Decision Sciences Department Business Analytics Program. Decision Sciences 6290: Introduction to Business Analytics (1.

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1 Decision Sciences Department Business Analytics Program Decision Sciences 6290: Introduction to Business Analytics (1.5 credit hours) Dr. Demirhan Yenigun Course Description The advancement in computing and information management technology created the opportunity for businesses to store, organize and analyze the vast amounts of their customer data. This course provides an introduction to database analytics concepts, methods and tools with concrete examples from industry applications. Students will learn the fundamentals of data analytics driven strategies in creating the leading edge Analytical Competitors in today s business environment. At the same time the course provides an introduction to the relatively more recent advancements in analytical methods on business data acquired through online channels, the new practice of Web analytics. Pre-Requisites None Course Objectives Upon completing this course, the students will be able to: 1. Understand why Business Analytics is a key competency essential for business success 2. Understand how to assess the Analytics competency of a Business Enterprise 3. Understand how businesses can organize, enhance and store their business data 4. Interpret and analyze web data to derive actionable customer intelligence. 5. Familiarize themselves with the most popular Web Analytics Tools in the Industry Assignments Reading of textbook and other assigned material, class notes and the completion of team project will be required. There will be 4 in-class quizzes during the mini semester.

2 Texts and Software Required Text Software Competing on Analytics, The New Science of Winning, Thomas H. Davenport & Jeanne G. Harris, Harvard Business School Press. This course will utilize various industry leading software tools that are being used for Database and Web Analytics applications. IBM Cognos, IBM Coremetrics, Adobe Omniture, Google Analytics and Google Web Optimizer will be utilized to demonstrate examples of various business applications. Team Project Students will have the opportunity to further sharpen their skills and acquire hands-on experience with practical database analytics problems through a team project. Students will form groups consisting of between 3 and 4 people depending upon the size of class. Each group will design a database analytical solution that will be applied to a specific business that operates in a specific industry. Each team will give a brief class presentation on the project during the 7 th week of classes. Grading (30%) Team Project (35%) Class quizzes (35%) Final Exam

3 Syllabus and Deliverables Session Date Subject/Topic Deliverable Due 1 Introduction to Business Analytics Business Data Overview: Sources and Uses of Business Data. BIG DATA 2 Competing on Analytics in today s Business Landscape. Assessing the Analytical Competency of a Business Enterprise 3 Data Warehouse Modeling: OLAP and Reporting. Data Cube Technology Quiz 1 Introduction to IBM Cognos 4 How Businesses can utilize their Data: Overview on Data Mining Techniques Quiz 2 5 Introduction to Web Analytics Part I : Web Data and Analysis of Online Behaviors, Web Analytic Tracking Tools: Google Quiz 3 Analytics, IBM Coremetrics and Omniture 6 Introduction to Web Analytics Part II Web Analytic Applications, Analytical Methods and Tools for Website Effectiveness Testing Google Optimizer Quiz 4 7 Team Project Presentations FINAL EXAM Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see

4 Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at For additional information refer to Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

5 PROJECT ASSIGNMENT You are appointed as the new members of the data analytics team for a company. The newly appointed CEO believes in the power of data analytics and wants to make a big impact to the company s competitive positioning and the bottom line. He/she asked your group to come up with a detailed plan that will transform the company into a data-driven analytic enterprise as outlined in our course textbook, Competing on Analytics, The New Science of Winning. He/she wants to see specific details in: o Identifying all applicable internal and external data sources for the enterprise o Creating the necessary information management infrastructure to store and organize the data o Making the actionable data available to management and the executive team o Describing the analytical framework for how this data will be utilized to help with business decision making. o Outlining the specific analytics driven online strategy that will be deployed to increase company sales o Company Website and its content o Web-Analytics Implementation We will have several groups of 3-4 Students (depending on the final count). Each group will select a company for this project. The assignment is to develop a detailed Analytics Roadmap that addresses the specific items listed above. You will be expected to complete the following: o A detailed paper (10-15 Pages Maximum) o 25 Minute PowerPoint class Presentation

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11 Decision Sciences Department Business Analytics Program Decision Sciences xxxx Statistics for Analytics 1.5 credit hours Course Description This course introduces the foundations for statistical methodologies used in business analytics and serves as the prerequisite for the rest of the core courses in predictive analytics. In so doing, the course focuses on statistical inference and builds on the probability models introduced in Stochastics for Analytics I. Topics include methods of estimation, hypothesis testing, contingency table analysis, analysis of regression models and logit and probit analysis. Pre-Requisites Stochastics for Analytics I Course Objectives To provide students with an understanding of 1) Statistical inference. 2) Statistical analysis of probability models. 3) Role of statistical inference in model building. 4) Use of regression models for continuous and categorical models. Learning Objectives 1. Understand how statistical analysis is developed for different probability models and is used to answer inference questions relevant to managerial decision making. 2. Learn about how to develop statistical analysis of probability models using software tools and how to implement these by analyzing real life business data. Reading Assignments The student is responsible for studying and understanding all assigned materials. If reading generates questions that are not discussed in class, the student has the responsibility of addressing the instructor privately or raising the issue in a discussion section on Blackboard. Additional reading, including technical papers and on-line material, may be assigned during the course.

12 Texts and Software Required TBD Text Optional TBD Text Software SAS and R Group formation The weekly assignments will be a group effort. The groups will consist of 3 or 4 students. The students are expected to form their own groups. Grading (30%) Assignments (35%) Class quizzes (35%) Final Exam Session Date Subject/Topic Deliverable Due Introduction to statistical inference. Statistics versus parameters. Point estimation. Method of moments and maximum likelihood 1 estimation. Concept of sampling distribution and its role in statistical modeling. Estimation in binomial and normal models Analysis of categorical data and statistical inference. Hypothesis testing for proportions. Contingency table analysis. Discrete variables and measures of association. Analysis of continuous data and statistical inference. Introduction to analysis of bivariate continuous data. Introduction to regression models. Regression models with continuous and categorical independent variables. Analysis of variance. Introduction to multiple regression models. Quiz 1-2, Assignment 1-2 Quiz 3, Assignment 3 Quiz 4, Assignment Multiple regression models and their analysis. Applications of regression models. Regression models with categorical dependent variables. Logit and probit analysis. 8 FINAL EXAM Quiz 5, Assignment 5 Assignment 6

13 Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at For additional information refer to Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

14 Course Description Decision Sciences Department Business Analytics Program Decision Sciences xxxx Stochastic Foundations: Probabilistic Models 1.5 credit hours This course introduces the foundations of Probability, along with the commonly used Probability models (Binomial, Normal, and Poisson) in predictive analytics. Topics covered include probability laws, probability models for modeling dependence, univariate and bivariate models and their applications, conditional mean models including simple regression and extensions to probit and logit models. Pre-Requisites None Course and Learning Objectives To provide students with an understanding of Key probability concepts and graphical representations The basic probability models and related probability distributions (normal, binomial, and Poisson) Commonly used measures for univariate and bivariate distributions (means, variances, co-variances) Conditional mean models and their applications. Reading Assignments The student is responsible for studying and understanding all assigned materials. \ Additional reading, including technical papers and on-line material, may be assigned during the course. Texts and Software Required TBD Text Optional TBD Text Software R

15 Grading (30%) Individual assignments (35%) Class quizzes (35%) Final Exam Session Final Exam Date Subject/Topic Deliverable Due Dealing with uncertainty. Interpretations of probability. Concept of a random experiment. Special random quantities: Events and random variables. Bernoulli trials and categorical random variables. Introduction to rules of probability. Concept of dependence. Conditional probability. Categorical random variables and contingency table models. Law of total probability and Bayes rule. Graphical representations for probability models: trees for probability computations and graphical models for describing dependence. Introduction to univariate probability models. Means and variances for random variables. Binomial, Poisson and normal models and their applications. Introduction to bivariate and multivariate probability models. Covariance of random variables, its properties and applications. Bivariate normal distribution. Simple regression model and bivariate normal model. Conditional mean and introduction to normal regression model. Applications. Other models for conditional means and their applications. Logit and probit models and Poisson regressions. Quiz 1 Session 2 Assignment 1 Assignment 2 Assignment 3 Quiz 2 Assignment 4 Quiz 3 Assignment 5 Quiz 4

16 Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at For additional information refer to Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

17 Decision Sciences Department Business Analytics Program Decision Sciences xxxx Applied Probability Models 1.5 credit hours Course Description This course introduces the basics of stochastic processes. In so doing, the course focuses on applications of stochastic processes and their statistical analysis and builds on the probability models introduced in Stochastics for Analytics I and statistical methodologies in Statistics for Analytics. Topics include Bernoulli processes, Markov chains, Poisson processes and their extensions, Brownian motion, statistical inference for stochastic processes. Pre-Requisites Statistics for Analytics and Stochastics for Analytics I Course and Learning Objectives To provide students with an understanding of 1) Stochastic processes. 2) Statistical analysis of stochastic processes. 3) Properties of important stochastic processes such as Bernoulli process, Markov chains and Poisson processes. 4) Use of stochastic processes. Reading Assignments The student is responsible for studying and understanding all assigned materials. If reading generates questions that are not discussed in class, the student has the responsibility of addressing the instructor privately or raising the issue in a discussion section on Blackboard. Additional reading, including technical papers and on-line material, may be assigned during the course. Texts and Software Required TBD Text Optional TBD Text Software R

18 Grading (30%) Individual assignments (35%) Class quizzes (35%) Final Exam Session 1 Date Subject/Topic Deliverable Due Discrete and continuous probability models and their characterizations. Some distributional results. Introduction to moment generating functions and their use Final Exam Introduction to stochastic processes. Important concepts in stochastic processes. Bernoulli process and related processes. Applications of Bernoulli process and their statistical analysis. Markov chains and their applications. Statistical analysis of Markov chains. Introduction to continuous time stochastic processes. Poisson process and its extensions. Statistical analysis of Poisson processes. Other continuous time stochastic processes. Introduction to Brownian motion and its applications. Quiz 1 Session 2 Assignment 1 Assignment 2-3 Quiz 2- Session 4 Assignment 4 Quiz 3 Assignment 5

19 Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at For additional information refer to Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

20 Course Syllabus Course: MGT Data Mining Spring 2009 Course Website: Instructor: Dr. Srinivas Prasad 415 D Funger Hall Ph. No.: (202) prasad@gwu.edu Office Hours: TBA Teaching Assistant: Bumsoo Kim E- mail: TBA Office Hours: TBA Recommended Texts: Data Mining: Concepts and Techniques, Second Edition, 2nd Edition, Jiawei Han and Micheline Kamber Copyright Morgan Kaufmann Title. ISBN: Data Mining Techniques : For Marketing, Sales, and Customer Relationship Managaement by Michael J. A. Berry, Gordon Linoff, Wiley Computer Publishing; 2 edition (April 5, 2004) Class Format: Class meetings will consist of lectures, case studies, software exercises, and presentations. Student teams will also complete a semester- long project that involves the application of one or more mining techniques in the analysis of large data sets. Hands on experience with software tools will be used to reinforce readings from papers and reference books.

21 Objectives: How can organizations make better use of the increasing amounts of data they seem to be collecting? How can they convert data into information that is useful for managerial decision making? We will attempt to answer these questions by examining several data mining and data analysis methods and tools for exploring and analyzing data sets. Grading: Project 25% Assignments 25% (All assignments will be posted on Blackboard) Three Exams 50% +/- grades will be used. Attendance: Attendance is mandatory. You are allowed one excused absence during the semester. Tentative Schedule: Session Date Topic / Readings 0 Jan 13 No class. Our first class session will be on Jan 27. Please make sure you install SAS on your computers and read the following for this week. Links to other articles will be posted on Blackboard. Readings Getting Started with SAS Software (Online Tutorial in SAS) Getting Started with Enterprise Miner (Online Tutorial in SAS) Knowledge Discovery and Data Mining: Towards a Unifying Framework (1996) Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. Statistics and Data Mining: Intersecting Disciplines, David Hand, SIGKDD Explorations, June Jan 20 Inauguration Day - Holiday 1 Jan 27 Introduction to Data Mining Database and Data Warehousing Basics Multidimensional Systems; OLAP; Excel Pivot Tables Readings Han and Kamber, Chapters 1, 3, 4 Berry and Linoff: Chapters 1 through 4, Chapter 15 An Overview of Data Warehousing and OLAP, Surajit Chaudhuri and Umeshwar Dayal, ACM Sigmod Record, Mar Feb 3 Data Pre-Processing / Intro to SAS Project Team Formation / Initial Proposal

22 Han and Kamber, Chapter 2 Berry and Linoff: Chapter 17 3 Feb 10 Building Predictive Models Regression / Stepwise / Logistic Regression Readings Han and Kamber. Chapter 6 (certain sections) Berry and Linoff: Chapters 5 and 9 Enterprise Miner Reference: Regression Node, Predictive Modeling, 4 Feb 17 Classification/ Prediction/Decision Trees Readings Han and Kamber. Chapter 6 (certain sections) Berry and Linoff: Chapter 6 Enterprise Miner Reference: Tree Node. 5 Feb 24 Decision Trees Readings Berry and Linoff: Chapter 6 6 Mar 3 Association Analysis Han and Kamber. Chapter 5 Enterprise Miner Reference: Association Node 7 Mar 10 Exam (1) In class Mar 17 Spring Break - Holiday 8 Mar 24 Neural Networks Readings Han and Kamber, Chapter 6 Berry and Linoff: Chapter 7 Enterprise Miner Reference: Neural Network Node. 9 Mar 31 Neural Networks / Clustering Readings Ηan and Kamber, Chapter 7 Berry and Linoff: Chapter 11 Enterprise Miner Reference: Clustering Node 10 Apr 7 Clustering / Memory Based Reasoning Readings Ηan and Kamber, Chapter 7 Berry and Linoff: Chapter 8 Enterprise Miner Reference: Memory Based Reasoning Node 11 Apr 14 Genetic Algorithms, Link Analysis Readings Han and Kamber, Chapter 9 Berry and Linoff: Chapters 10 and 13 Enterprise Miner Reference: Link Analysis Node 12 Apr 21 Ethical Issues in Data Mining Special Applications : Bayesian Data Mining

23 Readings Han and Kamber, Chapters 8 and Apr 28 Exam (2) In -Class 14 Apr 30 Project Presentations (Make up day) 15 May 5 Exam (3) - Take Home Due

24 Project Description: The project is designed to serve as an exercise in applying one or more of the data mining techniques covered in the course to analyze real life data sets. A primary objective is to understand the complexities that arise in mining massive, real life datasets that are often inconsistent, incomplete, and unclean. Students can use a variety of software tools to perform the analysis, but the primary toolkit that will be used is SAS Enterprise Miner. This is a semester long project, and students will typically work in 2-3 person teams. The deliverables include a formal project proposal (due in Session 7), and a final report (due at the end of the semester at the time of your final project presentation - Session 14). Examples of typical data mining projects can be found at

25 Decision Sciences Department Business Analytics Program Decision Sciences 6290: Forecasting for Analytics (1.5 credit hours) Dr. Demirhan Yenigun Course Description The focus of the course is on predictive analysis and use of black-box models for time-series forecasting. Emphasis will be given to identifying hidden patterns and structures in the data and exploiting these for forecasting. Topics include use of smoothing methods, identification of seasonalities, trends and non-stationarity, analysis of autocorrelation and partial autocorrelations and their use in identification of Autoregressive Moving Average (ARMA) models. The students will be using SAS Forecasting System throughout the course to apply different forecasting models and methodologies to real life time-series data. Pre-Requisites Statistics for Business Course Objectives Upon completing this course, the students will be able to: 1. Understand the most popular Forecasting methods used in business 2. Familiarize themselves with specific forecasting applications in various vertical markets 3. Use SAS Forecasting System Software and apply it to various types of Forecasting problems Learning Objectives 1. Understand how businesses utilize various statistical methods for predicting the future movements in their key performance measurements 2. Learn about how to utilize various software tools that businesses use for implementing their forecasting activities Texts and Software Required Text Software Practical Time Series Forecasting, by Galit Shumeli, 2011, 2nd Edition, SAS Forecasting System software will be the main software tool for this course.

26 Assignments Reading of textbook material, class notes and the completion of weekly group assignments will be required. There will be 6 group assignments during the mini semester and 5 in-class quizzes. You will use SAS Forecasting System to complete each assignment. Group formation The weekly assignments will be a group effort. The groups will consist of 3 or 4 students. The students are expected to form their own groups. Grading (30%) Assignments (35%) Class quizzes (35%) Final Exam Syllabus and Deliverables Session Date Subject/Topic Deliverable Due 1 Characteristics of time series data. Visualization of time series. Introduction to SAS forecasting system. 2 Comparison of models. Evaluation of forecasts. Retrospective versus predictive analysis. 3 Introduction to basic concepts and models. Autocorrelations and white noise series. Quiz 1, Assignment 1 Naive forecasts. 4 Modeling trends and seasonality. Forecasting using deterministic time series models. Detrended and deseasonalized time series. Quiz 2, Assignment 2 Differencing. 5 Smoothing methods for forecasting. Simple smoothing and exponential smoothing methods. Dealing with trends and seasonality Quiz 3, Assignment 3 by smoothing. 6 Modeling autocorrelated time-series. Autoregressive processes: Identification and Quiz 4, Assignment 4 forecasting. 7 Moving average and ARMA models. Model identification and forecasting. Role of differencing. Forecasting from regression. Ith correlated error terms. Quiz 5, Assignment 5 8 FINAL EXAM Assignment 6

27 Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at For additional information refer to Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

28 Decision Sciences Department Business Analytics Program Optimization I 1.5 credit hours Course Description The course offers a practical and thorough introduction to the field of linear optimization and its versatile applications. The two areas covered are linear programming and network flows. The overarching goal is to enable students to acquire the skills, tools, and foundational analytic knowledge to become sophisticated users of linear optimization models and methods. Intuitive understanding of solution methods and underpinning theoretical paradigms is emphasized throughout, and is deemed essential for the effective usage of linear optimization models, and for future learning about other types of optimization models. The course also emphasizes model formulation, solving and interpretation of results using powerful and popular commercial software. Pre-Requisites Students are expected to have had some exposure to calculus and matrix algebra. Course Objectives 1) Acquire a solid understanding of the fundamental underlying analytic concepts and methods applicable to linear programming and network flow models 2) Practice modeling and solving of linear optimization models using popular commercial software 3) Gain experience in interpreting solutions from optimization models and conducting sensitivity and parametric analyses Text and Software The required textbook for the class is Optimization in Operations Research, by Ronald L. Rardin, Prentice Hall. As shown below in the tentative schedule below, required readings are assigned from the text in support of the class discussions. The following software will be used for developing and solving optimization models: Excel with standard Premium Solver add-in: Premium Solver is a standard add-in that comes with Excel, and is readily accessible for modeling, solving, and interpreting the outputs from optimization models. Excel with Cplex add-in: Instead of Premium Solver, it is possible to use a Cplex add-in, which is a very powerful industrial solver. Required academic license will be provided by the instructor. AMPL: AMPL is a powerful algebraic modeling language that has a far richer language than spreadsheets for modeling complex optimization problems. AMPL interfaces with 1

29 several powerful commercial optimization model solvers including Cplex. Required academic license will be provided by the instructor. Blackboard Students will be required to participate in the course via the Blackboard course page set up for this purpose. This means checking Blackboard for announcements, handouts, updated schedule, homework assignments, final exam, and so on. In addition, the course page has a Discussion Board for you to communicate with each other and with me regarding the course. While I am prompt in answering questions posed through Blackboard, I do not typically answer courserelated questions sent to me via , unless they are of a private nature and of no relevance to the rest of the class. Grading The grades earned will be assigned based on the following: Class participation: 5% Group active participation: 5% Three group assignments: 60% Final exam: 30% You ll be working in pre-assigned and randomly selected teams consisting of two or three members (depending on student count). At the end of the semester, you will be asked to rate the performance of your team members along several criteria. Class Participation On a periodic basis, we shall be working together in class on specific pre-assigned material, and you will need to bring along your laptops for that purpose. Each one of you will be expected to: Have read the pre-assigned material before class Participate in discussions and, occasionally, lead some of the discussions Submit your work (which may be incomplete) at the end of the class, which will be graded based on effort (and not correct answers), and on a pass/fail basis Assignments The class groups are required to work on three sets of assignment questions, some of which will require the usage of the course optimization software. Each group will be required to submit only one report for each assignment, listing all the names in the group. These reports will be graded for both content and presentation. Further assignment guidelines can be found in Blackboard. Final Exam A comprehensive take-home home exam will test your mastery of the material. The exam will require the usage of the optimization software tools employed throughout the course. You are expected to work independently on the exam; no collaboration, whatsoever, will be allowed. Due Dates Deliverables must be turned in through Blackboard by the due date and time given in the syllabus unless noted otherwise. Only the instructor can extend any deadlines for assignments, the GTA 2

30 cannot extend deadlines. Late submission will be penalized 10% per day (integer values only, 1 day late, 2 days late, etc., including holidays and weekends). Deliverables will earn zero points if submitted beyond 1 week past the due date. Tentative Class Schedule Session Date Subject/Topic Readings Deliverable Due 1 Week 1 Linear Programming Models Spreadsheet Modeling Week 2 Linear Programming Models 4.6 Spreadsheet Modeling Handout Modeling using AMPL In-class problem 3 Week 3 Simplex Algorithm Assignment 1 4 Week 4 Simplex Algorithm Overview of Interior Point Methods Handout In-class problem 5 Week 5 Duality & Sensitivity Assignment 2 6 Week 6 7 Week 7 Duality & Sensitivity Characterization of Network Flows Characterization of Network Flows Network Simplex Classification of Network Models In-class problem ,10.9 Assignment 3 Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability Support Services office at For additional information refer to 3

31 Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard. 4

32 Decision Sciences Department Business Analytics Program Optimization II 1.5 credit hours Course Description For many optimization models, the linearity assumption is too restrictive, and it is necessary to introduce integer and/or nonlinear requirements. The course covers integer, nonlinear, and dynamic programming models, along with the fundamental underlying analytic concepts and solution methods. The goal is to enable students to acquire the insights, skills, tools, and foundational analytic knowledge to become sophisticated users of these types of optimization models. The course also emphasizes model formulation, solving and interpretation of results using powerful and popular commercial software. Pre-Requisites Optimization I or equivalent Some exposure to calculus and matrix algebra Course Objectives 1) Learn about the various type of modeling options possible with the introduction of integer variables and/or nonlinear terms 2) Gain an appreciation of good versus poor model formulation choices in the presence of integer variables and/or nonlinear terms 3) Get exposed to the fundamental theory and methods for integer programming models 4) Get exposed to the fundamental theory and methods for nonlinear optimization 5) Gain familiarity with dynamic programming and it applications Text and Software The required textbook for the class is Optimization in Operations Research, by Ronald L. Rardin, Prentice Hall. As shown below in the tentative schedule below, required readings are assigned from the text in support of the class discussions. The following software will be used for developing and solving optimization models: Excel with standard Premium Solver add-in: Premium Solver is a standard add-in that comes with Excel, and is readily accessible for modeling, solving, and interpreting the outputs from optimization models. Excel with Cplex add-in: Instead of Premium Solver, it is possible to use a Cplex add-in, which is a very powerful industrial solver. Required academic license will be provided by the instructor. AMPL: AMPL is a powerful algebraic modeling language that has a far richer language than spreadsheets for modeling complex optimization problems. AMPL interfaces with

33 several powerful commercial optimization model solvers including Cplex (for linear, integer, and quadratic programming), and Knitro (for nonlinear mixed integer programming). Required academic license will be provided by the instructor. Blackboard Students will be required to participate in the course via the Blackboard course page set up for this purpose. This means checking Blackboard for announcements, handouts, updated schedule, homework assignments, final exam, and so on. In addition, the course page has a Discussion Board for you to communicate with each other and with me regarding the course. While I am prompt in answering questions posed through Blackboard, I do not typically answer courserelated questions sent to me via , unless they are of a private nature and of no relevance to the rest of the class. Grading The grades earned will be assigned based on the following: Class participation: 5% Group active participation: 5% Three group assignments: 60% Final exam: 30% You ll be working in pre-assigned and randomly selected teams consisting of two or three members (depending on student count). At the end of the semester, you will be asked to rate the performance of your team members along several criteria. Class Participation On a periodic basis, we shall be working together in class on specific pre-assigned material, and you will need to bring along your laptops for that purpose. Each one of you will be expected to: Have read the pre-assigned material before class Participate in discussions and, occasionally, lead some of the discussions Submit your work (which may be incomplete) at the end of the class, which will be graded based on effort (and not correct answers), and on a pass/fail basis Assignments The class groups are required to work on three sets of assignment questions, some of which will require the usage of the course optimization software. Each group will be required to submit only one report for each assignment, listing all the names in the group. These reports will be graded for both content and presentation. Further assignment guidelines can be found in Blackboard. Final Exam A comprehensive take-home home exam will test your mastery of the material. The exam will require the usage of the optimization software tools employed throughout the course. You are expected to work independently on the exam; no collaboration, whatsoever, will be allowed.

34 Due Dates Deliverables must be turned in through Blackboard by the due date and time given in the syllabus unless noted otherwise. Only the instructor can extend any deadlines for assignments, the GTA cannot extend deadlines. Late submission will be penalized 10% per day (integer values only, 1 day late, 2 days late, etc., including holidays and weekends). Deliverables will earn zero points if submitted beyond 1 week past the due date. Tentative Class Schedule Session Date Subject/Topic Readings Deliverable Due 1 Week 1 Integer Programming Models Week 2 Integer Programming Methods I In-class problem 3 Week 3 Integer Programming Methods II Assignment 1 4 Week 4 Nonlinear Optimization Models Classical Optimization Theory 13.1, 14.1 Handout In-class problem 5 Week 5 Nonlinear Programming Methods I Assignment 2 6 Week 6 Nonlinear Programming Methods II In-class problem 7 Week 7 Dynamic Programming Principles Shortest Path Algorithms Discrete Dynamic Programs Assignment 3 Applicable Policies & Other Information Attendance The George Washington University Bulletin, Graduate Programs, : "Regular attendance is expected. Students may be dropped from any class for undue absence. Students are held responsible for all of the work of the courses in which they are registered, and all absences must be excused by the instructor before provision is made to make up the work missed." University Policies Regarding Conduct and Academic Integrity Students are expected to do the individual assignments and exams on their own. Plagiarism on individual assignments will result in loss of all the points for the assignment and report to academic integrity office. Students are also expected to know and understand all college policies especially the code of academic integrity. For more details see Cell phones and electronic equipment: As a courtesy please turn off all cell phones, etc. You may quietly use electronic devices (e.g. laptops, etc.) for taking notes as long as it does not provide a distraction from the class lecture or discussion. Accommodations: Any student who feels he or she may need an accommodation based on the impact of a disability should contact his or her professor privately to discuss specific needs. To establish eligibility and to coordinate reasonable accommodations, please contact the Disability

35 Support Services office at For additional information refer to Changes: This syllabus represents the current plan of the course best possible plan at this time. The instructor reserves the right to make revisions to any item on this syllabus, including, but not limited to any class policy, the course outline and schedule, grading policy, required assessments, etc. Please note that the requirements for deliverables may be clarified and expanded in class, via , or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check and Blackboard announcements and discussion forums frequently before submitting deliverables. Other notes: The student is responsible for studying and understanding all assigned materials, whether covered in class or not. If the assignments or projects generate questions that are not discussed in class, the student has the responsibility of discussing with the instructor individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.