RUTGERS MBA/MS dual degree in Operations Research and Business Analytics (MBA-ORBA)

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1 MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick RUTGERS MBA/MS dual degree in Operatins Research and Business Analytics (MBA-ORBA) Summary The dual MBA/MS degree in Operatins Research and Business Analytics is a partnership between Rutgers Business Schl (RBS), the Rutgers Graduate Schl - New Brunswick Campus, and the Rutgers Center fr Operatins Research (RUTCOR). The prgram integrates management, finance, accunting, peratins and analytics. It is a sixty (60) credit dual-degree prgram cmpsed f tw cmplementary cmpnents. The first cmpnent is the Master f Business Administratin prgram at the Rutgers Business Schl. It allws the students wh cmplete the Master f Operatins Research prgram t als btain the MBA degree by cmpleting in additin the thirty (30) credit curriculum f the MBA required classes. It als allws the MBA students with a slid quantitative educatin t btain the MSc degree by cmpleting the Master f Operatins Research curriculum, as a cncentratin. The secnd cmpnent is the Master f Operatins Research degree with a business analytics cncentratin. It intends t prvide students wh have a slid quantitative educatin (i.e., students with engineering, science, mathematics, ecnmics r similar undergraduate educatin) with an MS degree that fcuses n analytical skills and an understanding f hw these are applied t business and management prblems. It is a thirty (30) credit degree. Students must cmplete the first cmpnent (MBA cre) befre they can start the secnd cmpnent (MS part) *. Mtivatin. There is a grwing need acrss many fields fr managers with bth MBA skills and gd quantitative training. Graduates f the Rutgers dual MBA/MS in Operatins Research and Business Analytics degree pssess an impressive assrtment f analytical and prblem-slving skills. The prgram s skills are in demand acrss a wide range f industries, including finance, pharmaceutical industry, and transprtatin. Graduates can expect t wrk in bth technical and managerial capacities n develpment f prjects fr analyzing and acting n business data. The US BLS frecasts that jbs in peratins research will grw by 22 percent ver the perid , much faster than the average jb grwth. These jbs require Masters and PhD degrees. The greatest grwth will be in the pharmaceutical manufacturing and the financial services industrial sectrs bth sectrs are clusters in the NYC metrplitan area. There will be a shrtage f talent necessary fr rganizatins t take advantage f big data. By 2018, the United States alne culd face a shrtage f 140,000 t 190,000 peple with deep analytical * This requirement des nt apply t students already enrlled in the RUTCOR MS, fr the 2011/2012 academic year

2 MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick skills, as well as 1.5 millin managers and analysts with the knw-hw t use the analysis f big data t make effective decisins. Admissin Requirements The deadline fr applicatin t the prgram and fr financial supprt fr the Fall semester (beginning September) is January 15th. This deadline may be waived fr qualified students, when this is pssible. Applicatins fr financial aid are accepted even int the summer mnths whenever supprt is still available. Freign students, n the ther hand, shuld be aware that the issue f I-20 and IAP-66 frms, that are needed t btain a visa, can cnsume cnsiderable time; they are advised t apply as sn as pssible. Prspective students shuld have aptitude fr cmputers and quantitatively-riented material. Applicants are expected t have a bachelr s degree and basic knwledge in calculus, statistics, and cmputer prgramming. We particularly welcme applicants with undergraduate degrees in engineering, cmputer science, mathematics, statistics, and related fields. Part-time students with gd credentials will be accepted. Students withut an adequate backgrund can take a number f undergraduate curses during their first year, and in sme cases will be admitted t cursewrk nly n a nn-matriculated basis, with actual admissin int the MBA-ORBA prgram depending n their perfrmance. Applicatins Prcess. Yu will need the fllwing: Essay n (1) f the 3 tpics belw. 1. Describe an ethical dilemma. 2. Discuss a team prject that yu were a part f that failed. 3. Discuss (3) prfessinal accmplishments that shw yu are a gd manager. Official transcripts Resume, and Three(3) letters f recmmendatin Tests: Applicants shuld take either the GRE r the GMAT test. We prefer applicants with a minimum GRE scre f 1100 (n the cmbined verbal and quantitative GREs) r a GMAT scre f 650. Hwever, admissin t the prgram is based n an verall appraisal f the applicant such as recmmendatin letters, scres, grades, as well ptential fr success in ur prgram. Freign applicants are required t have a minimum TOEFL scre f 110. Prspective students shuld apply t bth prgrams separately at: use cde :16:711 (Operatins Research) and must be accepted by bth prgram

3 MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick MBA/MS in Operatins Research - Business Analytics MBA Cmpnent In this case the 30 credits f the Master in Operatins Research prgram cunt twards the MBA degree. In additin, the students have t take the fllwing curses: Cre MBA Curses (19 Credits) 22:010:502 Accunting fr Managers 22:223:521 Managerial Ecnmic Analysis 22:373:623 Business Ethics & Sciety (1 credit/5 weeks) 22:390:522 Financial Management 22:620:540 Organizatin Behavir 22:630:550 Marketing fr Decisin Making 22:799:564 Operatins Analysis Fundatin MBA Curses (6 credits- each 2 credits unless nted) 22:198:504 Infrmatin Technlgy fr Managers 22:373:510 Business Cmmunicatins 22:373:531 Law and Legal Reasning 22:553:533 Internatinal Business 22:620:542 Strategic Management 22:960:575 Data Analysis and Decisin Making (3 cr) Capstne MBA Curse (1 Curse; 3 credits) 22:621:543 Integrated Business Applicatins 22:620:672 Urban Entrepreneurship & Ecn. Dev. 22:799:650 Supply Chain Mgt Industry Prject These curses are identical with the required part f the MBA curriculum at the Rutgers Business Schl ( )

4 MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick MBA/MS in Operatins Research - Business Analytics MS Cmpnent Curriculum The prgram curriculum has tw main parts: Six required curses: 16:711:614 Thery f Linear Optimizatin 16:711:517 Cmputatinal Methds f Operatins Research 16:711:550 Nnlinear Optimizatin 26:960:575 Intrductin t Prbability 26:960:577 Intrductin t Statistical Linear Mdels 26:960:580 Stchastic Prcesses At least fur f the fllwing electives: 16:711:465 Integer Prgramming 16:711:555 Stchastic Prgramming 16:711:557 Dynamic Prgramming 16:711:613 Game Thery 26:799:661 Stchastic Mdels fr Supply Chain Management 16:711:631 Financial Mathematics r 26:711:563 Stchastic Calculus fr Finance 26:960:576 Financial Time Series 26:198:622 Machine Learning 26:198:644 Data Mining r 16:711:514 Operatins Research Appraches in Data Mining Based n student s recrd, a required curse may be waived; in that case an elective curse can be substituted, s that the ttal number f credits earned equals 30. Other electives may be substituted fr the prgram s electives, upn apprval f the prgram s directr

5 MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick Brief MScCurse Descriptins Required Curses 1. 16:711:614 Thery f Linear Optimizatin. Cnvex sets, plyhedra, Farkas lemma, cannical frms, simplex algrithm, duality thery, revised simplex methd, primal-dual methds, cmplementary slackness therem, maximal flws, transprtatin prblems, 2-persn game thery. Students will have the chance t apply the methds t real life prblems. One f the aims f the curse will be t teach the students the path: frm real life prblem t abstractin, t mathematical frmulatin, t slving the mathematical prblem, t applying this slutin in the real life framewrk :711:517 Cmputatinal Methds f Operatins Research. The curse will be highly interactive with individual and grup assignments and with intensive cmputer practice. The students will be ffered varius prblems and prjects t wrk n during the semester. Sme f the prjects will invlve the use f certain sftware packages, while sme thers will require cding. In additin, each f the students will be required t slve hmewrk assignments, mstly prgramming tasks. Grading will be based n hmewrks and prjects. The curse cncentrates n OR mdeling and prblem slving with AMPL, a mathematical mdeling language. Additinally, elements f ther prgramming envirnments will be described, and a few assignments will be given, in particular, in PERL t realize basic data structures and cmbinatrial algrithms; in C++ t develp basic rutines, and interface with CPLEX and/r XPressMP; and n HTML, Javascript and CSS t develp hme pages and interactive webprjects :711:550 Nnlinear Optimizatin. Cnvex sets. Separatin. Cnes. Cnvex functins. Elements f subdifferential calculus. Tangent cnes. Metric regularity. Optimality cnditins. Lagrangian duality. The methd f steepest descent. Newtn s methd. Cnjugate gradient methds. Nngradient methds. Truncated Newtn s methd. Feasible directin methds. Penalty methds. Dual and augmented Lagrangian methds. Sequential quadratic prgramming. Interir pint methds. Intrductin t Nndifferentiable Optimizatin :960:575 Intrductin t Prbability. Fundatins f prbability. Discrete and cntinuus simple and multivariate prbability distributins; randm walks; generating functins; linear functins f randm variable; apprximate means and variances; exact methds f finding mments; limit therems; stchastic prcesses including immigratin-emigratin, simple queuing, renewal thery, Markv chains. Prerequisite: Undergraduate r master s-level curse in statistics :960:577 Intrductin t Statistical Linear Mdels. Linear mdels and their applicatin t empirical data. The general linear mdel; rdinary-least-squares estimatin; diagnstics, including departures frm underlying assumptins, detectin f utliners, effects f influential bservatins, and leverage; analysis f variance, including ne-way and tw-way layuts; analysis f cvariance; plynmial and interactin mdels; weighted-least squares and rbust estimatin; mdel fitting and validatin.emphasizes matrix frmulatins, cmputatinal aspects and use f standard cmputer packages such as SPSS :960:580 Stchastic Prcesses. The curse cvers the thery and mdeling f stchastic prcesses. Tpics include: martingales, stpping therems, elements f large deviatins thery, Renewal Thery, Markv Chains, Semi-Markv Chains, Markvian Decisin Prcesses. In additin, the class will cver sme applicatins t finance thery, insurance, queueing and inventry mdels

6 Electives MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick 1. 16:711:465 Integer Prgramming.Overview f discrete ptimizatin mdels ccurring in business, engineering, industry and the sciences Mdelling with integer variables Specially structured prblems:knapsack, cvering and partitining prblems A quick intrductin t cmplexity thery:prblems, instances, wrst-case cmplexity, plynmial algrithms, the classes P and NP Linear prgramming relaxatins, integrality fslutins, unimdularity and applicatins fr assignment prblems, shrtest path and netwrk cmputatins Enumerative methds:branchand-bund, implicit enumeratin, bunding techniques, Lagrangean and surrgate duality Cutting planes, Gmry's algrithm, lifting and prjecting fr binary ptimizatin Heuristics: greedy algrithms, lcal search, truncated expnential schemes :711:555 Stchastic Prgramming.Overview f statistical decisin principles.overview f stchastic prgramming mdel cnstructins: reliability type mdels, penalty type mdels, mixed mdels, static and dynamic type mdels. The simple recurse mdel and its numerical slutin techniques. Cnvexity thery f prbabilistic cnstrained mdels.bunding and apprximatin f prbabilities. Numerical slutin f prbabilistic cnstrained mdels. Tw-stage prgramming under uncertainty and the slutin f the relevant prblem by Benders' decmpsitin. Multistage stchastic prgramming mdels. Scenari aggregatin. Distributin thery f stchastic prgramming. Applicatins t prductin, inventry cntrl, water resurces, finance, pwer and cmmunicatin systems :711:557 Dynamic Prgramming.The shrtest path prblem.the principle f ptimality. Label crrecting algrithms. Cntrlled Markv chains. Finite hrizn stchastic prblems. Dynamic prgramming equatins. Discunted infinite hrizn prblems. Value and plicy iteratin methds. Linear prgramming apprach. Applicatins in inventry cntrl, scheduling, lgistics. The multiarmed bandit prblem. Undiscunted infinite hrizn prblems. Stchastic shrtest paths. Methds fr slving undiscunted prblems. Optimal stpping; asset pricing. Average cst prblems. Methds fr slving average cst prblems. Cntrlled cntinuus time Markv chains.intrductin t apprximate dynamic prgramming :711:613 Game Thery. Matrix games, max-min, min-max and saddle pint. Pure and mixed strategies. Slvability in mixed strategies. Vn Neumann's Therem fr matrix games. Bimatrix and n-matrix games. Nash equilibria and Nash slvability. Perfect equilibria and perfect slvability. Sphisticated equilibria and dminance slvability. Games in extensive, psitinal and nrmal frm. Perfect infrmatin and slvability in pure strategies. Nash slvability f the cyclic games. Dminatin and dminance slvability. Backward inductin. Dminance slvable extensive and secret vet vting schemes. Cperative games. Calitins. Transferable and nntransferable utilities, TU- and NTU-games. Cres and cre-slvability. Bndareva-Shapley's Therem and Scarf's Therem. Effectivity functins and game frms, Mulin-Peleg's Therem. Cperative games in effectivity functin frm, Keiding's Therem. Stable effectivity functins and stable families f calitins. Intrductin t Scial Chice Thery. Paradx Arrw. Scial chice functins and crrespndences. Blean functins and graphs in game thery: Blean duality and Nash slvability. Read-nce Blean functins, P4-free graphs and nrmal frm f the psitinal games with perfect infrmatin. Stable effectivity functins and Berge's perfect graphs. Stable families f calitins and nrmal hypergraphs. The Shapley value and the Banzhaf pwer index fr cperative games and apprximatin f pseud-blean functins :799:661 Stchastic Mdels fr Supply Chain Management. This curse cvers quantitative methds in supply chain management under uncertainty. The emphasis is n the fundatins f dynamic ptimizatin tls in stchastic inventry mdels. We study key cncepts such as Preservatin and Attainment, Mypic Plicies, ptimality f (s,s) plicies, capacitated inventry management, Bayesian Inventry Mdels, and Cntracts in Supply Chains, Manufacturer s Return Plicies and Retail Cmpetitin. Other tpics include: Supply Cntracts with Quantity

7 MBA/MS in Operatins Research and Business Analytics (MBA-ORBA) Rutgers Business Schl & Rutgers Graduate Schl f Arts and Sciences New Brunswick Cmmitment and Stchastic Demand. Optin Cntracts in Supply Chains. Cmpetitive and Cperative Inventry Plicies :711:631 Financial Mathematics.Cash flw streams. Financial instruments (stcks, bnds, futures, ptins, cash flws). Utility functins. Arbitrage pricing thery. Applicatin f martingales. Brwnian mtins. It's lemma. Black-Schles thery. Parablic PDEs and their numerical slutins. The Feynman-Kac slutin. Extic and path-dependent ptins (chser, barrier, lkback, Asian, Bermudan, etc.). Interest rate mdels (Vasicek, Hull-White). Shrt intrductin t stchastic prgramming mdels.markwitz mean-variance mdels. Bnd prtfli cmpsitin mdels. Term structures. The use f gal prgramming. Dynamic ptin selectin mdels. Value at Risk mdels :198:622 Machine Learning. Cnditinal prbability and Bayes therem. Intrductin t R. Intrductin t Bayesian thinking. Single-parameter mdels. Multiparameter mdels. Intrductin t Bayesian cmputatin. Markv Chain Mnte Carl. Hierarchical mdeling. Mdel cmparisn. Regressin mdels. Gibbs sampling. Cnfidence and exchangeability. Cnfrmal predictin :198:644 Data Mining. Intrductin t data mining tasks (classificatin, clustering, assciatin rules, sequential patterns, regressin, deviatin detectin). Data and preprcessing: data cleaning, feature selectin, dimensinality reductin. Classificatin: decisin-tree based apprach, rule-based apprach, instance-based classifiers. Bayesian apprach: naive and Bayesian netwrks, classificatin mdel evaluatin. Clustering: partitinal and hierarchical clustering methds, graph-based methds, density-based methds, cluster validatin. Assciatin analysis: a priri algrithm and its extensins, assciatin pattern evaluatin, sequential patterns and frequent subgraph mining. Anmaly detectin: statisticalbased and density-based methds :960:576 Financial Time Series. This curse cvers applied statistical methdlgies pertaining t time series, with emphasis n mdel building and accurate predictin. Cmpletin f this curse will prvide students with enugh insights and mdeling tls t analyze time series data in the business wrld. Students are expected t have basic wrking knwledge f prbability and statistics including linear regressin, estimatin and testing frm the applied perspective. We will use R thrughut the curse s prir knwledge f it is welcme, but nt required