January 31, 2014 Working Group Name Working Group Co-Chairs Working Group Members Engineering, Computer Science, and Technology Academic Program Working Group Robert A. Freeman, Juan R. Iglesias Heinrich Foltz, Jungseok Ho, Wendy Lawrence-Fowler, Rajiv Nambiar, Mahmoud K. Quweider, Yong Zhou, Sanjay Kumar, Immanuel Edinbarough Executive Summary The proposed degree programs address the need for improved access to career choices in engineering and computer science in the Rio Grande Valley (RGV). This proposal maintains all existing accredited degree programs at both institutions, consolidates common disciplines, strengthens weak programs and creates a cost effective pathway to new programs including masters and PhD programs, as warranted by future developments in technology and by developments in the local, regional, national and global economy. ABET accredited engineering programs at UTPA include Civil Engineering, Computer Engineering, Electrical Engineering, Manufacturing Engineering, and Mechanical Engineering while UTB has an ABET accredited program in Engineering Physics. These five programs will serve as the initial engineering programs at UT-RGV. Both UTPA and UTB have ABET accredited programs in Computer Science. Under ABET regulations, a school may have only one accredited program in Computer Science, thus the degrees will be merged such that only one degree exists. However, to better meet the rapid expansion of the discipline, tracks have been defined that provide students with the option to choose a broad field major or to follow a more individualized degree by choosing one of five tracks within the accredited program: Computational Science, Human Centered Computing and Computing and Media, Data Management and Intelligent Information Systems, Computing and Information Security, and Software Engineering. An attached document describes the integrated computer science degree plans. UTB also provides a degree in Computer Information Technology. The goal is to have the degree ABET accredited by 2017. Working from a strong base of accredited Engineering and Computer Science programs, new undergraduate programs are proposed to expand the engineering disciplines available to students in the area and to offer instruction in new technologies as they emerge. To effectively develop these new programs, it is proposed that the existing Engineering Physics will serve as an incubator for these new programs. This approach allows the creation of new tracks within the existing Engineering Physics degree in areas such as Bioengineering, Chemical & Petroleum Engineering, and Aerospace to address Page 1 of 24
local economic interests without the expense associated with the creation of an entire program at one time. This approach sustains the accreditation status for students in the Engineering Physics degree plan and again, is specifically aimed at providing the scaffolding to introduce new disciplines in a gradual and cost effective fashion as warranted by future developments in technology and by developments in the local and global economy. Additionally, the Engineering Physics degree supports the creation of an elite program in Engineering that would draw the best and the brightest of the Rio Grande Valley to view UT-RGV as the place to go to get their engineering degree. Potential tracks that would elicit this type of attraction are Bioengineering (or Biomedical Engineering) and Nano- Materials and Technology. The proposal also addresses the reality that not all students interested in Engineering and Computer Science have the requisite background or competency in mathematics required by most engineering disciplines by proposing the continuation and strengthening of the existing UTB Engineering Technology degree. The engineering technology degree is not yet accredited and in its current state, with one FTE is not accreditable. The degree however is of great value to the area, especially as it relates to the impact that manufacturing, and infrastructure development and construction have on the local economy. Developing an accredited engineering technology program gives area students, inclined toward engineering yet not toward mathematics, the opportunity to pursue a degree while concurrently filling a significant local workforce need. The proposal addresses regional issues of college readiness in the K-12 student population in the Rio Grande Valley. The development of an educator track within the college in 2015 is focused on providing the area with much needed K-12 teachers with expertise in engineering and computer science. This will afford students who have a profound desire to share their knowledge the opportunity to encourage children to study STEM disciplines, as well as support the enhance the regional competency in the literacy of the 21 st century. Along with the undergraduate programs, the proposed new graduate programs are initially aimed at establishing Ph.D. s which are requisite to developing a research level institution. The PhD in Manufacturing & Advanced Materials specifically addresses the need for a highly trained technical workforce to encourage the existing manufacturing companies (maquiladoras) in the region to relocate their design functions to the area as well. This degree makes use of the extant local faculty expertise in the two areas of manufacturing and materials science. The Masters and PhD in Data Analytics responds to a national need for data management, data modeling, data integration, data warehousing, data analysis, data mining, data visualization, quantitative methods, and computational thinking. The proposed degrees pertinent to engineering and computer science disciplines enable faculty in the college to participate broadly in knowledge creation, application, and dissemination. Increased educational attainment, particularly as outlined in the proposal, will improve the quality of life of not only the students, but also their immediate and extended families. Maintaining and expanding the programs as proposed will formalize a sustainable infrastructure that will improve persistence and Page 2 of 24
encourage excellence, as well as nurture leaders prepared for the challenges that impact the social and economic well-being of the Rio Grande Valley, and more broadly the world. The proposed plan is dependent upon appropriate resources in terms of learning and research space and facilities, as well as technical and non-technical staff. Working Group Meetings Date Location/Format 10/11/2013 UTB / Face to face 11/18/2013 Teleconference / Face to face 12/13/2013 UTPA CESS / face to face 1/17/14 Harlingen / Face to face Current Academic Programs UTB Bachelor of Applied Technology in Computer Information Technology Bachelor of Science in Computational Science Bachelor of Science in Computer Science Bachelor of Science in Engineering Physics Bachelor of Science in Engineering Technology Master of Science in Computer Science Master of Science Interdisciplinary Studies in Computer Science UTPA Computer Science (BSCS & MS-CS & MS-IT) Civil Engineering (BSCE) Computer Engineering (BSCE) Electrical Engineering (BSEE & MSE-EE) Manufacturing Engineering (BS MfgE & MSE- MfgE & MS- Engr. Management) Mechanical Engineering (BSME & MSE-ME) Current Academic Programs to be Offered Fall 2015 Bachelor s Degrees Master s Degrees Doctoral Degrees Civil Engineering Computer Science Computer Engineering Electrical Engineering Computer Information Technology Information Technology Computer Science Computational Science Manufacturing Engineering Electrical Engineering Mechanical Engineering Engineering Physics Engineering Management Engineering Technology Manufacturing Engineering Mechanical Engineering Civil Engineering Page 3 of 24
New Academic Programs for Fall 2015 (Please indicate with an asterisk (*) your top 3 priorities.) Bachelor s Degrees Master s Degrees Doctoral Degrees Consolidate UTB Engineering Physics Civil Engineering Track within MSE Manufacturing & Advanced Materials* (EP) tracks in Mechanical, Electrical, Computer Engineering, & Computer Science with existing UTPA degrees * Expand Bio-Engineering Track within Computer Science Data Analytics* EP Start development of Aerospace Engr. track within EP Start development of Educator track within the College Expand Civil Engineering degree plan to include Architecture track New Academic Programs for Fall 2017 (Please indicate with an asterisk (*) your top 3 priorities.) Bachelor s Degrees: all to be accredited Start development of Chemical &/or Petroleum Engr. track within EP* Computer Information Technology Engineering Technology Educator track within the College Master s Degrees Computational Science Civil Engineering Concentration in MSE* Bioengineering Track within MSE Doctoral Degrees Computer Science Data Analytics* New Academic Programs for Fall 2020 (Please indicate with an asterisk (*) your top 3 priorities.) Bachelor s Degrees: all to be accredited Add a new degree if warranted by EP track incubation results Bio-Engineering Master s Degrees Engineering Physics* Engineering Technology Emerging Technology (TBD) Doctoral Degrees Computer/Electrical Engineering* Mechanical Engineering* Page 4 of 24
New Academic Programs for Fall 2025 (Please indicate with an asterisk (*) your top 3 priorities.) Bachelor s Degrees: all to be accredited Add a new degree if warranted by EP track incubation results Emerging Technology (TBD)* Master s Degrees Bio-Engineering* Civil Engineering* Doctoral Degrees Examples of innovative programs Identify institutions and/or programs that are organized in an innovative way. In what ways are the programs innovative? How does this organization promote student success and/or scholarly activity? 1. Develop graduate degrees in Data Analytics (Defined as the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories). 2. Use the Engineering Physics Degree concentration areas as a test-bed/incubator for new BS degrees and for flexible individually tailored degree plans. 3. Introduce a multifaceted Co-Op &/or Local Apprenticeship &/or Undergraduate Research experiential learning graduation requirement. 4. Work towards 5 year BS + MS degree plans for qualified students. 5. Develop an institution wide Adaptive Expertise Development (AED) program Challenge-Based Instruction employing the Legacy Cycle learning scaffold. Employs a curriculum development and implementation support team approach (modeled after the NSF ERC for Bioengineering Educational Technologies - VaNTH). Teams consist of IT, Learning Science, Assessment, and Domain experts. The goal is to develop multidisciplinary curriculum that enhances student ability to adapt their expertise to addrees challenges spanning beyond the scope of the domain in which they obtained the expertise. 6. Develop an Engineering Educator Program (similar to the UT Teach effort in the sciences) 7. Develop a Study Abroad in the Lower 48 program. The goal is to introduce our students to graduate research level educational institutions and the cultural aspects and expectations of those institutions. Possible consultations Identify campuses that faculty and administrators who are engaged in further planning may wish to study or visit and/or leaders/scholars that planners may wish to consult. Georgia Tech UTD TAMU Cal-Poly Pomona UT San Antonio Boston University Page 5 of 24
Trans-disciplinary Opportunities Describe the trans-disciplinary opportunities that extend beyond your group of disciplines. 1. An AED Honors program development of courses addressing societal issues related to technology and technological advancement and governance. Examples include political forces and their impact on infrastructure development, and other socio-technological challenges. Courses of this type would be the expected result of the AED curriculum development program. 2. Bi-National (US-Mexico) Environmental Sustainability program (Engineering and Environmental Science) 3. Engineering Physics possibility to develop concentrations/tracks in trans-disciplinary areas. 4. Computational Science concentrations in innovative applied computing for trans-disciplinary areas. Examples include, biomedical informatics, computational chemistry, and computational mathematics. 5. Degrees in Data Analytics Bicultural/ Biliterate/Bilingual Describe how the proposed academic programs reflect the bicultural/bilingual/biliterate mission of the new university and culture of our region. 1. Create a Bi-National (US-Mexico) Environmental Sustainability program working on common curriculum and interaction with regional universities in Mexico to address regional environmental issues crossing the boarder (e.b., water accessibility and quality. 2. Human accessibility studies in engineering and computer science (cultural influences in pursuing these disciplines). 3. Increase Study abroad programs with Mexican Universities 4. Create Student exchange programs in engineering and computer science with Mexican universities. Community Engagement Describe how the proposed academic programs reflect the community engagement mission of the new university. The proposed academic programs will have a multifaceted impact on the community. Undergraduate Programs The proposed new undergraduate programs are aimed at filling the gaps in the engineering disciplines available to students in the area. The fundamental discipline that doesn t yet exist here is Chemical engineering (with petroleum being closely related). Civil Engineering is an extreme example of the impact that a new program can have. Prior to the introduction of Civil Engineering at UTPA students had to go to either A&M Kingsville or UTSA to study the discipline. Now they can and do come here, as illustrated by the growth of the program from zero majors to over 350 majors (second largest in the college) in just over three years. Keeping the Engineering Physics degree is specifically aimed at providing the structure to introduce new disciplines in a gradual and cost effective fashion as warranted by future developments in technology and by developments in the local economy. This gradual introduction of disciplines will allow investigation with real data of the demand for the discipline. Indeed, this will allow for short term program durations as dictated by changing economic forces (the shale oil in the area will not last forever) and long term programs to support continued economic growth in areas requiring that expertise (manufacturing is an obvious example of a local economic engine). The engineering technology degree is not yet accredited and in its current state (only one FTE) is not accreditable. The degree however is of great value to the area, especially in terms of the emphasis manufacturing and infrastructure development and construction has on the local economy. There is a need for workers trained in the various engineering technologies where that training does not necessarily require the level of mathematical competencies that other engineering disciplines and degrees require. Developing an accredited engineering technology program will give students in Page 6 of 24
the area inclined toward engineering yet not toward mathematics that opportunity and at the same time fill a local workforce need. The development of an educator track within the college is aimed at providing the area with much needed K-12(?) teachers with expertise in engineering and computer science. This will also afford students who have a (profound) desire to share their knowledge and encourage children to not only study STEM disciplines but to hopefully show them how interconnected science and technology is with their daily lives and society in general. Graduate Programs The proposed new graduate programs are initially aimed at establishing Ph.D. s which are requisite to developing a research level institution. The first one, one that is long in the offing, in Manufacturing & Advanced Materials specifically addresses the need for a highly trained technical workforce to encourage the existing manufacturing companies (maquiladoras) in the region to relocate their design functions to the area as well. This degree also makes use of the significant local faculty expertise in the two areas of manufacturing and materials science. The second new proposed degree, data analytics (DA) is also broad and will enable many faculty in the college to participate. After this, the developments are then largely aimed at filling out the programs in the different disciplines which in turn also helps the development of the local expertise to staff all aspects of not only manufacturing concerns but other engineering industries as well. Academic Structure Describe the academic structure you are recommending for your group of disciplines. All academic programs and research centers related to engineering and computer science will be under the College of Engineering and Computer Science. The college will begin with the departments of Civil Engineering, Computer Science, Electrical Engineering, Engineering Science (formerly Engineering Physics), Engineering Technology, Manufacturing Engineering, and Mechanical Engineering. If student enrollment in a program is not enough to create an individual department then the program will be offered through a hosting department which will nurture the program until enrollment is large enough to create an independent department. Each program has its own program director. Other ideas: Different schools/institute under the college for applied/transdisciplinary/technology programs? Civil Engineering Department Fall 2015 Each major location must have a designee for signatures and have advising available Lower/Upper division structure and capacity limits for degree programs with excessive enrollment (Enrollment management with Institutional Research and Effectiveness support) Support for distance delivery via ITV Resources to support AED program Infrastructure and Human resources for additional faculty required to deliver new proposed degree programs Page 7 of 24
[Type the document title] The goal of the computer science program is to prepare students to invent the future of computing, the foundations and physical basis; to understand and realize the power of computations in algorithms, systems, software, architecture, and networks to benefit our community and the larger world. General Computing Track: Broad-based Major in Computer Science 1 The general computing track is the most flexible route through the Computer science major. It provides students with a broad-based background drawing from the entire spectrum of computing. Depending on the courses chosen, students can be exposed to the design and architecture of computers, the development of software, and the theory and techniques using in designing efficient computer programs. Students can also learn about the application of computers to problems in science, in human computer interaction and in data management. This track prepares students for many different careers in many different areas of computer science. (Existing ABET Accredited Program at UTPA and UTB) For the Broad-field major, students take the computer science core required courses (32 hours) include: CSCI 1101 Introduction to Computer Science CSCI 1170 Computer Science 1 Lab CSCI 1170 Computer Science 1 CSCI 2380 Computer Science II CSCI 2333 Computer Organization and Assembly CSCI 2344 Programming in Unix / Linux Environment CSCI 3333 Algorithms and Data Structures CSCI 3334 Systems Programming CSCI 3336 Organization of Programming Languages CSCI 3340 Software Engineering I CSCI 4325 Automata and Formal Languages and Computability CSCI 4390 Senior Project Students choose a 3 hour programming course from the following: CSCI 3326 JAVA CSCI 3328 C# Students must choose 6 hours from the following:: CSCI 4333 Databases CSCI 4334 Operating Systems CSCI 4335 Architecture CSCI 4345 Networking Students must select 6 advanced hours excluding programming languages from the following: CSCI 3342 Internet Programming CSCI 4185 Research Seminar CSCI 4301 Digital Image Processing CSCI 4302 Multimedia Systems CSCI 4310 Design and Analysis of Algorithms CSCI 4318 Cyber Security and Forensics 1 Undergraduate Course Matrix by Track with potential new courses is provided in Appendix A. Page 8 of 24
CSCI 4327 Compiler Construction CSCI 4336 Parallel and Distributed Computing CSCI 4341 Topics in Computer Science CSCI 4346 Advanced Operating Systems CSCI 4350 Artificial Intelligence CSCI 4360 Computer Graphics and Interactive Systems CSCI 4363 Advanced Computer Networks and Network Security CSCI 4381 Interactive Systems and User Interface Design CSCI 4382 Computer Visualization Other Courses: CSI 2302 Web Graphics and Animation CSCI 2320 Computer Programming in Another Language CSCI 3330 Internship in Computer Science CSCI 3328 Object Oriented Programming in C# Computer Science with Track Emphasis In addition to the broad based major in Computer Science, courses students may choose to structure their program around a particular area within computer science. These particular areas of tracks combine regular computer science instruction with sequences of senior level courses in computer science and other disciplines that will lead to a deeper understanding and student expertise in a particular area of computer science. All of the tracks are designed to fall under the current accreditation. The possible tracks are: Computational Science Human Centered Computing: Computing and Media Data Management and Intelligent Information Systems Computing and Information Security Software Engineering Computation and Mobile Devices Two additional tracks might include Information Technology and a track in Computer Science Education. There is ABET accreditation for Information Technology and this accreditation will be sought for Information Technology. Computational Science Track Computational Science is a multidisciplinary area within computer science drawing from traditional computer science, mathematics, the physical and biological sciences, engineering, and medicine. Our understanding of the natural world is now based on computation as well as on traditional theory and experiment. Numerical simulations permit investigations that would be too time-consuming, expensive, dangerous, or even impossible to do experimentally. Problems considered by computational scientists include climate and weather prediction, video game construction, and the discovery of new medicines and treatments among many others. Computer scientists with knowledge in these areas and with relevant computer applications will play essential roles in future advances. Page 9 of 24
The Computational Science track s flexibility reflects the diverse fields of Computer Science that are applied to areas throughout the STEM disciplines. Students can choose among several possible areas of emphasis including database design and data mining; algorithms, artificial intelligence and machine learning, and numerical computation. The track integrates knowledge and techniques from all of these disciplines to create computational technologies for a wide range of important applications in science and engineering. Students in this track will be prepared to work in teams to develop software and systems for a variety of applications. Employment examples include: bioinformatics, contributing to basic research in biology and medicine, identifying new drugs or drug targets. In addition to core requirements in Computer Science, students will be required to complete a minimum of 17 hours that must include 3 courses in anyone of the following disciplines with at least 3 hours at the upper division level: astronomy. biology, chemistry, physics, and environmental science. Course sequences might include: Broad Field Computer Science Core plus, e.g. Astronomy o ASTR 3302 Astrophysics o ASTR 3303 Numerical Methods Biology o CSCI 4341 Bioinformatics o CSCI 4341 Evolutionary Computation Human-Centered Computing: Computing and Media Computing is changing our lives. The transformation is shaped not only by technology but also by how people express themselves, how they think, and how they interact in groups. The Human-Centered Computing track will prepare students to contribute to this accelerating global process by combining technology and insight into cognition and social interactions in groups and whole societies. They will design and develop systems that support communication and collaboration. These systems can be used to address issues and opportunities in the world (Rio Grande Valley and beyond). The Human Centered Computing track integrates human computer interaction, design of interactive systems, computer supported cooperative work, computer supported collaborative learning, educational technology, tools that support creativity, user-developed knowledge collections, and gaming. Students in this track will be prepared to be leaders in shaping the media and modes of interaction that empower citizens to participate in their communities, support creative expression, and address human needs in the emerging digitally literate society. In addition to core requirements in Computer Science, students will be required to complete a minimum of 17 hours that must include 3 courses in anyone of the following disciplines with at least 3 hours at the upper division level: Cognitive Psychology, Sociology, Economics, Antrhopology. Course sequences might include: Broad Field Computer Science Core plus, e.g. CSCI 4334 Operating Systems CSCI 4350 / COCS 4350 Artificial Intelligence CSCI 4360 / COCS 4330 Computer Graphics and Interactive Systems CSCI 4381Interactive Systems and User Interface Design Page 10 of 24
CSCI 4341 Ubiquitous Computing CSCI 4341 Wearable Computing Possible Advanced Electives PSY 3324 Social Psychology PSY 4319 Cognitive Psychology PSY 4326 Cross-Cultural Psychology PSY 4343 Human Factors SOCI 3325 Social Psychology SOCI 4325 Cultural Sociology SOCI 4326 Population and Society Project South Texas Data Management and Intelligent Information Systems The data management and intelligent information systems track broadens the focus of database and data management techniques beyond their traditional scope. With the rapid proliferation of data (Big Data) and the interest in Knowledge Management and Intelligent Information Systems, this track addresses the theory and systems work in areas such as Big Data management, Cloud computing, probabilistic databases, stream processing, sensor-based monitoring, databases and the web, ML, data management for ubiquitous computing, data integration, and data mining. In addition to core courses, students will study Computing for Data Analytics, Computational Methods, Data and visual analytics, and choose electives from a set of courses that may include: fundamental of data signal processing, introduction to automation and robotics, environmental data analysis, information visualization, game theory and internet programming. In addition to the CS Broad Field Core, course sequences might include: CSCI 4341 Topic: Advanced Databases CSCI 4341 / COCS 4343 Data Mining CSCI 4341 Machine Learning CSCI 4341 Emerging Database Technologies and Applications CSCI 4341 Information Visualization CSCI 4341 Introduction to Information Theory Computing and Information Security / Cyber Security The students in the Computing and Information Security track gain theoretical understanding and technical skills they need to be leaders in the design, implementation, and evaluation of concepts, techniques, methods, and policies assuring the security of information. The information security / cyber security track provides students with background and an fundamental understanding of computer science, before they concentrate on technical issues of computer and information security. They will examine the dimensions of providing security for information processing systems, including secure operating systems including secure operating systems and applications, network security, cryptography, and security protocols. They will also gain an understanding of non-technical security issues including policy and organizational behavior. Page 11 of 24
In addition to core and specialized computer science courses, students will be required to elect 6 to 9 hours of advanced electives in policy and organizational behavior. Course sequences might include: CS Broad Field Core plus CSCI 4318 Cyber Security and Forensics COCS 4318 Forensics COCS 4319 Computer and Cyber Security CIS 4391 Business Information Security Electives may include: CSCI 3342 / COCS 2316 Internet Programming CSCI 4341 Mobile and Ubiquitous Computing CSCI 4341 The Art of Building Intelligent Devices CSCI 4341 Intelligent and Interactive Systems CSCI 4341 Pattern Recognition CSCI 4341 Pervasive Systems and Networking SOCI 4314 Sociology of Deviance PSY 3324 Social Psychology Policy and Policy Evaluation MGMT 4361 Organizational Behavior Computing and Mobile Devices Building on the existing Computer Engineering curriculum, specific courses will be developed that focus on mobile computing and mobile computing devices. Students will explore areas including smart device and smart device applications including wearable computing. In addition to the CS Broad Field Core, course sequences might include: CSCI 4336 Distributed Systems and Parallel Systems CSCI 4350 / COCS 4350 Artificial Intelligence CSCI 4360 / COCS 4330 Computer Graphics and Interactive Systems CSCI 4381 / COCS 4332 Interactive Systems and User Interface Design CSCI 4382 / COCS 4335 Computer Visualization CSCI 4341 Mobile Applications and Services for Converged Systems Additional Electives may include: CSCI 3342 / COCS 2316 Internet Programming CSCI 4302 Multimedia Systems CSCI 4341 Mobile and Ubiquitous Computing CSCI 4341 The Art of Building Intelligent Devices CSCI 4341 Wearable Computing CSCI 4341 Knowledge Based AI CSCI 4341 Game AI CSCI Natural Language Understanding Page 12 of 24
CSCI 4341 Computational Neuroscience CSCI 4341 Computer Simulation CSCI 4341 Intelligent and Interactive Systems CSCI 4341 Mixed Reality Experience Design Software Engineering Software permeates the very fabric of modern society. Entire industries such as transportation, shipping, banking, government, and medicine would be unable to function without software infrastructure. Software engineers work in teams to create and maintain this software, ensuring that the resulting systems are reliable, efficient, and safe. The Software Engineering track emphasizes courses in: Core software engineering concepts, methods, and tools; The understanding of user requirements and user interface design; The ability to design programming languages and software tools that support software development; and Working in teams to achieve complex objectives. Software Engineering is an area of study with significant potential for lifelong employment. The demand for software engineers is projected only to increase for the foreseeable future. The field of software engineering leads many published lists of fastest-growing occupations in the country. IN addition to the broad field core, course sequences might include: CSCI 3341 Software Engineering II 2 Semester Senior Design / Software Engineering Project Advanced Electives may include: CIS 3336 Systems Analysis CIS 4308 IT Project Management CIS 4336 System Design and Implementation CIS ERP Customization MGMT 4370 Project Management MGMT 4361 Organizational Behavior Information Technology (RIT, University of North Florida, Kennesaw State, and Southeastern Louisiana serve as models of accredited programs) IT is a discipline that takes many forms. With the explosive growth of the pervasive presence of computing in the world, IT professionals think about technology on both the individual and organizational level. The focus is on making things work in today s and tomorrow s computing environments. IT then becomes a toolkit for innovation, problem solving, and productivity improvement that provides a competitive edge. IT professionals require core competencies in programming and application development, web and mobile computing, database management systems, networking and system administration and user centered design and deployment and human computer interaction. Page 13 of 24
The IT track prepares students to secure positions as IT professionals in business and industry, have appropriate skills to be lifelong learners in the IT field, work as team members, take on leadership roles in the IT industry, and make contributions to the growing field of IT. Students will be able to analyze legal, social, security, and ethical issues that arise in the technology as well as address user needs through the selection, creation, implementation, integration, and administration of computer-based systems. The program's curriculum includes theoretical computer science topics and mathematics for graduates to successfully converse with other computing professionals while focusing more on the applications of computer science. Courses include: CSCI 1101 Introduction to Computer Science CSCI 1170 Computer Science 1 Lab CSCI 1170 Computer Science 1 CSCI 2380 Computer Science II CSCI 2344 Programming in Unix / Linux Environment CSCI 3336 Organization of Programming Languages CSCI 3342 Internet Programming CSCI 3340 Software Engineering I CSCI 4334 Operating Systems CSCI 4345 Networking CSCI 4318 Cyber Security and Forensics CSCI 4390 Senior Project Choices include: CSCI 4302 Multimedia Systems CSCI 4327 Compiler Construction CSCI 4346 Advanced Operating Systems CSCI 4360 Computer Graphics and Interactive Systems CSCI 4363 Advanced Computer Networks and Network Security CSCI 4381 Interactive Systems and User Interface Design CSCI 4382 Computer Visualization CSCI 4341 Advanced Databases CSCI 4341 / COCS 4343 Data Mining CSCI 4341 Gaming Page 14 of 24
Proposed M.S. / Ph.D. Program in [Big] Data Analytics Data Analytics: Motivation, Definition, Challenges, Demand, and Opportunities We are living in a time of unprecedented information growth. According to International Data Corporation (IDC), the rate of information growth appears to be exceeding Moore s Law and the amount of digital information is estimated to grow nearly 50 times larger over the course of this decade (2010 2020). Nowadays, the size of our Digital Universe is measured in zettabytes and quintillions of information containers or sources. The value of this massive amount of data for businesses, government, healthcare, education, and individuals is yet to be extracted and the next generation of professionals, specialists and scientists is yet to be brought up. Data Scientist or Data Analyst is the name of the new profession that is designated as the sexiest job of the 21st century by Harvard Business Review. Data Analytics is the science of today s data-driven world. It combines enterprise-level data management, data modeling, data integration, data warehousing, data analysis, data mining, data visualization, quantitative methods, computational thinking, and business intelligence to attack the common goal make sense of voluminous datasets in an organization to support data-driven decision making, planning, pattern recognition, trending, future performance prediction, and business success. While the Data Analytics body of knowledge encompasses the complete lifecycle of data acquire-store-analyze-visualize-manage-share-integrate-acquire, the main focus is finding the value for business in time- and cost-efficient fashion. Some of the novel challenges that Data Analytics faces today are data volume, data variety, data velocity, and data veracity that characterize modern, voluminous datasets. In other words, we now have more data than we used to handle, different forms of data that are not always manageable using traditional databases, high acquisition rates of new data that may have a limited usefulness window, and presence of data uncertainty that complicates data analysis. Yet the biggest challenge is the lack of professionals who specialize in Data Science and Data Analytics. The demand for Data Analytics is tremendous. Major multi-billion corporations and financial institutions, including Google, Yahoo!, Amazon, Facebook, Twitter, Netflix, Microsoft, IBM, Wal-Mart, New York Stock Exchange, and many others, collect terabytes and even petabytes of data every day as we write. This data is critical to their operation and competitive advantage; in fact, it is frequently a single most valuable asset of an organization. The government and its anti-terrorist surveillance program ran by the Department of Homeland Security and National Security Agency logs millions of phone calls and billions of Internet communications. Obamacare or Affordable Care Act, while just launching, will undoubtedly collect huge amounts of health-related data from millions of subscribers. It is no surprise that in 2012 the Obama Administration announced the Big Data Research and Development Initiative and challenged the industry, research universities, and non-profits to join with the Administration to make the most of the opportunities created by Big Data. Life and earth scientists, among others, have collected vast amounts of complex, heterogeneous data, wondering How the hell do you analyze that data? and whether they should learn computer science or teach computer scientists their discipline. Furthermore, while many organizations have already hit the wall of big data, many others are expected to meet the challenge this decade. There is simply no data analytics workforce in existence to support this demand. There have been no university programs offering degrees in data science and data analytics a year ago (in 2012). While such programs (half a dozen) are starting to pop up and some will admit their first students in 2014, it will take time to develop expertise and competence. The Department of Computer Science at the University of Texas Pan American is in a unique position to develop and deliver the first program in Data Analytics in the region and the state of Texas. The department has three database professors with the broad range of expertise in data technologies, two artificial intelligence professors with interests in machine learning, decision making, and recommendation systems, one professor with primary expertise in data visualization, and multiple professors specializing in related advanced information technologies and computer science areas. In addition, the department plans to strengthen the existing expertise and acquire additional strengths with the two new hires in 2014. Page 15 of 24
A program in Data Analytics opens many new opportunities for UTPA students and faculty. At the time of writing, the popular job search engine (http://www.indeed.com) finds over 2,400 openings in Texas and over 38,000 openings nationwide for data analysts and data scientists (LinkedIn lists over 11,000 jobs with data analyst in their titles). For comparison, keyword programmer fetches roughly only half of those amounts for Texas and USA. While the Bureau of Labor Statistics does not recognize the new occupation due to its novelty, in general, most computer and information technology occupations are projected to grow faster than average. Furthermore, Information Week estimates big data skills gap to be 1.7 million workers by 2018. Therefore, the employment prospects for the students are excellent. The UTPA faculty involved in a Data Analytics program will be able to take advantage of new industry research collaboration and federal research funding opportunities. With the launch of the National Big Data Research and Development Initiative in 2012, six Federal departments and agencies announced more than $200 million in new funding. The Administration is particularly interested in projects that advance technologies that support Big Data and Data Analytics, educate and expand the Big Data workforce, and foster regional innovation, which coincides with our vision of UTPA Program in Data Analytics goals. References and Recommended Reading 1. Data Scientist: The Sexiest Job of the 21st Century (Harvard Business Review, October 2012), http://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century/ar/1 2. Big Data: The next frontier for innovation, competition, and productivity (McKinsey, May 2011), http://www.mckinsey.com/insights/mgi/research/technology_and_innovation/big_data_the_next_ frontier_for_innovation 3. 2011 IDC Digital Universe Study: Big Data Is Here, Now What? (2011), http://chucksblog.emc.com/chucks_blog/2011/06/2011-idc-digital-universe-study-big-data-ishere-now-what.html 4. Special Report: It s a Smart World (The Economist, November 2010), http://www.economist.com/node/17388368 5. Scientific Data Has Become So Complex, We Have to Invent New Math to Deal With It (Wired Science, 2013), http://www.wired.com/wiredscience/2013/10/topology-data-sets/ 6. Big Data Is Too Big for Scientists to Handle Alone (Wired Science, 2013), http://www.wired.com/wiredscience/2013/10/big-data-science/ 7. Infographic: The Four V's of Big Data (IBM Big Data Hub, 2013), http://www.ibmbigdatahub.com/infographic/four-vs-big-data 8. Big Data Talent War: 10 Analytics Job Trends (Information Week, March 2012), http://www.informationweek.com/software/business-intelligence/big-data-talent-war-10- analytics-job-tre/232700311?pgno=3 9. Big Data Across the Federal Government (The White House, March 2012), http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_fact_sheet_final_1.pdf 10. Big Data is a Big Deal (The White House, March 2012), http://www.whitehouse.gov/blog/2012/03/29/big-data-big-deal 11. Making the Most of Big Data (National Science Foundation, 2013), http://www.nsf.gov/cise/news/2013-bigdata-announcment.jsp Sample Programs and Industry Training/Certification in Data Science and Data Analytics Academia: 1. The City University of New York, http://sps.cuny.edu/programs/ms_dataanalytics 2. Columbia University, http://idse.columbia.edu/masters 3. University of San Francisco, http://www.usfca.edu/analytics/ 4. Northwestern University, http://www.scs.northwestern.edu/program-areas/graduate/predictive-analytics/index.php Page 16 of 24
5. University of Maryland, http://www.umuc.edu/academic-programs/masters-degrees/dataanalytics.cfm Industry: 1. EMC, https://education.emc.com/guest/campaign/data_science.aspx 2. Cloudera,http://cloudera.com/content/cloudera/en/training/courses/data-analyst-training.html 3. Hortonworks,http://hortonworks.com/hadoop-training/applying-data-science-using-apachehadoop/ A Model Program: M.S. in Data Analytics Core (21 hours required) CSCI 6xxx Algorithms and Computation for Data Analytics (3 hours) CSCI 6xxx Statistics and Probability for Data Analytics (3 hours) CSCI 6xxx Machine Learning (3 hours) CSCI 6xxx Data Acquisition and Management (3 hours) CSCI 6xxx Data Modeling and Data Integration (3 hours) CSCI 6xxx Data Warehousing and Data Mining (3 hours) CSCI 6xxx Data and Information Visualization (3 hours) Electives (9 hours required) CSCI 6xxx Relational Databases (equivalent to CSCI 6333/6315) (3 hours) recommended CSCI 6xxx ML Databases (3 hours) recommended CSCI 6xxx Distributed Databases and Computing (3 hours) recommended Other CSCI courses, including CSCI 6381 Independent Research and Study New Program Assessment of Need New professors (2-3) to support the program and bring in required expertise. Additional equipment and space for a small cluster capable storing and processing large datasets. Higher salaries and/or other initiatives; people with data analytics skills are in short supply and very high demand. Other initiatives may include: o Reduced workload for professors developing new courses. o Summer salaries for professors developing new courses. o Industry training/certification for professors teaching and developing new courses. Proposed M.S. Program in Computational Science Computational Science combines methods of computer science and applied mathematics with the study of problems in science and engineering. It is an inherently interdisciplinary endeavor that provides high payoffs in the subject fields involved. Nationally, academic programs in computational science are becoming widespread. Implementation of a computational science program in the UTPA College of Science and Engineering can provide 1) for faculty, access to methodologies for scientific investigation that are currently lacking, 2) for the college, a springboard for further programs, e.g., computational biology, computational fluid dynamics, computational physics, and 3) for students, a highly marketable degree. Page 17 of 24
The recent emergence of computational science has been facilitated by explosive growth in computational power primarily through the use of parallel and distributed systems. This form of computation requires mastery of new principles of computation and numerical methods coupled with practice in emerging hardware systems and software tools. Due to the applied nature of computational science, the curriculum is best implemented in an environment affording ample opportunity for student involvement in solving real world scientific and engineering problems, such as provided by the graduate programs in the UTPA College of Science and Engineering and other colleges. Several factors combine to allow cost-effective implementation and development of a high quality computational science program in the UTPA College of Science and Engineering: recent faculty growth, needs and skills of new faculty, and maturation of graduate programs. Departments in the core disciplines of computer science and mathematics have been revitalized in the last four years, adding 43% and 38% tenured and tenure track faculty, respectively. Most newly hired faculty across all the science and engineering disciplines make use of computational science methodologies in their research, e.g., computational physics, and so provide a foundation of discipline specific expertise, as well as a need for intellectual and physical resources. It is likely that future hiring will only accelerate this trend, e.g., computational biology and chemistry. At UTPA the maturation of the computer science and mathematics master s programs, together with recent and anticipated faculty hiring in biology, chemistry and physics, provide the human resources with which to deliver a computational science program at UTPA. The UT-RGV Medical School research initiatives will also benefit from capabilities in computational science. In part, the emergence of computational science as a separate discipline is the result of the natural evolution of disciplines use of computing technology, especially parallel technologies in the scientific enterprise. At UTPA, for example, recent courses additions include CSCI 6356: Parallel Computing, CSCI: 6370 Bioinformatics and Computational Biology, and MATH 6375: Numerical Analysis. Expansion of research efforts across the STEM colleges and the medical school can provide opportunities for student involvement in computational projects in disciplines including, but not limited to physics, geology, engineering, mathematics, biology, and chemistry, as well as new biomedical and computational programs that might be developed. Two parallel computing facilities are available that can meet the initial needs of a computational science program. The Department of Computer Science maintains a 32 node cluster for use by students and faculty. For a computational science program, this system would be primarily experimental and serve as a test-bed for new hardware and software configurations. Also available is a Dell 8 node 16 processor system acquired by the Physics and Geology Department and the Computing and Information Technology Center (CITeC). This system would be maintained as a stable platform for computational science programming and research. Ongoing efforts to acquire systems capable of meeting longer term needs through external funding are being coordinated by CITeC and involve a multidisciplinary group of faculty in physics, mathematics, computer science, and engineering with shared interests in computational science. The proposed Computational Science program would build upon existing computer science courses, supplemented by existing mathematics courses, to apply principles in these disciplines to scientific and engineering domains. The program will take the form of Computational Science track in the existing Master of Science in Computer Science. A Model Program: M.S. in Computational Science Core (15 hours required) CSCI 6305 Foundations of Algorithms, Data and Programming Languages (3 credits) CSCI 6323 Design and Analysis of Algorithms (3 credits) MATH 6375 Numerical Methods (3 credits) CSCI 6339 Theoretical Foundations of Computation new course CSCI 63 Computational Science (3 credits)*** Page 18 of 24
Prescribed Electives (15 hours project option, 12 hours thesis option) CSCI 6333 Advanced Database Design and Implementation (3 hours) recommended CSCI 6337 Simulation (3 hours) recommended CSCI 6361 Visualization (3 hours) recommended CSCI 6356 Parallel Computing (3 hours) recommended ELEE 6372 Parallel and Distributed Systems Mathematics Prescribed electives: Select at least one course from: MATH 6365 Probability and Statistics (3 hours) recommended MATH 6366 Mathematical Statistics MATH 6387 Mathematical Modeling (3 hours) recommended Project or Thesis, 6 or 9 hours 3 hour Project, with 3 hours pre-project study 6 hour Thesis, with 3 hours pre-thesis study Note: As computation-oriented courses are developed in the several disciplines, a third set within Prescribed Electives would be added from which students would select at least one course to insure expertise in a domain area, e.g., Specialization Prescribed Electives (future). Select at least one course from: Computational Biology Computational Chemistry Computational Fluid Dynamics Etc. Page 19 of 24
Appendix A: Undergraduate Computer Science Course by Track Matrix CORE CSCI 1101 Intro to CS CSCI 1170 CS I Lab CSCI 1370 / COSC 1336 CS I CSCI 2380 / COSC 1437 CS II COCS 2336 Programming Fundamentals III CSCI 2333 / COCS 2325 Computer Organization and Assembly CSCI 2344 Programming in Unix / Linux Environment CSCI 3333 / COCS 3335 Algorithms and Data Structures/ Algorithm Analysis CSCI 3334 Systems Programming CSCI 3336 / COCS 3355 Organization of Programming Languages CSCI 3340 / COCS 4346 Software Engineering I CSCI 4325 / COCS 4361, COCS 4362 Automata and Formal Languages and Computability; Computability Broad- Field Major Computa tional Science Human Centered Computing: Computing and Media Data Management and Intelligent Information Systems Computing and Information Security Software Engineerin g IT Page 20 of 24