Draft Proposal. Ph.D. in Analytics and Data Science. Last Updated: September 4, 2013

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1 Draft Proposal Ph.D. in Analytics and Data Science Last Updated: September 4, 2013

2 Institution: Kennesaw State University Institutional Contact: Daniel Papp, President Date: XXXX School/Division: School of Science and Mathematics Department: Department of Mathematics and Statistics Departmental Contact: Jennifer Lewis Priestley Name of Proposed Program: Ph.D. in Analytics and Data Science Degree: Ph.D. CIP Code: XXXX DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 2

3 Executive Summary The McKinsey Global Institute has identified that the demand for deep analytical talent will outpace the supply in the United States by almost 200,000 people within three years. In response, the White House has launched a Big Data Research and Development Initiative, to expand the workforce needed to develop and use Big Data technologies. This theme is echoed by Thomas Davenport s recent article in Harvard Business Review titled Data Scientist: The Sexiest Job in the 21 st Century. These studies and many others point to the need for universities to educate and train Data Scientists to address this demand. However, no university in the country currently has a degree program in Data Science defined as the intersection of Statistics, Mathematics and Computer Science. Kennesaw State University is proposing a Ph.D. in Analytics Data Science one of the first of its kind in the country. The degree will train individuals to translate large, unstructured, complex data into information to improve decision making. This curriculum will include programming, data mining, statistical modeling, and the mathematical foundations to support these concepts. Importantly, it will also emphasize communication skills both oral and written as well as application and tying results to business and research problems. Because this degree is a Ph.D. (rather than a Doctorate in Data Science), it creates flexibility for the student. Graduates can either pursue a position in the private or public sector as a practicing Data Scientist where the demand is expected to greatly outpace the supply or pursue a position within academia, where they would be uniquely qualified to teach these skills to the next generation. Kennesaw State University is well positioned to launch this degree. This is evidenced by the unparalleled success of the MS in Applied Statistics where graduates are in great demand and continue to have 100% placement and by the Minor in Applied Statistics with undergraduate demand from every college across the university, and similarly strong placement. The Minor attracts approximately 200 undergraduates every semester making it the most successful and sought out Minor in the history of KSU. The Ph.D. in Analytics and Data Science will not only help to close the talent gap in the area of Data Science, but will also continue KSU s trajectory of regional and national recognition in the area of applied analytics. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 3

4 There are three ways you can get to the top of a tree: 1) sit on an acorn 2) make friends with a bird 3) climb it. Anonymous SECTION 1. PROGRAM DESCRIPTION AND OBJECTIVES: a. Objectives of the program The Ph.D. in Analytics and Data Science at Kennesaw State University is an advanced degree, which will prepare individuals to work as Data Scientists in a private or public sector capacity or, secondarily, to work in academia within a department focused on the application of data analysis. This Ph.D. will utilize a multidisciplinary approach, with emphasis on Statistics, Mathematics, Computer Science and a content discipline such as Biology, Chemistry, Finance, Physics, Political Science, etc. b. Needs the program will meet I skate to where the puck is going to be, not where it has been. Wayne Gretzky The United States Federal Government recently issued a press release addressing what it sees as a growing critical shortage of data analysts and, on March 29, 2012, issued the Big Data Research and Development Initiative. One of the main purposes of the initiative is to expand the workforce needed to develop and use Big Data technologies. The term Big Data is increasingly included within descriptions of required skill sets across a wide variety of disciplines and sectors of the economy. While the accepted definition of Big Data is continuing to evolve, there is no question about the expansion and prevalence of related concepts and their expanded role in the future. According to The Economist magazine, unmanned American military aircraft (i.e., drone aircraft) flying over Iraq and Afghanistan in a single year (2009) produced approximately 24 years worth of video surveillance footage. Every year, Google acquires an equivalent amount of data to the entire Library of Congress. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 4

5 These astonishing facts highlight at least four major points about how data is collected, analyzed, and used: 1. Extraordinary, previously unimaginable amounts of data are being collected and stored for subsequent analysis, which contain potentially significant and meaningful information in the private and public sectors and to society at large. 2. It is not feasible to manually review and/or analyze such massive data in a timely manner using traditional methods. Computer-assisted semi- or fully-automated processes using new computational and data mining methods are needed in order to extract useful information from massive data sources in a timely manner. 3. In addition to massive amounts of traditional structured data (i.e., tabular data), extraordinary amounts of unstructured, non-traditional data such as video footage, audio recordings, and unstructured text are being collected and stored. Increasingly, these two very different types of data must be merged together in systematic ways in order to obtain useful information. 4. Unlike the past, data collection and analysis is no longer a purely academic endeavor. Data gathering and analysis for obtaining useful information most often used in decision making processes is used in almost every field and sector imaginable at present including the sciences, public health, the healthcare industry, all aspects of business and finance (including retail, insurance, marketing, the service industry, the credit industry, fraud detection, the communications industry, etc.), psychology, education, public policy agencies, government elections, and critically, in national security and defense. From these four points it follows that: The next generation of statisticians will face very different challenges and issues than previous generations of statisticians. As a result, the next generation of statisticians requires a new set of knowledge and skills in order to effectively serve the data analysis needs of the 21 st century. These skills will incorporate more emphasis on applied mathematics and on computer programming than has historically been the case even for applied statisticians. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 5

6 A recent article in Significance magazine made this point clear: Traditional data is numbers. (Emerging data) is not. It is digital, but it is generated by all kinds of hardware and software. It is text, it is videos, it is tweets and Facebook pages; it is about transactions and interconnections by the billions. Data is the raw material of statistics, but traditional statistical disciplines will not cope; this new data needs new ways of handling it, of analyzing it, of thinking about it and using it. A large number of new and well-trained 21 st century statisticians are needed in every sector of almost every society in the world, not just America, and not just developed countries. The U.S. business community is also aware of this need: Hal Varian, Ph.D., the Chief Economist at Google, Inc. states simply, Data are available; what is scarce is the ability to extract wisdom from them. Further to the recognition of the talent shortage evidenced through the White House Big Data Research and Development Initiative, the Big Data Report from the McKinsey Global Institute (MGI) estimates that the demand for data analysts could exceed the current supply by 140,000 to 190,000 positions by the year 2018 (see Figure 1). Figure 1 illustrates that there are 440,000 to 490,000 total data analyst job positions projected for 2018 with only 300,000 trained analyst to fill those positions. In other words, the demand for big data analysts could be 50 to 60% greater than its projected supply by DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 6

7 FIGURE 1: The Talent Gap for Big Data Analysts The Big Data MGI report also predicts differential gains as a result of the impact of big data and its use across different sectors. According to MGI, finance and government (Cluster B in Figure 2) are expected to benefit strongly from big data use in the future where computer and electronic products and information sectors (Cluster A in Figure 2) have already and will continue to experience substantial benefits from the impact and use of big data. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 7

8 FIGURE 2: Differential Potential Gains of Big Data by Sector A brief survey of the diverse disciplines which have recognized the role of Big Data and the changing role of analytics includes: Business Customer relations management (CRM) is one of the most innovative and profitable ways in which businesses use big data. CRM is essentially the business practice of analyzing customer-centric big data to discover trends and use that information to customize or personalize offers and communications with customers to optimize business. CRM was once used only by Fortune 500 companies, however, now with the proliferation of big data and reduced costs in collecting and storing it, all types of companies are using it to optimize their business. In one example of a typical CRM application, a U.S. bank used big data analytics to predict which product offer was most likely to be accepted by a particular customer and thereby customize the next on-line product offered to that customer in an effort to cross-sell to existing customers (Berry & Linoff, 2000). This CRM initiative resulted in substantial gains in cross-selling and therefore profits to the bank well above the cost of implementation. This is just one of many examples of big data analytics in business. Others include fraud identification, DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 8

9 service rate estimation, predicting product failure, and optimizing direct mailing campaigns, among others. By all accounts, the main hindrance in CRM is a lack qualified data analyst (The Economist, 2010; Significance, 2012). Healthcare & Public Health The proper use of digitized medical records has the potential of revolutionizing the healthcare industry. Proper analysis of these records may be used to detect unwanted drug interactions and/or side-effects, identify best practices in care (e.g., identify the most effective drug therapies), and even predict the onset of certain diseases before patients themselves are aware of symptoms (The Economist, 2012). In one example, medical doctors and data analysts in Alabama developed automated infection surveillance software that assists hospitals in identifying changes in nosocomial infection (i.e., hospital-acquired infection) rates using massive data from Blue Cross/Blue Shield of Alabama and statistical and data mining methods (Putman, 2003). It has been estimated that nosocomial infections add as much as nine days to a patient s hospital stay leading to more than a $4 million per year additional expense. This infection surveillance software provides early warning to hospitals and allows them to intervene in a timely manner. This is only one of many possible examples where nontraditional statistical work involving big data has made a substantial improvement in healthcare quality and substantial savings to society. Government According to the National Science Foundation (NSF, 2012) in the document entitled, Core Techniques and Technologies for Advancing Big Data Science & Engineering (NSF ), the impact of big data is causing a literal paradigm shift in scientific and biomedical investigation that is transforming the missions of a number of U.S. Federal Government agencies: Today, US government agencies recognize that the scientific, biomedical and engineering research communities are undergoing a profound transformation with the use of large-scale, diverse, and high-resolution data sets that allow for data-intensive decision-making, including clinical decision making, at a level never before imagined. New statistical and mathematical algorithms, prediction techniques, and modeling methods, as well as multidisciplinary approaches to data collection, data analysis and DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 9

10 new technologies for sharing data and information are enabling a paradigm shift in scientific and biomedical investigation. Advances in machine learning, data mining, and visualization are enabling new ways of extracting useful information in a timely fashion from massive data sets, which complement and extend existing methods of hypothesis testing and statistical inference. As a result, a number of agencies are developing big data strategies to align with their missions. These examples and countless others highlight three common emerging themes: 1. Big Data is ubiquitous. All disciplines. All sectors of the economy. 2. Data is no longer considered a necessary cost to be managed down, but rather as an asset to be mined and leveraged. 3. All sectors are increasingly finding a dearth of analytical talent to support their nascent, but explosive analytical needs, particularly as it is related to Big Data. In response to this, Kennesaw State University is proposing the development of a Ph.D. in Analytics and Data Science. It is our position that the Data Scientist will be uniquely positioned to fill the talent shortage as outlined above. It is critical to note that this is a proposal for a Ph.D. program in Analytics and Data Science rather than in Statistics. A great deal of attention is emerging in the field of analytics towards the role of the Data Scientist From IBM - A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong application acumen, coupled with the ability to communicate findings in a way that can influence how an organization approaches a business challenge. From Thomas Davenport, Senior Managing Partner at Accenture and author of Competing on Analytics (Data Scientists) are not typical scientists but rather hybrids of science and computation. Somewhere along their career journeys they became interested in, and good at, the manipulation of data. In fact, many of them really have computational in front of their scientific specialties: computational biology, computational ecology, etc. If you want some DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 10

11 evidence of this hybrid specialization, look at your favorite data scientist s profile on LinkedIn -- the home, by the way, of some of the best data scientists around -- and check out the skills they say they have. You ll see analytics (quantitative analysis, statistical modeling, predictive analytics, social network analysis, data mining, etc.) listed, of course. But you are also likely to see SQL, Java, C, Python, R, distributed databases, and so forth. All of these skills actually are found in one individual, and he seems typical of the breed to my knowledge, no universities have programs yet in big data analytics (though some are talking about them -- universities typically don t move too hastily). From Daniel Tunkelang, Chief Data Scientist at LinkedIn Strong analytical skills are a given: above all, a data scientist needs to be able to derive robust conclusions from data. But a data scientist also needs to possess creativity and strong communication skills. Creativity drives the process of hypothesis generation, i.e., picking the right problems to solve, the will to create value for users and the drive to improve business decisions. Communication is essential, because data scientists work in horizontal roles and partner with groups across the entire organization. At LinkedIn, data scientists collaborate with every other product group, as well as with sales and finance. Strong communication skills are a must-have. From Steve Hillion, VP of Analytics at GreenPlum, as quoted in Forbes - I m sure in 30 years time, there will be lots and lots of degrees in data science and that s where [data scientists will] come from, but right now it s coming from all these different buckets (math, computer science, economics) And, just as the early days of computing were born in the garages of Silicon Valley do-it-yourself-ers, data science is likely to develop first in an ad-hoc, hands-on way. It is our position that the intersection of skills outlined in multiple ways above, are brought together under the description of Data Scientist, in a way that does not occur in a traditional Statistics curriculum. This term has emerged as the moniker of an individual with strong computational and programming skills, but also possessing business/content acumen, enabling clear and meaningful communication. As can be seen below, the term data scientist is emerging as a dominant search term in Job Search engines. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 11

12 FIGURE 3: Job Trends in Data Science From Michael Rappa, Director of the Institute for Advanced Analytics at NC State The future of data science in the enterprise will be extremely bright, if a few key things happen: First, the right kinds of partnerships must be formed between data-rich companies and forwardthinking academic institutions. Second, institutions and employers need to encourage and reward the right set of data-science skills. Are statisticians going away? No. There will always be a need for traditional Statistics. Disciplines such as psychology, nursing, marketing research, medical research, etc., will always have a need for the traditional skills associated with hypothesis testing and model development. Data Scientists are different. They embody skills which traditional statisticians don t have. While data scientists must have strong skills in statistical testing and modeling, they are also strong in computational mathematics, data architecture, the process of ETL (extract, transport, load), programming (i.e., SAS, Java, C++, Hadoop), and typically have some content knowledge (i.e., Chemistry, Biology, Finance). The proposed Ph.D. in Analytics and Data Science at Kennesaw State University directly meets the national talent shortage in this space, as evidenced by movements such as the Big Data Research and Development Initiative of 2012, by effectively and thoroughly training and thereby expanding the workforce available to develop and use Big Data technologies. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 12

13 Furthermore, this degree program would transform teaching and learning in the field of Big Data technology, another major objective of the White House Initiative. Consequently, we believe the degree will directly and/or indirectly accelerate the pace of discovery in science and engineering used to further understanding and knowledge, strengthen U.S. national security, and increase the quality of life for the average American citizen. With respect to the shortage of big data analysts and their training, The Big Data MGI report states, we believe that the constraint on this type of talent will be global, with the caveat that some regions may be able to produce the supply that can fill talent gaps in other regions. It is our strongly held position that we can make Georgia one of these key regions which produces Big Data analytical leadership for the world with this proposed Ph.D. degree. A Ph.D. program in Analytics and Data Science at Kennesaw State University has the potential of defining Kennesaw State University, the University System of Georgia, and the State of Georgia, as cutting-edge, state-of-the-art innovators in the methods and technologies that will shape and see us through the 21 st century. So why do we think KSU can deliver a Ph.D. in Analytics and Data Science? The main reason is that we are already moving in that direction. This is evidenced through the successes of both the Minor in Applied Statistics and Data Analysis as well as in the Master of Science in Applied Statistics. The Minor in Applied Statistics, more than any other Minor field of study in the history of KSU, is a flagship of interdisciplinary success. Students are required to complete 15 hours (five courses) in Statistics at the 3000 level or above to qualify for a Minor in Applied Statistics and Data Analysis. In any given semester, the Minor serves the needs of over 200 students from almost every college across the university. Statistics represents the most diverse cross section of majors in 3000 or 4000 level courses, of any course of study. Where most upper division courses are populated by students from a single major, in the statistics courses (all STAT courses are above 3000), the classes are consistently populated with students from Biology and Chemistry, Finance and Economics, Psychology, Mathematics, Sociology and even Theater (see Figure 4). DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 13

14 Psychology Biology Mathematics Chemistry Biochemistry International Affairs Nursing Exercise & Health Sciences Communications Accounting Sociology Marketing Information Systems Geographic Info Sciences Finance Business Theatre Political Science Math Education Interdisciplinary Studies Environmental Studies Criminal Justice Computer Science Exercise & Health Science FIGURE 4: Distribution of Minors in Applied Statistics and Data Analysis by Declared Major (Fall 2012) Why do undergraduates at KSU seek out a series of five upper division electives in Statistics? We believe that there are three primary reasons that have created this demand: 1. Statistics at KSU includes an inherently interdisciplinary faculty the same faculty which will power the Ph.D. in Analytics and Data Science. Most of the Statistics faculty has had experience in the private sector, including Ford Motor Company, The Children s Hospital of Cincinnati, The Cancer Center at MD Andersen in Houston, TX, MasterCard International, VISA EU (London), AT&T/BellSouth (Brazil), Thompson Reuters, The Southern Company and ChoicePoint. Most students can find someone with an application of Statistics outside the classroom, aligned with their career aspirations. These experiences are brought into the classroom and students respond. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 14

15 2. Statistics is the process through which data is converted into meaningful information to support decision making. But, as outlined above, while data is increasingly ubiquitous and cheap and easy to capture and store, it is difficult to translate. Students recognize that whether they are studying Finance or Psychology, Biology or Political Science, they will have to understand how to translate data into information. This knowledge is no longer a differentiator but rather an ante to play. Since all disciplines work with data, in some form, all disciplines of study need to have some integration of Statistics for their graduates to be marketable. 3. Jobs. Jobs. Jobs. Students are increasingly turning to Statistics as a great way to position themselves in the marketplace. Undergraduates with Minors in statistics are having great success with job placement after graduation. Statistics students from KSU are recruited for positions across a wide variety of companies including The Home Depot, Coca Cola, The Southern Company, Link Analytics, Aspen Marketing Services, The Internal Revenue Service, Ultimate Software, IBM, Assurant, Compucredit, The CDC, Equifax. The Masters of Science in Applied Statistics has a similar story. Since the launch of the degree in 2006, very few of the applicants have had undergraduate degrees in Statistics. MSAS applicants come from Engineering, Business, Medicine, and Education. A defining characteristic of the MSAS program is its fluid alignment with the needs of the market the faculty works with their Advisory Board to consistently pivot the curriculum to be aligned with the market. As a result, the MSAS is proud of its effective 0% unemployment rate amongst students without work restrictions. Statistics emerged as a unique discipline at KSU in Fall of 2006 all of this success has occurred in less than 6 years. In an effort to ensure limited duplication with other successful initiatives in Statistics within the University System, such as the programs at the University of Georgia and at Georgia Tech, KSU pursued a strongly applied orientation, meaning that course materials were focused on leveraging faculty experience outside the classroom, and applying Statistics the way practitioners apply Statistics. From the beginning KSU elected to have less emphasis on theoretical Statistics. This last point meant that KSU would have to have strong integration of statistical software into the curriculum. In response, we looked to the dominant software/language in the marketplace. This was, without question, SAS, which is used DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 15

16 by 95% of the Fortune 500 including all of the top companies in our regional footprint. As a result, all of our students, both at the undergraduate and graduate levels, learn strong SAS programming skills as a complement to their statistics skills. It is this dimension of programming skills, combined with a strong mathematical foundation, and deep and broad instruction in statistical modeling which has already well positioned the program to offer a Ph.D. in Analytics and Data Science. c. Brief explanation of how the program is to be delivered Opportunities are usually disguised as hard work, so most people don't recognize them. Ann Landers While additional resources will be required to make the Ph.D. in Analytics and Data Science successful (see Section 4 below), much of the basic delivery infrastructure is in place. The general structure of the program will include three stages: Coursework Application Research Stage 1: Course Work If you only have a hammer, you tend to see every problem as a nail. Abraham Harold Maslow The Ph.D. in Analytics and Data Science will begin with 48 hours of core course work/instruction, spread over (expected) four years of study, plus six hours of electives and 24 (minimum) hours of dissertation and internship (78 total hours). In response to the market needs and skill gaps as outlined above, the Ph.D. in Analytics and Data Science will have a strong interdisciplinary and application orientation. Generally, coursework will be structured as provided in Figure 5. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 16

17 FIGURE 5: Proposed Approximate Distribution of Credit Hours for the Ph.D. in Analytics and Data Science Mathematics 15% "Content Application" 30% Computer Science 20% Statistics 35% A full listing of the proposed courses, and a sample program of study can be found in SECTION 5 below. The logic supporting this interdisciplinary approach is that the curriculum would be aligned with the needs of the marketplace as evidenced in Section 1b above. Students will be required to complete a comprehensive examination of their course materials before they are considered to have completed this stage. The comprehensive examination will cover materials from all of the three areas of study listed above. Stage 2: Application The Ph.D. in Analytics and Data Science is, at its core, an applied program. Ph.D. students would be required to engage in one year for a total of 15 credit hours of application. This application will take one of two forms. The first form of application is private or public sector work experience. The Statistical Advisory Board has agreed, in principle, to hire Ph.D. students on a contract basis for a minimum of one year, after they have completed their coursework but prior to completing their dissertation. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 17

18 This would accomplish three objectives: The hiring firm would cover one year (minimum) of doctoral student stipend ($25 - $30K). The Ph.D. student would apply concepts and skills learned in the classroom in a real environment. The experience has the potential to become a source of dissertation research. This integration with the companies represented by the Advisory Board also represents an important endorsement of the proposed program, as well as an extension of the engagement with the business community which has been the trademark of the Statistics programs to date. Letters of Intent and Support from the Statistical Advisory Board can be found in Section A.1. The second form would be to engage in applied research/scholarship projects with KSU faculty. Examples would include analytical work in the areas of Finance, Chemistry, Economics, Biology, Marketing, etc. It would be the intention that this second form of application would take place within the context of a grant, which would provide the funding for the student, as well as provide much needed analytical support for faculty engaged in grant-funded scholarship and research. It would be expected that either form of these 15 hours of application would segue into the third stage of the program dissertation research. Stage 3: Dissertation Research A Ph.D. in Analytics and Data Science would require a formal Dissertation process, involving an interdisciplinary committee, comprised of faculty from Statistics, Computer Science, and Mathematics. Depending upon the application path pursued above, a faculty member from a content discipline (e.g., Marketing, Finance, Chemistry, Biology, Economics) would be included and possibly an external committee member as appropriate. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 18

19 d. Prioritization within the institution s strategic plan The Vision statement for the university: Kennesaw State University will be a nationally prominent university recognized for excellence in education, engagement, and innovation. The Ph.D. program in Analytics and Data Science supports all three of these sources of recognition. As evidenced above, there is an unquestioned recognition of a current and emerging talent shortage in the areas of Analytics and Data Science. In addition, no university in the country has an advanced degree/curriculum which formally addresses the specific skill sets defining the Data Scientist. KSU has the opportunity to establish the model for such a degree. In addition, the Program is truly engaged engaged across academic disciplines, as well as engaged with public and private sector partners who support this initiative. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 19

20 SECTION 2. Description of the program s fit with the institutional mission and nationally accepted trends in the discipline. Noted above. SECTION 3. Description of how the program demonstrates demand and a justification of need in the discipline and geographic area (region, state, and nation) and is not unnecessary program duplication. The market demand for the skills which are aligned with a Ph.D. in Analytics and Data Science are evidenced above in Section 1b. The proposed Program is not only NOT a duplication of any programs currently in existence in the State of Georgia, the program would be the first of its kind in the country. A brief outline of the most closely related Ph.D. programs in the State of Georgia is provided in Table 1below. TABLE 1: Comparison of related Ph.D. programs in the University System of Georgia Institution Georgia Institute of Technology Georgia Institute of Technology Name of Program Ph.D. in Industrial Engineering with a Specialization in Statistics Ph.D. in Industrial Engineering with a Specialization in Computational Science and Engineering Stated Objectives Notes on Curriculum Program Housed The Ph.D. in (Industrial and Systems Engineering) is a research degree...students have the opportunity to pursue work at virtually any of the points across the applied/theoretical spectrum... Georgia Tech's CSE Ph.D. degree will prepare students for a variety of positions in industry, government and academia that emphasize research and development. Students will be well prepared for positions in industry and in government. Graduates may pursue work in software and systems for modeling and simulation, systems integration, data mining and visualization, high performance computing, and computational modeling. Academic career possibilities include research and education in departments concerned with advancing the stateof-the-art in the development and application of computational models in engineering, the sciences and computing. Courses incorporate strong mathematics, with methods courses aligned with manufacturing and engineering. Requirements include five core courses, two theory courses, three methods courses, one elective course (11 courses total). No requirement for internships or co-op. The program emphasizes the integration and application of principles from mathematics, science, engineering and computing to create computational models. Courses include 6 core courses in computational mathematics and in high performance computing, three elective courses which must go beyond using computers to deepen understanding of computational methods, preferably in the context of some application domain and three elective courses in an application domain (12 courses total). No requirement for internships or co-op. College of Engineering, H. Milton Stewart School of Industrial and Systems Engineering. College of Engineering, H. Milton Stewart School of Industrial and Systems Engineering. University of Georgia Georgia State University Ph.D. in Statistics Ph.D. in Mathematics and Statistics The Ph.D degree program in Mathematics and Statistics includes concentrations in bioinformatics, biostatistics, and mathematics. These concentrations address the critical need for mathematics faculty and the need for highly trained specialists in the areas of bioinformatics and biostatistics (the program) will graduate individuals with a broad background in applied areas for direct placement in business, industry, governmental institutions and research universities. Heavy theoretical emphasis placement is exclusively in academic positions. Program includes a minimum of 10 courses including four statistical theory courses (core), two subcore electives one of which is a statistical computing course, and four unspecified STAT electives. Heavy emphasis on mathematics. The four core courses include Real Analysis, Matrix Analysis, Theory of Probability and Linear Statistical Analysis. Remaining courses vary based upon selected concentration. The Concentration in Bioinformatics incorporates three computer science courses. Eighteen courses required. College of Arts and Science, Statistics Department. College of Arts and Sciences, Department of Mathematics and Statistics. DRAFT PROPOSAL Ph.D. in Analytics and Data Science Page 20

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