New Degree Program Proposal Master of Science in Data ..., Exams of Technology

Download New Degree Program Proposal Master of Science in Data ... and more Exams Technology in PDF only on Docsity! New Degree Program Proposal Master of Science in Data Science and Strategic Analytics (This new program will replace the current Master of Science in Computational Science) Proposal Faculty Preparation J. Russell Manson, Ph.D., P.E., Professor of Physics [Director of Master’s Program in Computational Science] Janet Wagner, Ph.D., Dean, School of Business [Adjunct Professor in Operations Research, School of Business] Planning Committee: Vince Cicirello, Ph.D. Professor of Computer Science and Information Systems Adeline Koh, Ph.D. Associate Professor, School of Arts and Humanities [Director of Digital Humanities at Stockton] Jessica Fleck, Ph.D. Associate Professor of Psychology Kim Lebak, Ed.D. Associate Professor of Education Wei Xuan Li, Ph.D. Associate Professor of Business Jason Shulman, Ph.D. Assistant Professor of Physics Duo Wei, Ph.D. Assistant Professor of Computer Science and Information Systems Adjunct Faculty: John Mick, M.S. Senior software engineer, GDIT Clifton Baldwin, Ph.D. Senior systems engineer, Federal Aviation Administration Additional Support: Lewis Leitner, Ph.D., Dean, Graduate and Continuing Studies 2 Preamble and context Since 2007, Stockton has offered an undergraduate degree in computational science and an accelerated dual degree BS/MS in computational science. Since January 2010, Stockton has offered an MS in computational science (MSCP) as a standalone degree. The accelerated dual degree required 30 credits at the graduate level to confer the MS portion. The standalone MS degree required 36 credits. In Fall 2012, the college decided that, due to low enrollments, the undergraduate degree was no longer viable and after a year-long process the program was closed leaving only the standalone MS degree. A student entering the MSCP program acquires substantial experience in sophisticated computational software and programming tools that allows the student to explore mathematical, computational and data driven problems in science, business, social science, medicine and/or the humanities. Students also develop skills in data analysis, presentation, and visualization, skills that will permit them to visualize results and make predictions. The coursework is supplemented with real world projects and/or internships with industry providing experience and networking opportunities in industry or research. This degree also requires as a prerequisite, advanced skills in mathematics involving, for example, CALC III i.e., the intimate understanding of the solutions of partial differential equations. This prerequisite may have dampened enrollments given its highly advanced nature. In fact, MSCP enrollments have continued to be low throughout its existence. During the lifetime of the computational science master’s program, a new discipline involving data science and analytics grew quite rapidly and labor market demand shifted to that area. This proposal involves replacing the MSCP with a new program in data science and analytics. The working group for this initiative is proposing this new degree in response to the explosive growth of the “big data” industry sector. Stockton is extremely well-positioned to deliver this new degree since the data science curriculum will contain a mix of computational science as well as courses focused on the skills required of a data scientist and analyst. The faculty committee therefore believes that with a relatively painless shift in resources and curriculum restructuring we can deliver this new degree. This new degree program will heavily focus on statistics and data along with a more minor focus on analytical mathematics. 2 5 Introduction "Information is the oil of the 21st century, and analytics is the combustion engine." - Peter Sondergaard, Senior Vice President, Gartner Research. In 2013, IBM estimated that two and a half million terabytes of data are created every day. For the layman, this is the equivalent of over 300 million high definition movies. The National Security Agency (NSA) gathers as much information as is stored in the Library of Congress every 6 hours1. Ninety percent of the world’s data was generated in just the past two years. Data is created by, among others: 1. Individuals (through social networks and smartphones) 2. Machines (through real-time, network connected sensors – “the internet of things”) 3. Business and commerce (e.g. transaction records and financial data) 4. Higher Education (e.g. registrations, completion and retention data, faculty workload) 5. Science (e.g. bioinformatics, large scale simulation, personal health records) Making sense of this vast sea of data for the use and benefit of society is considered an imperative for the coming years, indeed many companies and higher education institutions are already strategizing and restructuring for this “big data” tsunami. Data science has emerged as an interdisciplinary paradigm for developing solutions for gathering, cleaning, archiving, analyzing and visualizing data for the purposes of making informed decisions. The McKinsey Report on big data starts, “Data have become a torrent flowing into every area of the global economy”2. Two of their main summary findings are the following: ● Data have swept into every industry and business function and are now an important factor of production. ● There will be a shortage of talent necessary for organizations to take advantage of big data. Some examples of data science projects include: 1http://www.popsci.com/technology/article/2011-05/every-six-hours-nsa-gathers-much-data-stored-entire- library-congress 2Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. Technical report, McKinsey. 5 6 Business: Using historical discounting data from a department chain store at two hundred locations to predict how sales vary with department. Entertainment: Conducting a sentiment analysis on the tweets about summer blockbuster movies and using the data to predict future box office receipts. History: Developing a geospatial database of conflicts occurring during the Scottish Wars of Independence (1296-1328). Health: Predicting disease likelihood by exploring and correlating patient case history and genetic databases. Criminal Justice: Gathering and visualizing real time crime statistics for a city for the sake of efficient resource deployment. Education: Creating a web-based dashboard for describing student performance metrics across a school district. Science: Analyzing the jpg images of one million galaxies to categorize them according to their morphology. Sustainability: Creating a “smart building”: a building fitted with sensors collecting data on all aspects of the building’s efficiency thus allowing real time adjustments to save energy. We propose this new degree in response to this exploding “big data” industry sector. Stockton University is extremely well-positioned to deliver such a degree because of existing resources - including faculty (full-time and adjunct), computer equipment, curriculum and courses – within existing schools and programs. Specifically, we wish to create a Master of Science in Data Science and Strategic Analytics (MS- DSSA) by pooling and re-structuring present Stockton resources (faculty, equipment, courses) to better reflect the emerging data science industry. Students in the existing computational science master’s degree program are already being introduced to data science courses. Program faculty felt we had to offer such courses to properly serve our students. Students in the existing degree would be given the choice as to whether they want to complete the MSCP degree or switch to pursuing a MS-DSSA degree. 6 7 1. Program Objectives The primary academic objectives for this proposed new degree program will be the development of high level skills in: ● identifying and defining problems and decisions that can be answered by data ● acquiring, analyzing and exploring data o Acquiring: getting, cleaning, archiving, integrating data o Analyzing: visually, mathematically, statistically o Exploring: seeking trends and patterns ● managing and communicating data narratives (stories) that transform data into actionable information ● exposure to real-world problems through industry partnerships/practicums involving big data. Upon successful completion of this program, graduates will be well positioned to find employment in the burgeoning data science and analytics industry: indeed, because of industry sponsored practicums, it is possible that many will already be in that industry. This will be an interdisciplinary degree drawing on faculty from various schools (NAMS, ARHU, BUSN, SOBL) and programs (e.g., science, business, computer science, mathematics, digital humanities, psychology) in a similar way to our Professional Science Masters in Environmental Science or the Master of Arts in American Studies. Though supported by professional standards, the program is not tied to any particular professional certification or accreditation. Data science is an emerging field and as such no specific national accrediting body is currently in place. The Middle States Association of Colleges and Schools is the regional accreditation body for Stockton University and thus all of Stockton’s programs. That said, the degree is designed to be cognizant of voluntary external certifications such as the “Certified Analytical Professional” offered through Informs or the “Certified Web Analyst” offered through the Digital Analytics Association. The data science curriculum will provide much (if not all) of the background for these exams. The Data Analytics Association provides the following information to prospective candidates for certification, “Due to the required years of experience in order to be eligible to test for certification, we expect that many web analytics professionals eligible for certification should be able to pass the certification test without taking any courses.” It is also important to note that Stockton is a founding member of the New Jersey Big Data Alliance3 and will seek opportunities and synergies from that relationship. This alliance brings together universities and colleges from across the state, and has the overarching goals of identifying common challenges and areas of synergy, developing joint programs, and ultimately nucleating an effective alliance that will increase our research competitiveness and drive economic development in New Jersey. 3 http://rdi2.rutgers.edu/new-jersey-big-data-alliance 7 10 At the end of this program, students will be able to …… ● define problems to be addressed through data analysis ● gather data from a variety of public and private sources including web-mining and database interrogation. ● harmonize, rescale, clean, parse and convert data files to and from any data format. ● analyze data for patterns and trends. ● find and test predictive models that describe data. ● create meaningful descriptive data visualizations. ● creatively, innovatively, and entrepreneurially design data driven solutions. ● communicate data stories verbally and in writing. ● build teams and collaborative networks. Although students will undoubtedly learn other technologies (e.g. Excel, HTML, CSS, JavaScript, D3) within this master's degree they will become expert in the following technologies since these are pervasive in the data science industry: ● an operating system of choice for data science (initially this would be Linux) ● a high level scripting language for quantitative data manipulation and visualization (initially Python) ● a high level programming tool for statistics and machine learning (initially R or SAS) 3. Relationship to Institutional Strategic Plan and Impact on its own Offerings The creation of the master's degree in data science and strategic analytics is consistent with the Graduate Education Mission Statement: “Stockton University provides quality graduate programs which promote advanced inquiry and application of new knowledge, foster advanced level career opportunities, and transmit our intellectual and cultural heritage in all its diversity…Through accessible graduate education, the College responds to the State and regional needs.” In keeping with Stockton’s mission-specific commitment to “insisting on breadth, as well as depth, in our curriculum,” this interdisciplinary program will advance knowledge and skills both within the field of data science and analytics and across different domain knowledge fields of application through industry partnered internships and project work. The interdisciplinary curriculum resonates with Stockton’s historical interdisciplinary design. Specifically, one of the objectives of Stockton’s 2020 Strategic Plan calls upon the University to “deliver high-value learning experiences”. The proposed Master of Science in Data Science and Strategic Analytics is consistent with the four themes of Stockton’s strategic plan. The four themes include Learning (high quality impactful courses), Engagement (industry practicums, corporate partnering), Global Perspectives (global data science such as climate change, water issues, etc.) and Sustainability (data induced efficacies in a variety of environmental settings). 10 11 Finally, as indicated in the Program Objectives section, the proposed MS-DSSA utilizes the broad objectives/outcomes of Stockton University’s Essential Learning Outcomes and is aligned with the Graduate Education Mission Statement which includes a focus on graduate programs which promote advanced inquiry, application of new knowledge, and which fosters advanced-level career opportunities. This degree will be attractive and valuable for graduating seniors in Computer Science, the Sciences, Business and the digital Humanities. It is envisaged that undergraduate students in these degree programs will be advised as per Stockton policy to take two of the courses in this masters in their senior year and thus garner a head start on their graduate degree. This graduate degree offers potential synergies with Stockton’s current undergraduate Computer Science and Information Systems and Business Studies programs. For example, the Association for Computing Machinery’s “IS 2010: Curriculum Guidelines for Undergraduate Degree Programs in Information Systems” lists business intelligence and more specifically business analytics as a foundational information systems (IS) topic. Likewise, several foundational elements of data science are core components of computer science (CS), including data structures, as well as the basics of machine learning within an artificial intelligence course. 4. Need The McKinsey Report2 suggests that by 2018 in the USA the data science industry “...faces a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts to analyze big data and make decisions based on their findings.” A search of any of the large job websites (such as simplyhired.com) reveals hundreds of current openings in data science. In terms of compensation the best study has been done by Burtch Works Executive Recruiting and has been documented in their report, “Data Science Salary Study” in 2014. It suggested that, Data scientists who are managers make considerably more than those who are individual contributors, but for both, compensation increases significantly with scope of responsibility and years of experience. The median base salary increases from $80,000 for level 1 individual contributors to $150,000 for those at level 3, while it increases from $140,000 for a level 1 manager to $232,500 for a level 3 manager. While 63% of level 1 individual contributors are eligible for a bonus and earned a median bonus of $11,100, 69% of those at level 3 are eligible and earned a median bonus of $40,000. Among managers at level 1, 63% are eligible for a bonus and earned a median bonus of $23,600, while 100% of those at level 3 are eligible and earned a median bonus of $82,500. 4 4 http://www.burtchworks.com/big-data-analyst-salary/big-data-career-tips/ 11 12 In terms of the current economy in Atlantic County, and particularly Atlantic City, there is need for data scientists at the Federal Aviation Administration and some opportunity for employment in the healthcare, education, finance and casino related industries, however the faculty preparing this paper wish to emphasize that local employment in this field is limited and this degree should not be marketed nor should student recruitment be driven by local industries. This will be a fully online degree to attract students throughout the state and region. 5. Students Enrollment projections The DSSA program would like to attract a sustainable enrollment with an upper limit of 25 per cohort each fall. Students with prerequisite knowledge and experience with descriptive statistics, college algebra, data processing/analysis, computer and mathematical skills will make up the vast majority of applications and enrollments. However, students with prerequisite knowledge in biology may seek enrollment in order to pursue careers in bioinformatics. A market demand survey was launched in early September 2015 to 12,000 potential respondents. The individuals surveyed were selected Stockton alumni and undergraduate students of Stockton University. 434 valid responses were collected. The information from the survey is in the table below: Master of Science in Data Science and Strategic Analysis Market Demand Survey Results 12 15 The data from this survey clearly shows prospective student interest in the program. Hybrid and online courses are favored. Accelerated coursework formats are a possibility and could be introduced after additional surveying is completed with matriculated students. Furthermore, we expect to see applications from individuals currently working within a wide variety of economic sectors and industries in the region (and possibly beyond) due to the interest in data science and data-driven decision making across many economic sectors. The responses received from particular “majors” is aligned with the type of students we are seeking to matriculate into the data science program. Of particular note is that the largest influencing factor in choosing a graduate program is cost (at 81%). We believe that we are beginning to address this by offering the degree as 30 credits over one calendar year. 6. Program Resources A. Faculty Since the proposed program is interdisciplinary in scope, it is important to include faculty who represent the different employer types included in the program which include, but are not limited to business, healthcare, education, government, science, engineering and humanities. “Lead” faculty in the Master of Science in Data Science and Strategic Analytics program would be expected to hold a terminal degree in data science or a related field (e.g. Ph.D.). Due to the crucial industrial and applied aspects of the proposed program, it is necessary to involve professional and/or adjunct (affiliated) faculty who are current leaders in the various organizational types included in the program. Per college policy, these current leaders will possess a master’s degree at a minimum along with relevant, applied experience. Current data scientists and practitioners will also be included as guest lecturers or speakers. Stockton has specific teaching, research, and service requirements for faculty based on rank. Faculty members are expected to meet or exceed these requirements. This new program will not require any new full-time faculty lines, at least in its initial stages. Faculty at Stockton have already expressed an interest in teaching in this degree. These faculty will need a release from current program and general studies teaching or would need to teach as overload (as is currently the practice in support of many of our graduate programs). Fortunately, we also have two excellent adjuncts with appropriate credentials and experience who have committed to teach in the new program (see front page). The faculty supporting the DSSA degree will be able to help the computational science student complete their master’s degree. Data Science courses can be used as substitutes for Computational Science courses. Although “big data” has exploded most rapidly in the business, marketing and healthcare sectors, data science is a pervasive discipline, and therefore it is crucial that the proposed program is interdisciplinary in scope. It is important that its delivery and administration is shared among schools and includes faculty with relevant, applied experience who represent the different “data using” organization types that the degree serves. Current leaders will also be included as guest lecturers or speakers. 15 15 B. Equipment, Materials, Library Based on the nature of the program, we anticipate no additional physical resources beyond the current Stockton Campus and Off-Campus facilities. We plan to utilize the resources and expertise offered by the Office of E-Learning and the Office of Computer Services. These offices will continue to support the online components of this program as they have already being doing for the MSCP. Blackboard will be utilized extensively. The data science industry makes extensive use of open license and open source materials; the largest user base is in Linux, Python and R which are all free to use. Our courses will all be based on open license software. In terms of library support, the college library has significant holdings in computer and computational studies. Data science is an emerging discipline and so it draws from a variety of existing resources. These include both print and internet sources. In addition, faculty will subscribe to online journals and reviews, as well as blogs. We do not anticipate significant additional resources to be needed either in equipment or software to support this program. C. Accreditation Regional accreditation will continue as per the Middle States Association Commission on Higher Education. No specialized accreditation is currently required or will be sought for this program, however this may change as the industry/discipline matures. 7. Degree Requirements The Master of Science in Data Science and Strategic Analytics (MS-DSSA) will be a 30-credit online master’s degree. A graduate certificate in data science that includes the first five courses in the curriculum will also be offered to those who wish to have some data science at their command. The graduate certificate would require a student to be matriculated. As with academic minors, certificates allow students to acquire some of the knowledge and skills of the discipline without committing to a complete degree. It is anticipated that on completion of a certificate some of the students may go on to complete the degree. Curriculum/Delivery It is proposed that this self-standing master's degree program will consists of 30 credit hours (10 required graduate courses) that may be completed in full-time or part-time study. Our current plan is to offer this degree online, in a series of “intensive” 7-week courses (using the subterm A and subterm B structure at Stockton). It is recommended that the courses be offered in three consecutive semesters (Fall, Spring, Summer) resulting in a degree awarded within one calendar year. The complete curriculum and the ideal sequence shown in table 1. Note that even part-time students can benefit from the three semester sequence as courses are always running. Graduate students tend not to want the same summer breaks as undergraduates. Master’s degree students are 15 16 bound by the academic progress requirements of the College’s graduate school, covered in the Graduate Bulletin. The program will work closely with the office of e-Learning and the faculty for this program will work with a common online structure. All 10 courses will have a similar online structure and/or “look and feel” to make the student experience an “integrated” experience, instead of ten courses that are merely “co-located”. Table 1: Curriculum and ideal sequence of courses (accelerated 7-week course structure model) Course Description Sequence REQUIRED CORE COURSES [These five courses also constitute the certificate] DSSA 5001 Introduction to data science and analytics A survey course introducing the student to the field of big data. Various application areas will be studied along. Basic introduction to data gathering, analysis and visualization. Introduction to tools and technologies. Overview of cybersecurity. Fall: subterm A DSSA 5101 Data structures and exploration Manipulating and cleaning datasets; storing data in appropriate data structures. Techniques for exploring large datasets introduced. Extensive use of industry standard software tools and operating systems/scripting languages. Fall: subterm A DSSA 5102 Data gathering and warehousing Gathering data from public and private databases. Database and web mining. Structured and unstructured database usage. Extensive use of industry standard software tools and operating systems/scripting languages. Data security. Fall: subterm B DSSA 5103 Data Visualization Interactive visualization of large datasets. Visualization delivery by web interface. Emphasis on integrity, parsimony and aesthetics of data representation. Extensive use of industry standard software tools and operating systems/scripting languages. Fall: subterm B 16