MS in Analytics Modular/Stackable Design (33 credit hours)

MS in Analytics New Degree Program Proposal Identifying information Graduate or Undergraduate: Graduate Academic unit: Kogod School of Business Teaching unit: KSB, Information Technology and Other Departments Name of degree: MS in Analytics Name(s) or Program(s): MS in Analytics Proposed effective date: Fall 2015 MS in Analytics Modular/Stackable Design (33 credit hours) Capstone Experience: KSB 620 Analytics Practicum (project, no lectures, in functional area 2 x 1.5 cr. courses = 3 cr.) Functional Specialization (12 cr.) Or, Functional Background (12 cr.) Accounting Forensics, Finance Marketing, IT Consulting Computational Statistics, Biostatistics Etc. Management, Int l Business Comp Science, etc. Analytics Core (9 cr.): ITEC 620, ITEC 621, ITEC 660 Core Competencies (9 cr.): KSB 065, ITEC 616, ITEC 610, ITEC 670

MS in Analytics Curriculum Overview (33 credit hours) Capstone Experience KSB 620 (New) Analytics Practicum 3 credits (2 x 1.5 credits each semester) Functional Specialization choose 12 credits from Accounting Forensics: 12 credits 9 credits from ACCT 551 Forensic Accounting (3); ACCT 677 Financial Statement Analysis (3); ACCT 680 Advanced Forensic Accounting & Fraud Detection (3); plus 3 credits from ACCT 549 Contemporary Assurance & Audit Services (3); ACCT 550 Accounting Information Systems (3); ACCT 600 Ethics in Business & Accounting (3); ACCT 607 Financial Accounting (3) ; ACCT 760 Advanced Auditing & Professional Practice (3); FIN 614 Financial Management; and FIN 630 Financial Analysis of the Firm: Concepts and Applications. Quantitative Financial Analysis I: 12 credits from FIN 605 Managerial Economics (3); FIN 614 Financial Management (3); FIN 660 Financial Modeling (3); FIN 574 Quantitative Methods in Finance (3) Quantitative Financial Analysis II: 12 credits from FIN 614 Financial Management (3); FIN 660 Financial Modeling (3); FIN 665 Quantitative Methods in Finance I (3); FIN 666 Quantitative Methods in Finance II (3). IT Consulting: ITEC 643 Project Management (3); ITEC 630 Business Process Analysis (3); ITEC 666 Cyber Security Risk Management (3); ITEC 622/MGMT 622 (NEW) Organizational and Social Network Analytics (3); Marketing: 12 credits from MKTG 612 Marketing Management (3); MKTG 755 Applied Market Segmentation (1.5); MKTG 561 Customer Relationship Management and Database Marketing (3); MKTG 741 Digital Marketing (1.5); MKTG 767 Research for Marketing Decisions (3) Computational Statistics (CAS): 12 credits from STAT 515 Regression; STAT 524 Data Analysis; STAT 525 Statistical Software (R, SAS); MATH 460/660 Tools of Scientific Computing (prerequisites: CSC 280, CSC 281, MATH 221, and MATH 222) Biostatistics (CAS): 12 credits from STAT 515 Regression; STAT XXX Advanced Biostatistics (currently a special topics course (or STAT 524 Data Analysis or STAT 510 Survey Sampling); STAT 520 Multivariate Analysis; STAT 521 Categorical Data Analysis. Any suitable 12 credit specialization certificate from AU academic units outside of Kogod, as approved by the respective academic unit, the Program Director, the EPC and the Kogod Council or Functional Background choose 12 any of the above and/or: Any of the functional specialization courses listed above Business Fundamentals courses from the MS in Management curriculum (i.e., FIN 605 Managerial Economics and Corporate Strategy (3); ACCT 607 Financial Accounting (3); plus 6 credit electives) Other Kogod graduate coursework as approved by the Program Director or the respective Department Chair. Any suitable set of 12 credit analytical course electives from AU academic units outside of Kogod, as approved by the respective academic unit, the Program Director, the EPC and the Kogod Council Analytics Core 9 credits ITEC 620 Business Insights through Analytics (3) ITEC 621 (NEW) Predictive Analytics (3) ITEC 660 Business Intelligence (3) Core Competencies 9 credits KSB 065 Analytics Readiness (0) (competency exam and prep course online) ITEC 616 Management Information Systems (3) ITEC 610 Applied Managerial Statistics (3) ITEC 670 Database and Big Data (3)

A. Rationale What is the role of the proposed program in relation to the goals and long-range plans of your teaching unit? American University: AU s Big Data, Analytics and Applied Data Science initiative is an AU Project 2030 proposal approved and supported by the Provost s Office. This proposal was prepared by a committee of 17 faculty and staff from all schools and most academic units. The committee developed the proposal utilizing findings from an Educational Advisory Board study commissioned by the Provost s Office which was based on publicly available data collected on 40 programs in big data, analytics, data science and other related areas from 30 institutions, as well as interviews of school administrators at six of these institutions. The study was complemented by several workshops and tutorials conducted by the Kogod School of Business, colleagues from other institutions, and experienced practitioners. The main conclusion from the study leading to the AU 2030 proposal was that without question, we have entered the age of big data and analytics, and AU needs to move aggressively into this aspect of research and education to remain competitive, not only in 2030, but also in the near future. The main recommendation of this proposal is to enhance AU s capabilities in applied data science, through the increased focus on research and education in big data and analytics across all disciplines. Consistent with the recommendations of this proposal, three new faculty lines have been approved in CAS and new courses have been developed and/or proposed e.g., computational statistics, SIS-619-038OL Big Data & Text Mining, ITEC-320 Business Analytics and ITEC-620 Business Insights from Analytics. At the most recent AU Academic Leadership Retreat in October of 2013, several strategic goals were articulated under the theme Demand More, Provide More, including the recommendations of both the Quantitative Training and the Big Data task forces, to provide more in depth education in quantitative, analytical and data areas. As part of the goals of this AU Project 2030 concept, we are proposing the creation of an MS in Analytics. As detailed below in this proposal, the design of this program is modular and flexible to facilitate integration with curriculum from any functional domain, thus supporting the long-range goals of fostering inter-disciplinary programs with an analytics focus, both within Kogod and externally with other academic units on campus. Kogod School of Business: One goal stated in the present Kogod Strategic Vision is for our programs to become more analytical. Enhancing the analytical content of the curriculum is also a goal emphasized by the AACSB. Recognizing the importance of these goals, a proposal was recently developed and submitted (by other colleagues) to the EPC to increase the analytical skills of our students, in recognition of the fact that our students need more quantitative skills and analytical training. Similarly, the IT Department has developed three courses to support these goals, including ITEC-320 Business Analytics, ITEC-470/670 Database, and Big Data and ITEC-460/660 Business Intelligence. Moreover, the new Full-Time MBA program was designed to include more

analytical content, leading to the creation of a new course, ITEC-620 Business Insights from Analytics. Consistent with these initiatives and the ones discussed above for AU as a whole, the proposed MS in Analytics is intended to provide a framework in which graduate analytics education can be enhanced, not just by training students on appropriate quantitative and analytical skills, but also by integrating the analytical curriculum with education in functional domains, which will enable students to better understand how to conduct analysis in the respective functional specialization domains. In sum, the modular/stackable design of the proposed MS in Analytics will not only be attractive to a new student audience, thus fostering enrollments, but it is also intended to provide internal support to all Kogod departments and AU academic units, to better integrate analytic education in theory, methods, approaches and tools to make our graduate students better-prepared for today s analytical and data-driven market. Does this program duplicate any other programs offered by other teaching units or academic units or by other members of the Consortium of Universities of the Washington Metropolitan area? No. On the contrary, this program is intended to reinforce, complement and integrate with programs in Kogod and other teaching and academic units at American University. Given the rising interest in data analytics, George Mason University (GMU), George Washington University (GWU), and the University of Maryland (UMD) from the Consortium of Universities of the Washington Metropolitan area have recently launched masters programs in data analytics. The GMU program is housed in the School of Engineering and targets a very different market segment than our proposed program. Whereas the programs at GWU and UMD focus predominantly on general analytics training or at the most on one functional specialization (e.g., marketing analytics UMD program), our program is distinguished by its strong emphasis on analytics with a choice of several functional specializations. Will this program replace or complement any other programs now offered by your teaching unit? By other teaching units? By other members of the Consortium of Universities of the Washington Metropolitan Area? Explain. It will not replace other programs, but it will complement them. Our program has been specifically designed to serve multiple functional areas. There are two areas of consensus in all the studies we have reviewed on analytics education, which are: (1) meaningful analytics projects are a multi-functional team effort; and (2) knowledge of the domain of analysis is imperative (i.e., technical/mathematical skills are insufficient). Essentially, as stated in various articles, there are two broad categories of analytics professionals: (1) those with deep analytical skills trained in data science, math, statistics, computer science, etc.; and (2) managers and functional experts capable of answering questions in their domains of expertise, who are also savvy consumers of analytics reports. The McKinsey Global Institute reported in 2011 that by 2018 the US will face a shortage of 140,000 to 190,000 people in the former and about 1.5 million people of the latter. Our

proposed program targets the second group, but other more technical and quantitative academic units (e.g., Math/Stats, Computer Science, Economics), could easily furnish a 4-course package and a practicum (or even more if necessary) to integrate with our program to target the first group. For example, a 4-course group of courses (or stackable certificate) in computational statistics, mathematical analysis or any other analytical area would integrate very well with the proposed program. Furthermore, given the proliferation of MS programs and course formats of recent years, this program has been intentionally designed to maximize the use of existing courses, formats and certificates. Furthermore, the modules described above can most certainly result in specific certificates. For example, from the 18 credits in the Core Competencies and Analytics Core modules, various 12-credit certificate packages could be created. For instance, a predictive analytics certificate could include ITEC-616 (IT), ITEC-610 (Managerial Stats), ITEC-620 (Intro to Analytics) and ITEC-621 (Predictive Analytics). In fact, in meetings with Peter Starr and Mieke Meurs from CAS they not only expressed interest in our program and provided 2 functional specializations (computational statistics and biostatistics), but would like to carve out an analytics module to complement their modular masters design. Similarly, SPExS is contemplating launching graduate certificates on healthcare analytics and they have expressed an interest in carving out the analytics certificate from our analytics course modules. In the functional specialization layer, we are working with faculty from various Kogod departments and the 12-credit course groups listed are either existing certificates (e.g., accounting forensics), planned certificates (e.g., finance) or bundles of existing courses (except one course labeled "new"). CAS has provided two 12-credit course packages in computational statistics and biostatistics as functional specialization areas for this program. We are also having conversations with CAS, SPA and SIS to identify additional 12-credit course packages for functional specializations. In addition to the functional specializations listed above, likely candidates include software programming (CAS), criminology (SPA), and global analysis (SIS). Naturally, this requires a lot of coordination with the respective units, but the idea is to restrict the selection of functional specialization to existing courses that are analytical in nature, rather than creating new courses. However, if the academic units wish to create new courses they can go through their respective curriculum approval processes. How do the requirements of the proposed program compare to the requirements of similar programs at other institutions nationally? Please give examples of similarities and differences from leading programs. Many schools in our region and elsewhere are launching analytics, big data and/or data science programs. In recognition of the possibility of being lost in the crowd, we purposely sought to design a program that had distinguishing characteristics. We

prepared an initial design for our program, which we subsequently discussed with our Information Technology Executive Council. The main conclusions from that discussion were that: (1) Kogod must launch an MS program in analytics to remain competitive; (2) leading employers in the consulting field often will not consider applicants that don t have formal specific training in analytical methods and tools; (3) that our best audience are young managers with specific functional knowledge either acquired through experience or through education in areas of functional specialization; and (4) that a distinctive program must have a strong experiential and hands on component. We also discussed our initial design with analytics experts and consulting firms like IBM, Computer Sciences Corporation, and Deloitte Consulting, who essentially said similar things. Kogod s Marketing and Communications also conducted a study of our competition and reported that: The main local competitors to Kogod s proposed MS in Business Analytics degree are George Washington University and the University of Maryland. These programs were recently launched and primarily seek to draw students from the Washington, D.C. area. Due to the newness of each program, it is difficult to ascertain how many students each will draw regularly, but both seek to develop their programs quickly and become the first mover in the region. George Mason University s School of Engineering offers a Graduate Certificate in Data Analysis, but does not appear to be a serious competitor and does not attract purely business-focused students. The leading programs in the field offer more intensive master s programs that create more added value for a student than a graduate certificate. Additionally, George Mason does very little to promote its program in the business arena, as the certificate is taught by the engineering school. Nationally, strong programs include Bentley University, Carnegie Mellon University, and Drexel University, which have the advantage of being well known, thus extending their reach into the D.C. market. While Carnegie Mellon is the only of the three with a strong foothold in D.C. (it has a Washington campus), the three schools compete nationally due to their reputations. When searching for business analytics degrees, it is nearly impossible to avoid them as a result of rankings and news buzz. These programs have developed relationships with employers that aid in the program s content and are highly involved in career services. Bentley has extensive corporate partnerships primarily focused on companies in the Boston region, of which many have connections to the D.C. market (Cisco Systems, Raytheon, Ericsson, and more). Carnegie Mellon s Heinz School of Public Policy offers a D.C. immersion program for its MS in Public Policy and Management, making it a familiar name in the region. Drexel offers corporate consulting classes, in which the students are essentially hired as consultants.

The distinguishing features of our program include: (1) the capstone course (practicum). Most other programs simply conclude with the student selecting elective courses; (2) most other programs focus predominantly on analytics training, whereas our program complements analytics training with functional specialization in both business and non-business areas; and (3) DC location and proximity to potential employers, although UMD, GMU and GWU share this distinction; however our strong foothold in the consulting field provides Kogod with a unique competitive advantage in this area. We redesigned the initial program based on all this information and using feedback received in individual discussions with all Kogod departments, with a final program design emerging with the following distinctive characteristics: The program can be completed in one year, provided that 6 credits are covered in the summer at the beginning and that the practicum is offered as a no-lecture 1.5 credit complement on top of 12 credits per semester. The program seeks to integrate with existing curriculum, not just at Kogod, but across campus. In consultation with various Kogod departments, the following functional domains have been already identified: accounting forensics, financial analysis (for light finance background), financial analysis (for strong finance background), marketing and IT consulting. Outside Kogod, a computational statistics and biostatistics functional specialization is being offered by CAS as part of this program. Other possible programs discussed with colleagues outside of campus include: criminology (SPA), math/stats, computer science, physics and international analysis (SIS). The 3-credit practicum is a key component of the program, intended to provide hands-on experience to make students job-ready. It can also serve as a networking mechanism, which has been done quite successfully with the support of Kogod Center for Career Development support in other areas such as consulting. A Kogod faculty would be assigned to this course to supervise projects, but other faculty may also supervise specific practicum projects. More importantly, the practicum offers the possibility to serve as a 5 th course for non-kogod programs seeking to have control over the experiential learning of their students. How does this program extend the intellectual development of its students beyond training within a field? Analytics is one of the strongest trends in business education and perhaps one of the strongest and most rapidly adopted disciplines we have seen in our times. This program provides a great opportunity for intellectual development of students beyond their field. By its nature, analytics provides the intellectual capability to extend students knowledge

beyond the standard practice in their disciplines into more data-driven analytical domains. Furthermore, while the main goal of the program is to prepare students in the MS in Analytics programs, the offering of analytics courses will also provide students outside of this program with the opportunity to take electives in this very important area. Does this program compete with other similar programs in the area? If so, what will be the distinctive attraction or need that will ensure student enrollment? The proposed MS in Analytics and the associated stackable certificates will give Kogod a distinctive position in the market, relative to the major competitive university business schools in the areas of consulting. While major local competitor business schools do offer various programs in analytics, they focus more on deep analytical training, rather than on training managers to become analysts in their respective domains. As mentioned above, just about every school is creating a program in data science, big data and/or analytics. Many programs have simple rebranded management science and operations research courses under the umbrella of analytics. Other schools are specializing on particular functional domains. For example, the Smith School of Business at the University of Maryland has launched a program on Marketing Analytics. Similarly, Georgetown University is planning heavy investments in this area and they already have a continuing education program on Marketing Analytics. In contrast, our program combines analytical training with application in different functional areas (e.g., biostatistics, computational statistics, forensic accounting, finance, and marketing). The analysis conducted by Kogod s Marketing and Communications group discussed above provides further information about the distinctiveness of our program. Please include statements from the deans of the other academic units and a statement from the University Librarian. All deans have been very supportive of the AU Project 2030 proposal on big data, which was presented at two Provost meetings with all the Deans, who further supported the initiative at the Leadership Retreat in October of 2012. While all deans are very supportive of graduate education in analytics, the Dean of CAS has expressed particular interest in this specific degree and would like to integrate curriculum from the Math/Stats and Computer Science Departments with this program. Peter Starr and Mieke Meurs from CAS have provided two functional specializations (computational statistics and biostatistics), and are also interested in including an analytics module in their modular masters design. Similarly, SPExS is contemplating launching graduate certificates on healthcare analytics and they have expressed an interest in including an analytics certificate from our analytics course modules. In addition, the AU Library has created a new position for a Data Librarian and hired an experienced professional for this position. Please include statements from all affected teaching units (units that need to provide courses, mentorship, or other support for this program). Such statements should provide an evaluation of the need for this degree, how it relates to programs within the affected

teaching unit, and if applicable what courses the teaching unit needs to provide in order to support the proposed program. All Kogod department have been consulted and are supportive of the proposed program. Four departments (Accounting, Finance, Marketing and IT) have provided 12-course sets for functional specialization, with the Finance Department providing 2 options, one for students with light Finance background and another for those with strong Finance background. The other two departments (International Business and Management) have provided a list of elective courses that could be selected for functional background, but have not ruled out the possibility of providing a 12-credit course set for a functional specialization in the future. In addition, outside Kogod, CAS has provided two functional specializations as part of the program. We anticipate, other non-kogod units will similarly provide functional specializations once the program becomes operational. B. Student Interest Please describe the most commonly expectable educational and occupational outcomes within five years of completion of the proposed program, including any available outcome data. The main goal of the program is to train and educate students on best practices associated with professional analytics work. Upon graduation, the students will be familiar with the key steps of the analytics process, which include how to: (1) formulate a relevant business question or hypothesis; (2) search and identify the necessary data sources to answer that question; (3) evaluate and select the most appropriate analytic models, methods and tools to analyze the data; and (4) formulate the respective analytical models effectively and employ the selected and tools competently; and (5) provide concisely articulated data-supported answers to the questions formulated. There are three types of analytics methods: descriptive to investigate and understand the data; predictive to use some of the data to make predictions of outcomes; and prescriptive to develop models to help managers decide on course of actions. We expect the graduates of the program to have developed substantial expertise with this process. What specific evidence exists that students would be interested in this program? Analytics is rapidly becoming a very popular are of interest to students. Schools like George Washington University are reporting rapidly increasing enrollments with minimal promotional work. Because there is such a high demand for and short supply of analytics professionals, analytics has become a hot trend in business education. Furthermore, some students are discovering that some of the top consulting firms will not consider candidates who lack sound analytical education. This was confirmed at a meeting we held with representatives from Deloitte Consulting. Analytics is most definitely one of the hottest trends in business today, and students are getting the message.

For example, on April 9 th of 2012, the IT Department and the Kogod Consulting Club inaugurated an annual panel series entitled Hot Trends in Consulting. The first panel topic was Big Data and Analytics and the 3-hour was event sponsored by Computer Sciences Corporation. The event was entirely run by students and organized by the Club s leaders. 132 students signed the attendance sheets provided at the entrance to the panel session (many more attended but did not sign the attendance sheet). Many of the students who attended this panel event were undergraduates. The following semester we launched our first undergraduate business analytics course ITEC-320 as an elective and 19 students registered for the course. Some of these students are reporting that they have developed a great appreciation for the field of analytics and plan to pursue specialized graduate studies in analytics at other institutions, but without exception they have stated that they would stay at Kogod if we were to offer that degree. We have heard similar accounts from some of our graduate students. ITEC-320 is the only elective course in the Information Systems and Technology concentration that is offered both in the Fall and the Spring semesters. The enrollments for the Spring semester have increased to 26 and we expect this course will continue to grow in popularity. Because the Accounting Department has selected ITEC-320 as an optional requirement, we expect the enrollments in ITEC-320 to continue to increase. Similarly, there seems to be an increasing interest among the AU student population outside of Kogod that are very interested in analytics, as reported by department chairs of other academic units. These trends provide us with convincing evidence that there will be a strong demand for the program. Because the program is partly designed to serve the internal AU audience by having the flexibility to concentrate in a variety of functional specializations, we anticipate that this will be a very popular program. Furthermore, because of its modular design, it lends itself to the stackable certificate format, which could be marketed to the continuing education market.

What is the strategic marketing plan for the proposed program? In marketing and recruitment efforts, what segments of the student market will be targeted using focused messages? The flexible design of our program will allow us to target several market segments for potential candidates to the program. The combination of a strong analytics core combined with a functional specialization will make the program attractive to professionals who have functional industry knowledge but lack a background in analytics. Recent evidence suggests that there is a growing external audience of young professionals aspiring to managerial ranks who are candidates for such a program. Additionally, the MS in Analytics should be an attractive option for AU students wishing to pursue a combined BS/MS program. The market for graduate business students is highly competitive, both locally and nationally (and online). But, as discussed, analytics programs are becoming more of the norm and most business schools are adopting analytics programs under various related specializations (e.g., business analytics, data analytics, big data, data science, etc.) We will seek to distinguish the MS Business Analytics (from local competitors in particular) by refining the target audience as much as possible. The primary external target market is aspiring managers and functional experts who need to be savvy consumers of analytics reports for decision-making. Given the functional background areas of the degree, we could further segment to the following industries: financial analysts; forensic accountants (or general accountants); IT consultants; marketers. As we incorporate additional functional specializations in areas like criminology, international affairs and statistics, we will focus on these market segments too. Within Kogod, we have strong faculty relationships with local consulting, law, and accounting firms that might prove advantageous. Indeed, the prospective students ability to complete this degree part-time would be hugely beneficial in opening the degree to a wider net of prospective students. Based on similar prior Kogod program rollouts (e.g. MS Marketing), the projected level of investment over AY2014-2015 would be roughly $60-75K in paid media/production expenses (advertising [digital and other], printed materials, signage, etc.) and considerable staff time. The marketing strategy, at least initially, is to target a variety of audiences. Because this program is meant to be a good complement to various functional specializations at various levels of education, we anticipate that having a mixed audience will help better define the most effective market. The external target audience are the young professionals who have functional industry knowledge, but lack a background in analytics. Internally, we also anticipate growing interest from students interested in the combined BS/MS program. The main competitor we will need to distinguish ourselves from is GWU s MS in Business Analytics degree program. The GW program touts an advisory board with

representatives from IBM, Deloitte, and PWC corporations with existing Kogod relationships also (including our ITEC council). However, GW seeks students with strong quantitative backgrounds, which might provide an opening for Kogod to market to prospects with less rigorous mathematical backgrounds but stronger managerial orientation. The first challenge will be to introduce the degree broadly to the market, which will require faculty involvement in pitching the degree through media outlets and other appropriate channels. Public relations tools will be heavily leveraged. We may be able to use the burgeoning in-company affinity alumni groups, including at Ernst & Young, Freddie Mac, Deloitte, IBM and CSC to help us identify potential peerto-peer students. We also recommend drawing on the Information Technology Executive Council as influencers and spokespersons in this space. Targeted online network media buys, which allow for demographic refinement, may be a useful tool. And clearly a strong Search Engine Optimization strategy will be of utmost importance for people who are seeking a degree with these characteristics. C. Resources available to support this program Which current faculty members (specify whether full-time, temporary, or part-time) will be available to support this program? Provide a brief statement of the contributions expected from each faculty member and his or her qualifications for providing this contribution. While we plan to hire new faculty with expertise in Analytics, we are fortunate that several of our current faculty are qualified to teach in the program. Because the program is largely based on existing courses, the same faculty can continue to teach those courses and we will need to add new faculty, only to meet additional demand. The following tenure line and term faculty currently teach courses or are qualified to teach courses in the MS in Analytics curriculum Dr. Ed Wasil Teaches or has taught ITEC 610 Managerial Statistics and is our leading scholar in the quantitative and management sciences. Dr. Wasil is also designing ITEC 620 Business Insights from Analytics, which is a required course in the MBA program. Dr. Itir Karaesmen is a leading scholar in the quantitative and management sciences, with specialization on operations management. She is qualified to teach analytics, with particular expertise in prescriptive analytics and decision modeling. Dr. Frank Armour is the co-designer of ITEC 320 Business Analytics and the current instructor for that course. Dr. Armour also runs a full-day tutorial and a minitrack on big data and analytics and one the most prestigious conferences in Information Systems (HICSS). Dr. Armour is also the current instructor for ITEC 470/670 Database and Big Data

Dr. Alberto Espinosa is the other co-designer of ITEC 320 and a leading architect of the MS in Analytics program. He also runs the full-day tutorial above in collaboration with Dr. Armour and two other external scholars. Because of his extensive experience in quantitative and statistical methods for research, he is ideally suited to design and develop and teach ITEC 621 Predictive Analytics Dr. Rick Gibson is one of our most versatile and popular teachers in the school. Prof. Gibson teaches ITEC 610 Managerial Statistics and is qualified to teach analytics. He is also our leading expert on Microsoft tools and has taught ITEC 601 IT Tools for Managers, ITEC 470 Database and ITEC 617 Management Information Systems. Dr. William DeLone is the current instructor for ITEC 460/660 Business Intelligence and ITEC 616 Management Information Systems and has experience teaching IT tools to undergraduate students. Dr. Gwanhoo Lee because of his extensive experience with quantitative methods for research and experience teaching Management Information Systems and IT Tools for undergraduates, he is qualified to teach ITEC 601 IT Tools for Managers and 616 Management Information Systems. Dr. Erran Carmel is another current instructor of ITEC 616. He is also qualified to teach ITEC 601 IT Tools for Managers and 616 Management Information Systems. Ms. Jill Klein is another current instructor of ITEC 616 and is qualified to teach ITEC 601 IT Tools for Managers. In addition, the professors teaching the various functional specialization and functional background courses will be the same professors that already teach these courses for current programs. Also, Drs. Mark Clark (Management Department) and Alberto Espinosa (IT Department) have developed substantial expertise with social network analysis methods through their research and are planning to co-develop the ITEC course on social network analysis. Similarly, Prof. Gwanhoo Lee is also qualified to co-develop and teach a similar course with a focus on social media, which is one of his areas of expertise. Does the academic unit anticipate that there will be a need for new faculty resources to support new courses, supervision of internships, or other academic activities related to this proposal? If so, explain when and what faculty resources are anticipated for this program. The IT Department is already planning to recruit a full-time term faculty with specialization in analytics, partly to meet the demand for courses in this area. Again, because we plan to add only two new courses to the required curriculum, the need for additional faculty beyond this new faculty line will be minimal. Future recruitment of analytics faculty will only be necessary to meet demand if enrollment levels dictate the need for multiple sections.

The 12-credit specializations listed in the proposal are from Kogod and other academic units and are all based on existing courses or certificates. The only exception is ITEC-622 Organizational and Social Network Analytics, which is a course we plan to offer regardless of this MS program (but is included in the MS proposal). In our discussions with other AU academic units we are emphasizing the need to lean on existing courses, so that enrollment in those courses is not critically dependent on enrollments in the MS in Analytics program. What other staff (e.g., secretaries, graduate assistants) currently available to the teaching unit will be used to support this program. This program will need the same level of administrative support that other MS programs require, namely a program director and general administrative and marketing/communication and enrollments support. Will other new hires (full-time staff, part-time staff, graduate assistants, etc) be needed? If so, provide details. No new support staff is anticipated at this time. Does the academic unit anticipate that there will be a need for special facilities or equipment beyond what is currently available to the teaching unit that will be used to support this program? If so, explain when and what resources are anticipated. No. It is worth noting that Kogod is in the process of adopting a student laptop requirement policy. When and if this policy is implemented, the need for special facilities will be dramatically reduced. Are current computer service facilities sufficient to support this program? If not, explain what additional support will be needed. Yes What currently available classroom space will be used for this program and is it anticipated that additional classroom space will be needed? If so, provide details and a timeline. Because most courses in this program are already in the catalog, the need for additional class space will be minimal. More classroom space will only be needed if enrollments grow substantially. Will the new program require internships? If so, will it require additional resources beyond that which is available for internship opportunities? Explain.

None specific to the program, other than the typical internships Kogod students participate in. However, the program will require partnerships with consulting firms and organizations to implement the Analytics Practicum. D. Implementation plans In which year will students be able to declare or apply for this major? Fall 2015 Provide a timeline of the implementation program from when it begins until implementation is complete. Implementation work and recruitment will begin in the Summer of 2014. Curriculum and course development will take place in Fall 2014 and program logistics will be finalized by December 2014. E. Enrollment projections If this is an undergraduate program, explain how enrollments for this new program will affect enrollments within your teaching unit, academic unit, and university enrollment. Will it result in additional enrollments or will there be a realignment of existing enrollments in other areas? Not applicable. Estimate the period of study (in semesters) required to complete each major in the degree program for both full-time and part-time students. Not applicable. Over the first five years of each major in the degree program, estimate the number of students (provide separate estimates of full-time and part-time students) who will be actively taking courses each year. Academic Year Undergrad Students Seeking +1 MS degree Students from Current Kogod MS Programs AU Students outside of Kogod Externally Recruited Students Total Number of Students AY 2014-15 5 5 5 6 21 AY 2015-16 6 5 6 8 25 AY 2016-17 8 5 8 12 33 AY 2017-18 10 6 10 15 41 AY 2018-19 10 6 10 20 46

What is the timing and sequencing for all required and elective courses. What will be done if enrollments exceed projections? See curriculum overview above. In the short-term, we anticipate that demand for this program will be met by existing capacity in current classes. However, as enrollment increases in this program we will have to offer more sections of the required classes in the program. F. Financial considerations On the basis of the information provided above, itemize the cost of implementing this degree program including the costs associated with the possibility of having additional sections. Faculty There will not be a need for additional faculty resources beyond what is currently planned, unless graduate enrollments expand to require additional sections. If that is the case, there are a number of highly qualified adjuncts with advanced degrees currently working on analytics and in consulting firms who could teach one or more of the courses in the MS in Analytics program. Costs would include hiring one or more adjunct faculty at that time based on student demand. In that case, student revenues would more than offset the additional adjunct faculty costs. Kogod Marketing and Web Page Development The MS in Analytics is part of a strategic package of courses into various modules. As a portfolio, it is critical that marketing strategies, Kogod web pages and resources be sufficient and be focused and targeted towards the diverse audiences for these programs. Highly visible and impactful web pages (including short videos from past consulting students, recruiters, alumni in consulting firms, etc.) along with web optimization strategies to direct searchers to the appropriate site will be critical components of the marketing/student recruitment overall strategy. Program Leadership The MS in Analytics and the related stackable modules are part of a strategic customizable package. This will require a program director to coordinate recruitment, marketing efforts, integration of functional specializations into the program, course scheduling and staffing. How do you plan to obtain the funds required to support implementation of this degree program? Funds can be provided by KSB from net new tuition revenues generated by additional graduate enrollments. What other financial considerations should be taken into account in deciding whether to implement this degree program?

None at this time. What are the long-range financial considerations associated with this degree program? The MS in Analytics is part of a strategic, flexible, modular package of courses that could also be implemented as stackable certificates and for weekend executive programs. It is anticipated, that over time the various combinations of modules and functional specializations will add significant enrollments. In the long run, the MS in Analytics will open up some very interesting options of building additional graduate degree programs or serving as an add on to existing degree programs of multiple areas across campus. These programs will further build interdisciplinary strengths for American University that would set AU s programs apart from other universities. G. Program Assessment Note: The major teaching unit will assess the program on a yearly basis. At the end of the first five years, the major teaching unit shall present an assessment of learning outcomes to the Faculty Senate Assessment Committee. What are the learning outcomes including the competencies that students are expected to demonstrate for each major in the degree program? MS in Analytics: The goal of the M.S. in Analytics program is to provide students with the knowledge and competencies to help organizations make decisions and solve problems based on data, in order to prepare student to work as savvy analytical managers or consultants. LO1: Functional Competencies, Demonstrate functional analytics knowledge, including knowledge of: analytics, management information systems, applied managerial statistics, and database and big data analysis. LO2: Analytical Problem Solving, Demonstrate analytical problem solving through data management and analytics tools, business intelligence, and predictive and prescriptive analysis. LO3: Communication, Develop professional competence in written communication by succinctly articulating problem statements and findings to answer the questions formulated. LO4: Applied Knowledge, Demonstrate the ability to (1) formulate a relevant business question or hypothesis; (2) search, identify and manipulate the necessary data to answer that question; (3) evaluate and select the most appropriate analytic models, methods and

tools to analyze the data; and (4) formulate the respective model effectively and apply the selected methods and tools competently. What qualitative criteria and evidence should be used to assess learning outcomes for students who complete each major? A qualitative evaluation of the students, used to evaluate LO3 and LO4, will occur during the applied practicum and will be conducted by three independent evaluators: (1) the professor supervising the practicum; (2) another Kogod professor not directly associated with the MS program; and more importantly (3) the team s external client for the analytics practicum project. Using an evaluation rubric, the independent evaluators will assess each student on their ability to: (1) formulate a relevant business question or hypothesis; (2) search and identify the necessary data sources to answer that question; (3) evaluate and select the most appropriate analytic models, methods and tools to analyze the data; and (4) formulate the respective model effectively and employ the selected and tools competently (LO4) and succinctly articulate findings in a written report (LO3). The evaluator scores will be combined to determine an average score for each student on each of the five dimensions described above. Consistent with the KSB Curriculum Assessment Committee standards, the assessment target is: 80% of students or better achieve a 60% or better on the practicum. What quantitative criteria and evidence should be used to assess learning outcomes for students who complete this degree program? Quantitative evidence will be obtained three times during the program in order to assess learning outcomes. First, in KSB 065, students will participate in an online prep course and take a competency exam on the functional knowledge covered in this course (LO1). Consistent with the KSB Curriculum Assessment Committee standards, students must achieve an 80% better on this exam. Second, exam questions, given in ITEC 620 and ITEC 621, will assess students functional knowledge (LO1) and analytical problem solving ability (LO2). Again, consistent with the KSB Curriculum Assessment Committee standards, the assessment target is: 80% of students or better achieve a 60% or better on the practicum. Provide some examples of methods you plan to use to assess learning outcomes for each major. The methods used to assess the learning outcomes include: (1) an assessment of student performance on applied practicum conducted by three independent evaluators; (2) an online competency exam; and (3) conceptual and analytical problems obtained from course exams.

In the case of doctoral programs, the University Senate Graduate Studies Committee requires a formal review and written evaluation of a doctoral degree program proposal by a team of qualified outside evaluators at the time that the proposal is submitted to the committee. Please consult with the chair of that committee to make the necessary arrangements. Not applicable. H. Catalog copy Please attach a description of the proposed degree program as it is to appear in the University Catalog, following the format of the current catalog.

CATALOG COPY FOR THE MS IN ANALYTICS PROGRAM MS in Analytics The M.S. in Analytics program provides students with an opportunity to obtain knowledge and competencies to help organizations make decisions and solve problems based on data. Analytics is about extracting meaning out of data and this program trains the student to do this in order to answer specific business questions and make sound decisions. Upon completion of the program, students will have the knowledge to: formulate business questions that can be answered with data; identify, acquire and prepare the necessary data for the analysis; select the most appropriate methods and tools for the analysis; develop the appropriate descriptive, predictive and prescriptive analytic models; and provide concise conclusions in response to the business question. Students who successfully complete the proposed MS in Analytics will be prepared to work as savvy analytical managers or consultants. Admission to the Program In addition to meeting the minimum university requirements for graduate study, applicants must have earned an undergraduate bachelor s degree from an accredited institution with a satisfactory grade point average. In addition, applicants must have earned a satisfactory grade point average for the last 60 credit hours of academic work from a Council of Postsecondary Accreditation (COPA) regionally accredited institution. Applicants whose first language is not English must have a satisfactory score on the Test of English as a Foreign Language (TOEFL). Application and admission requirements for Kogod s masters of science programs are described at http://www.american.edu/kogod/admissions/application_guide.cfm. As articulated in these guides, valid GMAT or GRE scores will be required to be considered for admission, with no particular preference for a particular exam. Degree Requirements Thirty three (33) credit hours of approved graduate coursework including 9 credit hours of required core competencies courses, 9 credits of required analytics foundational courses, 12 credits from a selected set of functional specialization courses, or alternatively, 12 credits from a selection of business background courses, plus 3 credits of an analytics practicum (no-lecture, project-based) course working on a project with a real organization. Course Requirements Core Competency Courses (9 credit hours) KSB 065 Analytics Readiness (0) (competency exam and prep course online) ITEC 616 Management Information Systems (3) ITEC 610 Applied Managerial Statistics (3) ITEC 670 Database and Big Data (3)

Analytics Core Courses (9 credit hours) ITEC 620 Business Insights Through Analytics (3) ITEC 621 Predictive Analysis (3) ITEC 660 Business Intelligence (3) Capstone Experience (3 credit hours) KSB 620 Analytics Practicum (1.5 credits) (Taken twice for total of 3 credit hours) Elective Courses (12 credit hours) In addition to the Core Competencies, Analytics Core, and capstone, students must complete 12 credits hours of approved graduate elective courses. Students should complete either a Functional Specialization or a Functional Background from those listed below. Functional Specialization Analytics is never conducted in the vacuum, but in the context of a functional domain (e.g., marketing, accounting forensics, quantitative financial analysis, etc. This program offers two alternatives for further education in various functional areas: functional specialization and functional background. For students who may not have work experience in a particular functional area, this program offers a number of 12 credit course groups in various functional specifications. Accounting Forensics (12 credit hours) 9 credit hours: ACCT 551 Forensic Accounting (3) ACCT 677 Financial Statement Analysis (3) ACCT 680 Advanced Forensic Accounting & Fraud Detection (3) Plus 3 credits from: o ACCT 549 Contemporary Assurance & Audit Services (3) o ACCT 550 Accounting Information Systems (3) o ACCT 600 Ethics in Business & Accounting (3) o ACCT 607 Financial Accounting (3) o ACCT 760 Advanced Auditing & Professional Practice (3) o FIN 614 Financial Management (3) o FIN 630 Financial Analysis of the Firm: Concepts and Applications (3) Quantitative Financial Analysis I (12 credit hours) This program offers two options for quantitative financial analysis specialization, depending on the student s financial background and experience, as approved by the Finance Department chair. For, quantitative Financial Analysis I: FIN 605 Managerial Economics (3) FIN 614 Financial Management (3)

FIN 660 Financial Modeling (3) FIN 574 Quantitative Methods in Finance (3) Quantitative Financial Analysis II (12 credit hours) FIN 614 Financial Management (3) FIN 660 Financial Modeling (3) FIN 665 Quantitative Methods in Finance I (3) FIN 666 Quantitative Methods in Finance II (3) IT Consulting (12 credit hours) ITEC 643 Project Management (3) ITEC 630 Business Process Analysis (3) ITEC 666 Cyber Security Risk Management (3) ITEC 622/MGMT 622 Organizational and Social Network Analytics (3); Marketing (12 credit hours) MKTG 561 Customer Relationship Management and Database Marketing (3) MKTG 612 Marketing Management (3) MKTG 741 Digital Marketing (1.5) MKTG 755 Applied Market Segmentation (1.5) MKTG 767 Research for Marketing Decisions (3) Computational Statistics (12 credit hours) STAT 515 Regression (3) STAT 524 Data Analysis (3) STAT 525 Statistical Software (3) MATH 460/660 Tools of Scientific Computing (3) Bio Statistics (Choose 12 credit hours) 9 credit hours: STAT 515 Regression (3) STAT 520 Applied Multivariate Analysis (3) STAT 521 Analysis of Categorical Data (3) Plus 3 credit hours from: STAT 510 Survey Sampling (3) STAT 524 Data Analysis (3) STAT xxx Advanced Biostatics (3) [currently an experimental course]

Any suitable 12 credit specialization certificate from AU academic units outside of Kogod, as approved by the respective academic unit, the Program Director, the EPC and the Kogod Council Functional Background Analytics is never conducted in the vacuum, but in the context of a functional domain (e.g., marketing, accounting forensics, quantitative financial analysis, etc. This program offers two alternatives for further education in various functional areas: functional specialization and functional background. For students who may already have work experience the focal functional area, this program offers 12 elective credits from various functional specifications and other approved business graduate courses, either from the Business Fundamentals curriculum from the MS in Management program or any other business graduate courses, Choose 12 credits from: Any of the functional specialization courses listed above Business Fundamentals courses from the MS in Management curriculum Or other Kogod graduate coursework as approved by the Program Director or the respective Department Chair; or Any suitable set of 12 credit analytical course electives from AU academic units outside of Kogod, as approved by the respective academic unit, the Program Director, the EPC and the Kogod Council I. If applicable, submit new course proposals separately. See attached EPC proposals for: Capstone Experience: KSB-620 Analytics Practicum (3 cr. @ 1.5 per semester) core requirement ITEC-621 Predictive Analytics (3 cr.) core requirement ITEC-622/ MGMT-622 Organizational and Social Network Analytics ( (3 cr.) for functional specialization

New Course Proposal: Capstone Experience: KSB-620 Analytics Practicum Academic Unit: Teaching Unit: Course Title: Course Number : Credit Hours: Proposed effective date: Prerequisites: KSB KSB Analytics Practicum KSB-620 3 (over 2 semesterss at 1.5 per semester) Fall 2015 Enrollment in the MS in Analytics program Course description for University Catalog: Students are introduced to descriptive, predictive, and prescriptive analytics and to models, tools, and methods that are commonly used in each area to develop multi-disciplinary business insights from data. They develop skills that will enable them to present solutionss to problems and provide answers to business questions in various business discipliness through hands-on exercisess and a term project. This course emphasizes puts into practice all the analytics concepts covered in the various courses in the program. This is a practicum course and, as such, has no lectures. Grade type: (choose one) X A/F o Pass/ /Fail o A/F and Pass/Fail Expected frequency of offering: (choose one) o Every Fall or Every Spring, depending onn revised FT MBA curriculum Check all thatt apply: o General Education course o Online course o Hybrid course o Rotating topics course o Individually supervised course such as Internship, Independent Study, Research o Course, Thesis, Dissertation o Research methods course o AU Abroad Program course

o Other study abroad course (offered directly by Academic Unit, not through AU Abroad) Please explain the main purpose of the new course, including whether it will be a requirement for an existing or proposed program or an elective, and how the new course relates to the existing courses in the program and department. Note: if a required course for an existing program, submit a corresponding Minor Change to Program proposal. This course is proposed to be a required course for the MS in Analytics program, but it can also be an elective for other graduate programs The need for such a course in the core curriculum is explained below: On March 29 the Obama administration unveiled a $200 million R&D big data initiative in recognition of the fact that our ability to extract knowledge and meaning out of large and complex collections of digital data can help solve some of the most pressing national problems. According to a report by Gartner, business intelligence, analytics and performance measurement filter vast and growing amounts of information to reach insights and decisions in the digitized world, which is transforming industry after industry. A recent article by the NY Times discussed how Target revenues grew $23 billion in an eight year period in which they adopted predictive analytics practices to analyze customer purchase patterns and better estimate future sales of specific items. At a recent featured speaker series at the Kogod School of Business, Laura Evans, Chief Experience Officer at the Washington Post gave a talk titled The Future is Data: Decision-Making Shouldn t be Done Without It. In February of 2012, a NY Times article reported that the McKinsey Global Institute projected that the US needs 140,000 to 190,000 more workers with deep analytical expertise and 1.5 million more data-literate managers. A McKinsey 2011 quarterly report stated that large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. IBM reports that between 2005 and 2010 they invested over $14 billion in over 24 acquisitions to expand their analytics capabilities and projects $16 billion in related revenues by 2015. These and many other stories provide conclusive evidence that we have entered the age of analytics and big data. Organizations, whether commercial, non-for profit, institutional, government or educational need to embrace this trend or run the risk of irrelevance. Many universities are now implementing educational and research programs on analytics and big data in recognition that this is a key educational area for the foreseeable future. UVA s recent survey of 189 schools (339 professors) found that 59 of them offer programs on analytics, big data, or business intelligence. Also, most of these schools have increased offerings since 2010. The top skills identified by this study are about data and quantitative skills. They also found that students enroll in these classes because they find them interesting. Today s business professional must master analytics, big data, and business intelligence. We are already facing competition in this area in our local market. Both GW and GMU are ramping up substantial programs in these areas. We need to have strong course offerings in analytics or run the risk of losing relevance in the next few years.

There is widespread consensus that analytics professionals need to understand the functional domain in which analytics is conducted and do a substantial amount of experiential learning by doing. The proposed capstone experience course is designed precisely to provide this experience by assigning teams to experienced faculty who will identify organizations and develop projects for the students who will work in teams through two semesters. It is important to note that the ideal scenario is to have projects tightly associated with the functional domain of expertise. These project will be supervised as internships by faculty in the respective areas of functional expertise. For functional specializations and functional background electives in the MS program outside of Kogod, this course will be ideally supervised by a faculty from the respective academic unit. Will the course require that students pay a special fee associated with the course? If so, please provide a justification for this additional cost to students. None. Has the course previously been offered under a rotating topics course or an experimental course number? If so: No. This is a new course. o Semesters/year offered: o Course number: o Instructor: o Enrollment: o What observations and conclusions were derived from the previous offering(s) that now lead to proposing this course as a permanent part of the curriculum? Please indicate other units that offer courses or programs related to the proposed course and provide documentation of consultations with those units. There are no comparable courses on campus on Business Insights through Analytics or related topics in the graduate program. Estimated enrollment per semester 25 initially, increasing thereafter. Yes. Yes. Does your teaching unit s classroom space allotment support the addition of this course? Are present university facilities (library, technology) adequate for the proposed course? Will the proposed course be taught by full-time or part-time faculty? As a required course, this course may be taught by both full-time and part-time faculty.

No. Will offering the new course involve any substantial changes to the scheduling of existing courses? What are the learning outcomes including the competencies that students are expected to demonstrate for the course and how are those outcomes assessed? After completing this class, the student will have develop hands-on experience working on a real analytics project, solving a real problem specified by an organization. This practicum will also serve as a mechanism to conduct learning assessments in this class. The following will be used to assess the learning outcomes. Supervising instructor evaluation External organization representative evaluation Students and teams will need to demonstrate their ability to: (1) formulate an effective analysis question to be answered through this project; (2) identify, gather, manage, cleanse and manipulate the necessary data for the analysis; (3) select the most appropriate methods of analysis for the problem at hand; (4) articulate their data-driven solution to the problem deriving from the analysis. Please attach a draft syllabus. Syllabus is attached.

KSB 620 Capstone Experience: Analytics Practicum PROPOSED SYLLABUS Faculty Name: TBA Office Location: TBA Faculty e mail: TBA Phone: TBA Office Hours: TBA Preferred contact: TBA Class Time & Location: TBA Course description: Students are introduced to descriptive, predictive, and prescriptive analytics and to models, tools, and methods that are commonly used in each area to develop multi-disciplinary business insights from data. They develop skills that will enable them to present solutions to problems and provide answers to business questions in various business disciplines through hands-on exercises and a term project. This course emphasizes puts into practice all the analytics concepts covered in the various courses in the program. This is a practicum course and, as such, has no lectures. Prerequisites: Enrollment in the MS in Analytics program Learning objectives: After completing this class, the student will have develop hands-on experience working on a real analytics project, solving a real problem specified by an organization. This practicum will also serve as a mechanism to conduct learning assessments in this class. Textbook: None Required reading: None Additional Resources, Data, and Software: SAS Education Analytical Suite or equivalent commercial analytics software will be used. In addition, Microsoft Access and Microsoft Excel as well as add on features to Excel such as XLMiner, NodeXL, and Solver will be used. The course will include analysis of business cases; the case studies will be made available in a course pack, or via Blackboard. Important Date: Last day to drop course without penalty is TBA. Grading: Deliverable Weight Composition Deliverables TBD Team Final Project Report TBD Team Final Presentation TBD Team TOTAL 100%

Deliverables: 1. Assignments: There will be a specific schedule of deliverables for the two semesters, culminating in a final project report and client presentation 2. Team member participation: team members will be graded based on the overall team project and on their specific contribution to the project. Academic Integrity Code Academic integrity is paramount in higher education and essential to effective teaching and learning. As a professional school, the Kogod School of Business is committed to preparing our students and graduates to value the notion of integrity. In fact, no issue at American University is more serious or addressed with greater severity than a breach of academic integrity. Standards of academic conduct are governed by the University s Academic Integrity Code. By enrolling in the School and registering for this course, you acknowledge your familiarity with the Code and pledge to abide by it. All suspected violations of the Code will be immediately referred to the Office of the Dean. Disciplinary action, including failure for the course, suspension, or dismissal, may result. Additional information about the Code (i.e. acceptable forms of collaboration, definitions of plagiarism, use of sources including the Internet, and the adjudication process) can be found in a number of places including the University s Academic Regulations, Student Handbook, and website at <http://www.american.edu/academics/integrity>. If you have any questions about academic integrity issues or about standards of conduct in this course, please discuss them with your instructor. Academic Support Services If you experience difficulty in this course for any reason, please don t hesitate to consult with me. In addition to the resources of the department, a wide range of services is available to support you in your efforts to meet the course requirements. Academic Support Center (x3360, MGC 243) offers study skills workshops, individual instruction, tutor referrals, and services for students with learning disabilities. Writing support is available in the ASC Writing Lab or in the Writing Center, Battelle 228. Counseling Center (x3500, MGC 214) offers counseling and consultations regarding personal concerns, self help information, and connections to off campus mental health resources. Disability Support Services (x3315, MGC 206) offers technical and practical support and assistance with accommodations for students with physical, medical, or psychological disabilities. If you qualify for accommodations because of a disability, please notify me in a timely manner with a letter from the Academic Support Center or Disability Support Services so that we can make arrangements to address your needs. Kogod Center for Business Communications (x1920, KSB 101) To improve your writing, public speaking, and team assignments for this class, contact the Kogod Center for Business Communications. You can get advice for any written or oral assignment or for any type of business communication, including memos, reports, individual and team presentations, and

PowerPoint slides. Hours are flexible and include evenings. Go to http://www.kogod.american.edu/cbc and click on "make an appointment," visit KSB 101, or email cbc@american.edu. You may also call x1920. Financial Services and Information Technology Lab (FSIT) (x1904, KSB T51) to excel in your course work and to maximize your business information literacy in preparation for your chosen career paths, we strongly recommend to take advantage of all software applications, databases and workshops in the FSIT Lab. The FSIT Lab promotes action based learning through the use of real time market data and analytical tools used by business professionals in the market place. These include Bloomberg, Thomson Reuters, Argus Commercial Real Estate, Compustat, CRSP, @Risk etc. For more information, please check out the website at Kogod.american.edu/fsit/ or send us an email to fsitlab@american.edu. EMERGENCY PREPAREDNESS FOR DISRUPTION OF CLASSES In the event of an emergency, American University will implement a plan for meeting the needs of all members of the university community. Should the university be required to close for a period of time, we are committed to ensuring that all aspects of our educational programs will be delivered to our students. These may include altering and extending the duration of the traditional term schedule to complete essential instruction in the traditional format and/or use of distance instructional methods. Specific strategies will vary from class to class, depending on the format of the course and the timing of the emergency. Faculty will communicate classspecific information to students via AU e mail and Blackboard, while students must inform their faculty immediately of any absence. Students are responsible for checking their AU e mail regularly and keeping themselves informed of emergencies. In the event of an emergency, students should refer to the AU Student Portal, the AU Web site (http://www.american.edu/emergency/) and the AU information line at (202) 885 1100 for general university wide information, as well as contact their faculty and/or respective dean s office for course and school/ college specific information. COURSE OUTLINE Team formation Project assignment Meeting with instructor and client organization Formulating the analysis question Identifying, gathering, cleansing and manipulating the data Preparing the data for analysis Selecting the appropriate analytic methods Application of methods with the data collected Preparation of final report and presentation Final presentation to client organization

New Course Proposal: ITEC-621 Predictive Analytics Academic Unit: Teaching Unit: Course Title: Course Number : Credit Hours: Proposed effective date: Prerequisites: KSB KSB Predictive Analytics ITEC-621 3 Fall 2015 ITEC 610 Applied Managerial Statistics Course description for University Catalog: Analytics is the process of transforming dataa into insightt for making better decisions. There are three primary types of analytics: Descriptive, which examines historical data and identifies and reports historical patterns and trends; Predictive, which predicts outcomes and future trends from existing data to help discover new relationships; Prescriptive, which formulates and evaluates new ways for a business to operate. This course focuses on Predictive Analytics concepts not already covered in ITEC 620. Predictive analytics is off particular importance for business because it helps decision makers evaluate possible outcomes (e.g., revenues, profits, market share, probability of making a sale, probability of losing a client, etc.) based on other historical data predictors (e.g., marketing expenditures, quality assurance investments, saless force size, etc.). The process of analytics involves specifying a question, problem, or decision, and finding the right answers using data. The process begins with identifying the appropriate data sources (internal or external, data format), and the appropriate models, tools, and methods for analysis. In this course, students are introduced to predictive modeling methods, approaches and tools. Students develop skillss in predictive analytics that will allow them to: (1) develop and use advanced predictive analytics methods; (2) develop expertise in the use of popular tools and software for predictive analytics; (3) learn how to develop predictivee analytics questions, identify and select the most appropriate predictive analytics methodss and tools, apply these methods to answer the respectivee questions and presenting data-driven solutions. Grade type: (choose one) X A/F o Pass/ /Fail o A/F and Pass/Fail Expected frequency of offering: (choose one) o Every Fall or Every Spring, depending onn revised FT MBA curriculum

Check all that apply: o General Education course o Online course o Hybrid course o Rotating topics course o Individually supervised course such as Internship, Independent Study, Research o Course, Thesis, Dissertation o Research methods course o AU Abroad Program course o Other study abroad course (offered directly by Academic Unit, not through AU Abroad) Please explain the main purpose of the new course, including whether it will be a requirement for an existing or proposed program or an elective, and how the new course relates to the existing courses in the program and department. Note: if a required course for an existing program, submit a corresponding Minor Change to Program proposal. This course is proposed to be a required course for the MS in Analytics program, but it can also be an elective for other graduate programs The need for such a course in the core curriculum is explained below: On March 29 the Obama administration unveiled a $200 million R&D big data initiative in recognition of the fact that our ability to extract knowledge and meaning out of large and complex collections of digital data can help solve some of the most pressing national problems. According to a report by Gartner, business intelligence, analytics and performance measurement filter vast and growing amounts of information to reach insights and decisions in the digitized world, which is transforming industry after industry. A recent article by the NY Times discussed how Target revenues grew $23 billion in an eight year period in which they adopted predictive analytics practices to analyze customer purchase patterns and better estimate future sales of specific items. At a recent featured speaker series at the Kogod School of Business, Laura Evans, Chief Experience Officer at the Washington Post gave a talk titled The Future is Data: Decision-Making Shouldn t be Done Without It. In February of 2012, a NY Times article reported that the McKinsey Global Institute projected that the US needs 140,000 to 190,000 more workers with deep analytical expertise and 1.5 million more data-literate managers. A McKinsey 2011 quarterly report stated that large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. IBM reports that between 2005 and 2010 they invested over $14 billion in over 24 acquisitions to expand their analytics capabilities and projects $16 billion in related revenues by 2015. These and many other stories provide conclusive evidence that we have entered the age of analytics and big data. Organizations, whether commercial, non-for profit, institutional, government or educational need to embrace this trend or run the risk of irrelevance. Many universities are now implementing educational and research programs on analytics and big data

in recognition that this is a key educational area for the foreseeable future. UVA s recent survey of 189 schools (339 professors) found that 59 of them offer programs on analytics, big data, or business intelligence. Also, most of these schools have increased offerings since 2010. The top skills identified by this study are about data and quantitative skills. They also found that students enroll in these classes because they find them interesting. Today s business professional must master analytics, big data, and business intelligence. We are already facing competition in this area in our local market. Both GW and GMU are ramping up substantial programs in these areas. We need to have strong course offerings in analytics or run the risk of losing relevance in the next few years. The proposed Predictive Analytics course is an important component in analytics education and a good complement to ITEC 610 Applied Managerial Statistics and ITEC 620 Business Insights from Analytics, which provides a step into providing our students with knowledge and skills in managing, manipulating, and analyzing data to enhance decision making. Will the course require that students pay a special fee associated with the course? If so, please provide a justification for this additional cost to students. None. Has the course previously been offered under a rotating topics course or an experimental course number? If so: No. This is a new course. o Semesters/year offered: o Course number: o Instructor: o Enrollment: o What observations and conclusions were derived from the previous offering(s) that now lead to proposing this course as a permanent part of the curriculum? Please indicate other units that offer courses or programs related to the proposed course and provide documentation of consultations with those units. There are no comparable courses on campus that we are aware of. Estimated enrollment per semester 25 students, initially, increasing thereafter Yes. Yes. Does your teaching unit s classroom space allotment support the addition of this course? Are present university facilities (library, technology) adequate for the proposed course? Will the proposed course be taught by full-time or part-time faculty?

As a required course, this course may be taught by both full-time and part-time faculty. No. Will offering the new course involve any substantial changes to the scheduling of existing courses? What are the learning outcomes including the competencies that students are expected to demonstrate for the course and how are those outcomes assessed? After completing this class, the student will develop the following competencies. Competency-1: Predictive Analytics Methods 1. The student will apply specific statistical and regression analysis methods to identify new trends and patterns, uncover relationships, create forecasts, predict likelihoods, and test predictive hypotheses. 2. The student will develop and use multiple linear regression models to identify relationships among variables and/or for forecasting. Competency-2: Predictive Analytics Tools 1. The student will develop familiarity with popular tools and software used in industry for predictive analytics, such as SAS Enterprise Guide, SAS Enterprise Miner and IBM SPSS Modeler. 2. Because of the popularity of MS Excel in business, the students will learn how to run some of the models learned in the course using MS Excel functions and add-on tools. Competency-3: The Predictive Analytics Cycle 1. Analytics is not just about math, statistics and tools, but more importantly, about learning how to formulate business questions that can be answered through predictive analytics. The student will learn how to formulate such questions. 2. The student will also learn how to select the appropriate method for predictive analysis, and how to build effective predictive models. 3. Student will then learn how to search, identify, gather, cleanse, and manipulate the necessary data for the analysis. 4. Finally, students will learn how to evaluate the soundness and validity of their models and how to interpret and report on results for a management audience. The following will be used to assess the learning outcomes. Hands-on exercises Assignments In-class tests Course project Please attach a draft syllabus. Syllabus is attached.

SYLLABUS ITEC 621 Predictive Analytics (3 cr) Faculty Name: TBA Office Location: TBA Faculty e mail: TBA Phone: TBA Office Hours: TBA Preferred contact: TBA Class Time & Location: TBA Course description: Analytics is the process of transforming data into insight for making better decisions. It involves specifying a question, problem, or decision, and finding the right answers using data. The process begins with identifying the appropriate data sources (internal or external, data format), and the appropriate models, tools, and methods for analysis. There are three primary types of analytics: Descriptive, which examines historical data and identifies and reports historical patterns and trends; Predictive, which predicts outcomes and future trends from existing data to help discover new relationships; Prescriptive, which formulates and evaluates new ways for a business to operate. Predictive analytics is of particular importance for business because it helps decision makers evaluate possible outcomes (e.g., revenues, profits, market share, probability of making a sale, probability of losing a client, etc.) based on other historical data predictors (e.g., marketing expenditures, quality assurance investments, sales force size, etc.). In this course, students are introduced to models, tools, and methods that are commonly used in predictive analytics. Students develop skills in predictive analytics that will allow them to: (1) develop and use advanced predictive analytics methods; (2) develop expertise in the use of popular tools and software for predictive analytics; (3) learn how to develop predictive analytics questions, identify and select the most appropriate predictive analytics methods and tools, apply these methods to answer the respective questions and presenting data driven solutions. Prerequisites: ITEC 610 Learning objectives: After completing this class, the student will develop the following competencies. Competency 1: Predictive Analytics Methods 1. The student will apply specific statistical and regression analysis methods to identify new trends and patterns, uncover relationships, create forecasts, predict likelihoods, and test predictive hypotheses. 2. The student will develop and use multiple linear regression models to identify relationships among variables and/or for forecasting. Competency 2: Predictive Analytics Tools 1. The student will develop familiarity with popular tools and software used in industry for predictive analytics, such as SAS Enterprise Guide, SAS Enterprise Miner and IBM SPSS Modeler.

2. Because of the popularity of MS Excel in business, the students will learn how to run some of the models learned in the course using MS Excel functions and add on tools. Competency 3: The Predictive Analytics Cycle 1. Analytics is not just about math, statistics and tools, but more importantly, about learning how to formulate business questions that can be answered through predictive analytics. The student will learn how to formulate such questions. 2. The student will also learn how to select the appropriate method for predictive analysis, and how to build effective predictive models. 3. Student will then learn how to search, identify, gather, cleanse, and manipulate the necessary data for the analysis. 4. Finally, students will learn how to evaluate the soundness and validity of their models and how to interpret and report on results for a management audience. Textbook: A custom textbook will be used in this course. TBD Required reading: TBD Additional Resources, Data, and Software: The course work will include analysis of large data sets. These data sets will be available from the textbook publisher or will be available freely in the public domain. SAS Education Analytical Suite, SAS Enterprise Guide, SAS Enterprise Miner, SPSS Modeler, and similar commercial analytics software will be used. In addition, Microsoft Access and Microsoft Excel as well as addon features and tool packs, such as Data Analysis, Solver, and XLMiner. Important Date: Last day to drop course without penalty is TBA. Grading: Deliverable Weight Composition Assignments 30% (6 @ 5% eacj) Individual Exam 30% Individual Term project 25% Team Attendance, class exercises and 15% Individual Participation TOTAL 100% Grading Legend: A: 93 or above; A : 90 to less than 93; B+: 88 to less than 90; B: 83 to less than 88; B : 80 to less than 83; C+: 78 to less than 80; C: 73 to less than 78; C : 70 to less than 73; D: C : 60 to less than 70; F: less than 60.

Grade Components: 3. Assignments: There will be six individual assignments on descriptive and predictive analytics modeling. The assignments will involve use of the software. 4. Exam: There one in class exam towards the end of the semester. 5. Predictive Analytics Term Project: A team of students (optimally 3 or 4) will identify an organization and build models and methods to enhance data driven decision making in this organization. Students will identify potential use of predictive analytics, formulate the problem, identify the right sources of data, analyze data, and prescribe actions to improve not only the process of decision making but also the outcome of decisions. This project will be delivered in three phases: a project proposal; a mid term deliverable; and a final in class presentation and a written report. 6. Class attendance, exercises and participation: In class participation is measured by the ability of students to bring quality discussion into the class. This course is based on a model of active learning, with class discussions and exercises playing a central role. Students are expected to read the assigned material and to carefully prepare for all cases and exercises before coming to class and completing the required class exercises, when assigned. Students will be called upon to respond to faculty questions. Absence and lateness will reduce your participation grade. Academic Integrity Code Academic integrity is paramount in higher education and essential to effective teaching and learning. As a professional school, the Kogod School of Business is committed to preparing our students and graduates to value the notion of integrity. In fact, no issue at American University is more serious or addressed with greater severity than a breach of academic integrity. Standards of academic conduct are governed by the University s Academic Integrity Code. By enrolling in the School and registering for this course, you acknowledge your familiarity with the Code and pledge to abide by it. All suspected violations of the Code will be immediately referred to the Office of the Dean. Disciplinary action, including failure for the course, suspension, or dismissal, may result. Additional information about the Code (i.e. acceptable forms of collaboration, definitions of plagiarism, use of sources including the Internet, and the adjudication process) can be found in a number of places including the University s Academic Regulations, Student Handbook, and website at <http://www.american.edu/academics/integrity>. If you have any questions about academic integrity issues or about standards of conduct in this course, please discuss them with your instructor. Academic Support Services If you experience difficulty in this course for any reason, please don t hesitate to consult with me. In addition to the resources of the department, a wide range of services is available to support you in your efforts to meet the course requirements.

Academic Support Center (x3360, MGC 243) offers study skills workshops, individual instruction, tutor referrals, and services for students with learning disabilities. Writing support is available in the ASC Writing Lab or in the Writing Center, Battelle 228. Counseling Center (x3500, MGC 214) offers counseling and consultations regarding personal concerns, self help information, and connections to off campus mental health resources. Disability Support Services (x3315, MGC 206) offers technical and practical support and assistance with accommodations for students with physical, medical, or psychological disabilities. If you qualify for accommodations because of a disability, please notify me in a timely manner with a letter from the Academic Support Center or Disability Support Services so that we can make arrangements to address your needs. Kogod Center for Business Communications (x1920, KSB 101) To improve your writing, public speaking, and team assignments for this class, contact the Kogod Center for Business Communications. You can get advice for any written or oral assignment or for any type of business communication, including memos, reports, individual and team presentations, and PowerPoint slides. Hours are flexible and include evenings. Go to http://www.kogod.american.edu/cbc and click on "make an appointment," visit KSB 101, or email cbc@american.edu. You may also call x1920. Financial Services and Information Technology Lab (FSIT) (x1904, KSB T51) to excel in your course work and to maximize your business information literacy in preparation for your chosen career paths, we strongly recommend to take advantage of all software applications, databases and workshops in the FSIT Lab. The FSIT Lab promotes action based learning through the use of real time market data and analytical tools used by business professionals in the market place. These include Bloomberg, Thomson Reuters, Argus Commercial Real Estate, Compustat, CRSP, @Risk etc. For more information, please check out the website at Kogod.american.edu/fsit/ or send us an email to fsitlab@american.edu. EMERGENCY PREPAREDNESS FOR DISRUPTION OF CLASSES In the event of an emergency, American University will implement a plan for meeting the needs of all members of the university community. Should the university be required to close for a period of time, we are committed to ensuring that all aspects of our educational programs will be delivered to our students. These may include altering and extending the duration of the traditional term schedule to complete essential instruction in the traditional format and/or use of distance instructional methods. Specific strategies will vary from class to class, depending on the format of the course and the timing of the emergency. Faculty will communicate classspecific information to students via AU e mail and Blackboard, while students must inform their faculty immediately of any absence. Students are responsible for checking their AU e mail regularly and keeping themselves informed of emergencies. In the event of an emergency, students should refer to the AU Student Portal, the AU Web site (http://www.american.edu/emergency/) and the AU information line at (202) 885 1100 for general university wide information, as well as contact their faculty and/or respective dean s office for course and school/ college specific information.

COURSE OUTLINE Week 1 Week 2 Week 3 Week 4 Week 5 Week 6 Week 7 Predictive Analytics Overview Articles by Davenport and others Descriptive Analytics Overview Overview of variable types Data manipulation in SAS Enterprise Guide Correlation analysis review ANOVA review Factor analysis review Data mining review The Predictive Analytics cycle Formulating predictive analytics questions Identifying the necessary data Selecting the most appropriate model Selecting the most appropriate tool Building an effective model Interpreting results The analytics report Predictive Analytics Modeling Review Simple regression analysis review Ordinary Least Squares (OLS) assumptions Multiple OLS regression analysis review Logistic modeling review Forecasting review Overview of various models when OLS assumptions are not met Analyzing Survey Data Factor Analysis and Principal Components Reliability Analysis Measurement Error Building variables from survey items Concurrent and predictive validity analysis Discriminant analysis Interaction Models Interaction with binary variables Interaction of two continuous variables Introduction to Structural Equation Models (SEM) Conceptual foundations When to use OLS for SEM Partial Least Squares (PLS) models

Week 8 Non Linear Regression Models Variable transformations Log regressions Quadratic models Spline Regression Other non linear models Week 9 Exam Week 10 Introduction to Social Network Analysis Models Social Network Concepts 1 Mode and 2 Mode Networks Key network structural properties Visualization of networks Network clusters Quadratic Assignment Procedure (QAP) correlation Quadratic Assignment Procedure (QAP) regression Week 11 Putting in all together The Predictive Analytics cycle review Week 12 Week 13 Week 14 Week 15 Term project review Predictive Analytics with Unstructured Data and Text Mining Visual analytics for predictive modeling Term project presentations

New Course Proposal: ITEC-622/MGMT-622 Organizational andd Social Network Analytics Academic Unit: Teaching Unit: Course Title: Course Number : Credit Hours: Proposed effective date: Prerequisites: KSB KSB Organizational andd Social Network Analytics ITEC-622 cross listed with MGMT-622 3 Fall 2015 ITEC 610 Applied Managerial Statistics Course description for University Catalog: Analytics is the process of transforming dataa into insightt for making better decisions. There are three primary types of analytics: Descriptive, which examines historical data and identifies and reports historical patterns and trends; Predictive, which predicts outcomes and future trends from existing data to help discover new relationships; Prescriptive, which formulates and evaluates new ways for a business to operate. This course focuses aspects on descriptivee and predictive analytics using organizational and social network data, not already covered in ITEC 621. Social network theory and analysis have been around for several decades and have become a popular research paradigm in information technology, organizational and social science research. With the ncreasing popularity of social media,, these theories and methods have been applied and improved substantially for the analysis of social networks. The process of analytics involves specifying a question, problem, or decision, andd finding the right answers using data. The process begins with identifying the appropriate dataa sources (internal or external, data format), and the appropriate models, tools, and methodss for analysis. In this course, students are exposed more specifically to key social network theories, methods and tools that will allow them to: (1) develop and use advanced social network analytics methods; (2) develop expertise in the use of popular network analysis tools and software; (3) learn how to develop analytics questionss that can be answered with social network analysis concepts and methods, identify and select the most appropriate network analysiss methods and tools; and (4) apply these methods and tools to answer the respective questions and presenting data-driven solutions. Grade type: (choose one) X A/F o Pass/ /Fail o A/F and Pass/Fail Expected frequency of offering: (choose one)

o Every Fall or Every Spring, depending on revised FT MBA curriculum Check all that apply: o General Education course o Online course o Hybrid course o Rotating topics course o Individually supervised course such as Internship, Independent Study, Research o Course, Thesis, Dissertation o Research methods course o AU Abroad Program course o Other study abroad course (offered directly by Academic Unit, not through AU Abroad) Please explain the main purpose of the new course, including whether it will be a requirement for an existing or proposed program or an elective, and how the new course relates to the existing courses in the program and department. Note: if a required course for an existing program, submit a corresponding Minor Change to Program proposal. This course is proposed to be a required course for the functional specialization in IT Consuling in the MS in Analytics program, but it will also be an important elective for other graduate programs The need for such a course in the core curriculum is explained below: On March 29 the Obama administration unveiled a $200 million R&D big data initiative in recognition of the fact that our ability to extract knowledge and meaning out of large and complex collections of digital data can help solve some of the most pressing national problems. According to a report by Gartner, business intelligence, analytics and performance measurement filter vast and growing amounts of information to reach insights and decisions in the digitized world, which is transforming industry after industry. A recent article by the NY Times discussed how Target revenues grew $23 billion in an eight year period in which they adopted predictive analytics practices to analyze customer purchase patterns and better estimate future sales of specific items. At a recent featured speaker series at the Kogod School of Business, Laura Evans, Chief Experience Officer at the Washington Post gave a talk titled The Future is Data: Decision-Making Shouldn t be Done Without It. In February of 2012, a NY Times article reported that the McKinsey Global Institute projected that the US needs 140,000 to 190,000 more workers with deep analytical expertise and 1.5 million more data-literate managers. A McKinsey 2011 quarterly report stated that large-scale data gathering and analytics are quickly becoming a new frontier of competitive differentiation. IBM reports that between 2005 and 2010 they invested over $14 billion in over 24 acquisitions to expand their analytics capabilities and projects $16 billion in related revenues by 2015. These and many other stories provide conclusive evidence that we have entered the age of analytics and big data. Organizations, whether commercial, non-for profit, institutional,

government or educational need to embrace this trend or run the risk of irrelevance. Many universities are now implementing educational and research programs on analytics and big data in recognition that this is a key educational area for the foreseeable future. UVA s recent survey of 189 schools (339 professors) found that 59 of them offer programs on analytics, big data, or business intelligence. Also, most of these schools have increased offerings since 2010. The top skills identified by this study are about data and quantitative skills. They also found that students enroll in these classes because they find them interesting. Today s business professional must master analytics, big data, and business intelligence. We are already facing competition in this area in our local market. Both GW and GMU are ramping up substantial programs in these areas. We need to have strong course offerings in analytics or run the risk of losing relevance in the next few years. The proposed Predictive Analytics course is an important component in analytics education for the more specific analysis of social media and other organizational and social network data, and a good complement to ITEC 620 Business Insights from Analytics and ITEC 621 Predictive Analytics, which provides a step into providing our students with knowledge and skills in managing, manipulating, and analyzing data to enhance decision making. Will the course require that students pay a special fee associated with the course? If so, please provide a justification for this additional cost to students. None. Has the course previously been offered under a rotating topics course or an experimental course number? If so: No. This is a new course. o Semesters/year offered: o Course number: o Instructor: o Enrollment: o What observations and conclusions were derived from the previous offering(s) that now lead to proposing this course as a permanent part of the curriculum? Please indicate other units that offer courses or programs related to the proposed course and provide documentation of consultations with those units. There are no comparable courses on campus that we are aware of. Estimated enrollment per semester 25 students, initially, increasing thereafter Yes. Does your teaching unit s classroom space allotment support the addition of this course? Are present university facilities (library, technology) adequate for the proposed course?

Yes. Will the proposed course be taught by full-time or part-time faculty? As a required course, this course may be taught by both full-time and part-time faculty. No. Will offering the new course involve any substantial changes to the scheduling of existing courses? What are the learning outcomes including the competencies that students are expected to demonstrate for the course and how are those outcomes assessed? After completing this class, the student will develop the following competencies. Competency-1: Social Network Analysis Concepts 3. The student will learn key popular concepts employed when analyzing social networks, such as: centrality, clustering, cliques, hemophilia, ego networks, structure, weak ties, and structural holes, among others. Competency-2: Social Network Analysis Methods and Tools 3. The student will develop familiarity with popular social network analysis quantitative and visual concepts: nodes, relationships, graph theory, sociomatrices, sociograms, onemode and two-mode networks, paths, distances, geodesics, degrees of separation, paths, walks, etc. 4. The student will also develop familiarity with popular quantitative methods and tools for social network analysis, such as: cluster analysis, grouping analysis (e.g., cliques, etc.), quadratic assignment procedure (QAP) correlation and regression, and P1 regressions among other things. The student will learn how to use popular quantitative tools for social network analysis like UCINET. 5. Similarly, the student will also develop familiarity with popular visual methods and tools for social network analysis, such as: network visualization, layout algorithms, stepping through networks, visual modeling of network changes over time. The student will learn how to use popular visual tools for social network analysis like NetDraw and Pajek. The following will be used to assess the learning outcomes. Hands-on exercises Assignments In-class tests Course project Please attach a draft syllabus. Syllabus is attached.

SYLLABUS ITEC 622/MGMT 622 Organizational and Social Network Analytics (3 cr) Faculty Name: TBA Office Location: TBA Faculty e mail: TBA Phone: TBA Office Hours: TBA Preferred contact: TBA Class Time & Location: TBA Course description: Analytics is the process of transforming data into insight for making better decisions. There are three primary types of analytics: Descriptive, which examines historical data and identifies and reports historical patterns and trends; Predictive, which predicts outcomes and future trends from existing data to help discover new relationships; Prescriptive, which formulates and evaluates new ways for a business to operate. This course focuses aspects on descriptive and predictive analytics using organizational and social network data, not already covered in ITEC 621. Social network theory and analysis have been around for several decades and have become a popular research paradigm in information technology, organizational and social science research. With the increasing popularity of social media, these theories and methods have been applied and improved substantially for the analysis of social networks. The process of analytics involves specifying a question, problem, or decision, and finding the right answers using data. The process begins with identifying the appropriate data sources (internal or external, data format), and the appropriate models, tools, and methods for analysis. In this course, students are exposed more specifically to key social network theories, methods and tools that will allow them to: (1) develop and use advanced social network analytics methods; (2) develop expertise in the use of popular network analysis tools and software; (3) learn how to develop analytics questions that can be answered with social network analysis concepts and methods, identify and select the most appropriate network analysis methods and tools; and (4) apply these methods and tools to answer the respective questions and presenting data-driven solutions. Prerequisites: ITEC 610 Learning objectives: Competency-1: Social Network Analysis Concepts 1. The student will learn key popular concepts employed when analyzing social networks, such as: centrality, clustering, cliques, hemophilia, ego networks, structure, weak ties, and structural holes, among others. Competency-2: Social Network Analysis Methods and Tools 1. The student will develop familiarity with popular social network analysis quantitative and visual concepts: nodes, relationships, graph theory, sociomatrices, sociograms, onemode and two-mode networks, paths, distances, geodesics, degrees of separation, paths, walks, etc. 2. The student will also develop familiarity with popular quantitative methods and tools for social network analysis, such as: cluster analysis, grouping analysis (e.g., cliques, etc.), quadratic assignment procedure (QAP) correlation and regression, and P1 regressions

3. among other things. The student will learn how to use popular quantitative tools for social network analysis like UCINET. 4. Similarly, the student will also develop familiarity with popular visual methods and tools for social network analysis, such as: network visualization, layout algorithms, stepping through networks, visual modeling of network changes over time. The student will learn how to use popular visual tools for social network analysis like NetDraw and Pajek. Textbook: A custom textbook will be used in this course. TBD Required reading: TBD Additional Resources, Data, and Software: The course work will include analysis of large data sets. These data sets will be available from the textbook publisher or will be available freely in the public domain. UCINET, NetDraw, Pajek and similar commercial analytics software will be used. Important Date: Last day to drop course without penalty is TBA. Grading: Deliverable Weight Composition Assignments 30% (6 @ 5% eacj) Individual Exam 30% Individual Term project 25% Team Attendance, class exercises and 15% Individual Participation TOTAL 100% Grading Legend: A: 93 or above; A : 90 to less than 93; B+: 88 to less than 90; B: 83 to less than 88; B : 80 to less than 83; C+: 78 to less than 80; C: 73 to less than 78; C : 70 to less than 73; D: C : 60 to less than 70; F: less than 60. Grade Components: 7. Assignments: There will be six individual assignments on descriptive and predictive analytics modeling. The assignments will involve use of the software. 8. Exam: There one in class exam towards the end of the semester. 9. Network Analytics Term Project: A team of students (optimally 3 or 4) will identify an organization and build models and methods to enhance data driven decision making in this organization. Students will identify potential use of social network analysis, formulate the problem, identify the right sources of data, analyze data, and prescribe actions to improve not only the process of decision making but also the outcome of decisions. This project will

10. be delivered in three phases: a project proposal; a mid term deliverable; and a final in class presentation and a written report. 11. Class attendance, exercises and participation: In class participation is measured by the ability of students to bring quality discussion into the class. This course is based on a model of active learning, with class discussions and exercises playing a central role. Students are expected to read the assigned material and to carefully prepare for all cases and exercises before coming to class and completing the required class exercises, when assigned. Students will be called upon to respond to faculty questions. Absence and lateness will reduce your participation grade. Academic Integrity Code Academic integrity is paramount in higher education and essential to effective teaching and learning. As a professional school, the Kogod School of Business is committed to preparing our students and graduates to value the notion of integrity. In fact, no issue at American University is more serious or addressed with greater severity than a breach of academic integrity. Standards of academic conduct are governed by the University s Academic Integrity Code. By enrolling in the School and registering for this course, you acknowledge your familiarity with the Code and pledge to abide by it. All suspected violations of the Code will be immediately referred to the Office of the Dean. Disciplinary action, including failure for the course, suspension, or dismissal, may result. Additional information about the Code (i.e. acceptable forms of collaboration, definitions of plagiarism, use of sources including the Internet, and the adjudication process) can be found in a number of places including the University s Academic Regulations, Student Handbook, and website at <http://www.american.edu/academics/integrity>. If you have any questions about academic integrity issues or about standards of conduct in this course, please discuss them with your instructor. Academic Support Services If you experience difficulty in this course for any reason, please don t hesitate to consult with me. In addition to the resources of the department, a wide range of services is available to support you in your efforts to meet the course requirements. Academic Support Center (x3360, MGC 243) offers study skills workshops, individual instruction, tutor referrals, and services for students with learning disabilities. Writing support is available in the ASC Writing Lab or in the Writing Center, Battelle 228. Counseling Center (x3500, MGC 214) offers counseling and consultations regarding personal concerns, self help information, and connections to off campus mental health resources. Disability Support Services (x3315, MGC 206) offers technical and practical support and assistance with accommodations for students with physical, medical, or psychological disabilities. If you qualify for accommodations because of a disability, please notify me in a timely manner with a letter from the Academic Support Center or Disability Support Services so that we can make arrangements to address your needs.

Kogod Center for Business Communications (x1920, KSB 101) To improve your writing, public speaking, and team assignments for this class, contact the Kogod Center for Business Communications. You can get advice for any written or oral assignment or for any type of business communication, including memos, reports, individual and team presentations, and PowerPoint slides. Hours are flexible and include evenings. Go to http://www.kogod.american.edu/cbc and click on "make an appointment," visit KSB 101, or email cbc@american.edu. You may also call x1920. Financial Services and Information Technology Lab (FSIT) (x1904, KSB T51) to excel in your course work and to maximize your business information literacy in preparation for your chosen career paths, we strongly recommend to take advantage of all software applications, databases and workshops in the FSIT Lab. The FSIT Lab promotes action based learning through the use of real time market data and analytical tools used by business professionals in the market place. These include Bloomberg, Thomson Reuters, Argus Commercial Real Estate, Compustat, CRSP, @Risk etc. For more information, please check out the website at Kogod.american.edu/fsit/ or send us an email to fsitlab@american.edu. EMERGENCY PREPAREDNESS FOR DISRUPTION OF CLASSES In the event of an emergency, American University will implement a plan for meeting the needs of all members of the university community. Should the university be required to close for a period of time, we are committed to ensuring that all aspects of our educational programs will be delivered to our students. These may include altering and extending the duration of the traditional term schedule to complete essential instruction in the traditional format and/or use of distance instructional methods. Specific strategies will vary from class to class, depending on the format of the course and the timing of the emergency. Faculty will communicate classspecific information to students via AU e mail and Blackboard, while students must inform their faculty immediately of any absence. Students are responsible for checking their AU e mail regularly and keeping themselves informed of emergencies. In the event of an emergency, students should refer to the AU Student Portal, the AU Web site (http://www.american.edu/emergency/) and the AU information line at (202) 885 1100 for general university wide information, as well as contact their faculty and/or respective dean s office for course and school/ college specific information.

COURSE OUTLINE Week 1 Overview of Organizational and Social Networks Week 2 Week 3 4 Week 5 6 Introduction to Social Network Basic Concepts Key definitions and concepts (nodes, relationships, sociomatrices, sociograms, etc.) Communication and knowledge networks Computational modeling of networks Overview of key Social Network theories Self Interest Collective action Contagion Semantic Cognitive theories Homophily Proximity Social support Weak ties Structural holes Overview of Key Network Analysis Concepts Relations and attributes Sociometric analysis and graph theory Interpersonal configuration and cliques Total and partial networks Understanding relational data Ego and Socio centricity Sociograms Network components, cores and cliques Network structure Network agglomerations: clusters and cliques Interlocking networks Distance and space metrics Advances in network visualization Overview of social network analysis software Week 7 11 Quantitative network analysis methods Overview of quantitative methods Levels of analysis Network types: one mode and two mode Ego networks Nework data collection and formats

Week 12 Week 13 Week 14 Week 15 Exam Graph theoretic notation Sociometric notation Graphs and matrices Centrality and prestige Structural balance and clustering Cohesive sub groups, cores and cliques Affiliation analysis Structural equivalence analysis Principal components Quadratic assignment procedure (QAP) correlation Quadratic assignment procedure (QAP) regression P1 regressions Network visualization methods Term project review TBA Term project presentations