing data are discussed Neural nets are discussed in detail, examples of supervised and unsupervised learning are presented Instance-based learning, i.e knearest neighbor learning, case-based reasoning are introduced Genetic algorithms are discussed on introductory level CIS 554 - Advanced Computer Graphics (3 credits) Prerequisites: CIS 454 or equivalent, or permission of instructor Three-dimensional graphics including: color, shading, shadowing and texture, hidden surface algorithms An extensive project will be assigned, including documentation and presentation CIS 555 - Advanced Bioinformatics (3 credits) Advanced coverage of computational approaches used in bioinformatics The course focuses on algorithmic challenges in analyzing molecular sequences, structures, and functions It covers the following topics: Sequence comparison, assembly and annotation Phylogenetic analysis RNA secondary structure Protein structure comparison, prediction, and docking Microarrays, clustering, and classification Genome, Hapmap, SNPs, and phenotypes Proteomics and protein identification Determining protein function and metabolic pathways CIS 563 MultiAgent Systems (3 credits) Introduction to multiagent systems and distributed artificial intelligence The course examines the issues that arise when groups or societies of autonomous agents interact to solve interrelated problems Topics include defining multiagent systems and their characteristics, reasoning about agents’ knowledge and beliefs, distributed problem solving and planning, coordination and negotiation, the organization and control of complex, distributed multiagent systems, learning in multiagent systems, and applications in the following domains: internet information gathering, electronic commerce, virtual markets, workflow management, distributed sensing network, distributed planning and resource allocation CIS 581 - Design and Verification of Information Systems (3 credits) Prerequisites: CIS 580 or equivalent, or permission of instructor Sound design methodologies and technologies in development and maintenance of information systems/business systems with special emphasis on workflow management systems An applied course that emphasizes the formal approach, this course also addresses issues of adaptability and flexibility of information systems and their evaluation The course supports concurrent execution of information systems during the design stage and adopts and applies various forms of Petri nets 37 Institution: UMass Dartmouth Proposed Degree: M.S in Data Science CIS 585 - Image Processing and Machine Vision (3 credits) Prerequisites: Graduate standing and permission of the instructor Foundations of image processing and machine vision Students apply and evaluate topics such as edge detection, segmentation, shape representation, and object recognition Stereo vision and motion analysis will be covered in detail including calibration, range images, change detection, motion correspondence, and 2-D and 3-D tracking Important research papers will be discussed in class MIS 432 - Business Data Systems (3 credits) Prerequisites: At least junior standing; MIS 322; for business majors only Students demonstrate their mastery of the analysis and design processes acquired in earlier courses by designing and constructing databases to meet the information needs of users Topics covered include data models and modeling techniques, information engineering, database design and implementation, data quality and security, and the client/server environment MIS 433 Advanced Database E-Business Applications Development (3 credits) Prerequisites: MIS 432 and senior standing; for business majors only Focuses on advanced database techniques and issues for e-commerce applications including web-based database application development and data warehousing design The course provides extensive opportunities for applying and extending database concepts learned in BIS 432 (Business Data Systems) through hands-on use of web-based database applications development tools that are commonly used in the business field Students complete a major project MIS 670 - Managing Information (3 credits) Managing information by understanding, designing and controlling the information processing activities of an organization The course explores how firms gather, represent, process and distribute information and knowledge to employees and customers A sample of the topics covered in the course includes: gathering information, or business intelligence; storing information, or information architectures; information/data modeling; processing information, or process modeling; knowledge management; data mining; and distributing information, or e-commerce brokerage and disintermediation MKT 671 Marketing Research (3 credits) Successful marketing by collecting, analyzing and interpreting information This course offers an understanding of the different marketing information needs of the organization The conception, planning and performance of marketing research projects are discussed as an objective basis for marketing strategies Topics include definition of research objectives, data sources, research design, interpretation of data and evaluation of research proposals and results The course focuses on applying marketing research concepts to solving real-world problems through written and video cases, applied research exercises and experiential research development projects POM 500 Statistical Analysis (3 credits) A case study approach involving the following statistical concepts: descriptive statistics, probability, sampling, probability distribution, statistical estimation, chi-square testing, analysis of variance and simple regressioncorrelation analysis PSY 502 Statistical Methods in Psychology (3 credits) Prerequisite: PSY 205 or similar statistics course and graduate standing in Psychology Advanced study of statistical methods in psychology including analysis of variance and regression Previous experience with the SPSS statistical program is suggested This course is intended for those who have completed an undergraduate statistics course 38