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

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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.

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Texts and Software Required Text

Competing on Analytics, The New Science of Winning, Thomas H. Davenport & Jeanne G. Harris, Harvard Business School Press.

Software 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 7th week of classes.

Grading

(30%) Team Project (35%) Class quizzes (35%) Final Exam

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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 Introduction to IBM Cognos

Quiz 1

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 Analytics, IBM Coremetrics and Omniture

Quiz 3

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, 2009–2010: "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 http://www.gwu.edu/~ntegrity/code.html.

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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 202-994-8250. For additional information refer to

http://gwired.gwu.edu/dss/.

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 email, or on Blackboard and students are expected to complete the deliverables

incorporating such clarifications and additions. Thus, students should check email 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.

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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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1.5 credit hours

Decision Sciences xxxx – Statistics for Analytics

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

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.

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Texts and Software Required Text TBD Optional Text TBD 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

1

Introduction to statistical inference. Statistics versus parameters. Point estimation. Method of moments and maximum likelihood estimation. Concept of sampling distribution and its role in statistical modeling. Estimation in binomial and normal models.

2-3

Analysis of categorical data and statistical inference. Hypothesis testing for proportions. Contingency table analysis. Discrete variables and measures of association.

Quiz 1-2, Assignment 1-2

4

Analysis of continuous data and statistical inference. Introduction to analysis of bivariate continuous data. Introduction to regression models.

Quiz 3, Assignment 3

5

Regression models with continuous and categorical independent variables. Analysis of variance. Introduction to multiple regression models.

6 Multiple regression models and their

analysis. Applications of regression models. Quiz 5, Assignment 5 7

Regression models with categorical dependent variables. Logit and probit analysis.

Assignment 6

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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 http://www.gwu.edu/~ntegrity/code.html. 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.

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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.

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 Text TBD Optional Text TBD Software R

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Grading

(30%) Individual assignments (35%) Class quizzes

(35%) Final Exam

1

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.

2-3

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.

Quiz 1 – Session 2 Assignment 1

4

Introduction to univariate probability models.

Means and variances for random variables. Binomial, Poisson and normal models and their applications.

Assignment 2

5

Introduction to bivariate and multivariate probability models.

Covariance of random variables, its

properties and applications. Bivariate normal distribution.

Assignment 3 Quiz 2

6

Simple regression model and bivariate normal model. Conditional mean and introduction to normal regression model. Applications.

Assignment 4 Quiz 3

7

Other models for conditional means and their applications.

Logit and probit models and Poisson regressions.

Assignment 5 Quiz 4

Final Exam

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Decision Sciences xxxx – Applied Probability Models 1.5 credit hours

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 Text TBD Optional Text TBD Software R

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Grading

(30%) Individual assignments (35%) Class quizzes

(35%) Final Exam

1

Discrete and continuous probability models and their characterizations.

Some distributional results. Introduction to moment generating functions and their use.

2-3

Introduction to stochastic processes. Important concepts in stochastic processes. Bernoulli process and related processes. Applications of Bernoulli process and their statistical analysis.

Quiz 1 – Session 2 Assignment 1

4-5

Markov chains and their applications. Statistical analysis of Markov chains.

Assignment 2-3 Quiz 2- Session 4

6

Introduction to continuous time stochastic processes. Poisson process and its extensions. Statistical analysis of Poisson processes.

Assignment 4 Quiz 3

7

Other continuous time stochastic processes. Introduction to Brownian motion and its applications.

Assignment 5

Final Exam

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Course Syllabus

Course:

MGT 279 - Data Mining –Spring 2009 Course Website: http://blackboard.gwu.edu/ Instructor:

Dr. Srinivas Prasad 415 D Funger Hall Ph. No.: (202) 994-2078 e-mail: [email protected] Office Hours: TBA Teaching Assistant: Bumsoo Kim

E- mail: TBA Office Hours: TBA

Recommended Texts:

ISBN: 978-1-55860-901-3

• 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

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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 1999.

Jan 20 Inauguration Day - Holiday 1 Jan 27 Introduction to Data Mining

Database and Data Warehousing Basics Multidimensional Systems; OLAP; Excel Pivot Tables

• Han and Kamber, Chapters 1, 3, 4 Readings

• 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 1997.

2 Feb 3 Data Pre-Processing / Intro to SAS Project Team Formation / Initial Proposal

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• 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

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• Han and Kamber, Chapters 8 and 10 Readings

13 Apr 28 Exam (2) – In -Class 14 Apr 30

(Make up day)

Project Presentations

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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 http://kdnuggets.com/datasets/

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Decision Sciences 6290: Forecasting for Analytics (1.5 credit hours) Dr. Demirhan Yenigun

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

Practical Time Series Forecasting, by Galit Shumeli, 2011, 2nd Edition,

Software SAS Forecasting System software will be the main software tool for this course.

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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

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. Naive forecasts.

4

Modeling trends and seasonality. Forecasting using deterministic time series models. Detrended and deseasonalized time series. Differencing.

5

Smoothing methods for forecasting. Simple smoothing and exponential smoothing

methods. Dealing with trends and seasonality by smoothing.

6

Modeling autocorrelated time-series.

Autoregressive processes: Identification and forecasting.

7

Moving average and ARMA models. Model identification and forecasting. Role of

differencing. Forecasting from regression. Ith correlated error terms.

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1.5 credit hours Optimization I

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

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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 course-related questions sent to me via e-mail, 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

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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 4.1-4.5

2 Week 2

Linear Programming Models Spreadsheet Modeling Modeling using AMPL

4.6

Handout In-class problem

3 Week 3 Simplex Algorithm 5.1-5.5 Assignment 1

4 Week 4 Simplex Algorithm

Overview of Interior Point Methods

5.6-5.9

Handout In-class problem

5 Week 5 Duality & Sensitivity 7.1-7.5 Assignment 2

6 Week 6 Duality & Sensitivity

Characterization of Network Flows

7.6-7.7

10.1-10.2 In-class problem

7 Week 7

Characterization of Network Flows Network Simplex

Classification of Network Models

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individually, or, as is generally preferred, raising the issue in the class or in a discussion forum on Blackboard.

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1.5 credit hours Optimization II

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

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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 course-related questions sent to me via e-mail, 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.

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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

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

Session Date Subject/Topic Readings Deliverable Due

1 Week 1 Integer Programming Models 11.1-11.7

2 Week 2 Integer Programming Methods I 12.1-12.4 In-class problem 3 Week 3 Integer Programming Methods II 12.5-12.8 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 13.1-13.8 Assignment 2 6 Week 6 Nonlinear Programming Methods II 14.1-14.8 In-class problem

7 Week 7

Dynamic Programming Principles Shortest Path Algorithms

Discrete Dynamic Programs

9.1-9.8

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Support Services office at 202-994-8250. For additional information refer to http://gwired.gwu.edu/dss/.

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Page 1 of 4 Decision Sciences Department

Business Analytics Program

DNSC 6210 - Decision Analytics

1.5 credit hours

COURSE DESCRIPTION

This course presents essential concepts, methods, and practical tools for the analysis of decisions under uncertainty. The decision analysis process involves formulating and modeling problems, gathering and combining information and data, and applying appropriate choice criteria to reach reasonable (if not optimal) solutions. The course will cover decision tree modeling, the strategic value of information and options, and the incorporation of decision makers’ risk attitudes in the decision making process. The role of sensitivity and robustness analysis will also be demonstrated throughout, as a means to deal with the ambiguities necessarily present in real situations. The methods and tools covered find applications in strategic planning, technology development, and innovation management, among others.

PREREQUISITES Basic familiarity with Excel.

LEARNING OBJECTIVES

From this course, you should

 Understand the scope of problems that can be fruitfully analyzed with decision and risk analysis tools;

 Acquire the “nuts and bolts” to design complete decision analysis models;  Understand the merits of alternative criteria for appraising risk, and know how to

use these criteria;

 Know how to interpret model results and derive actionable insights;

 Develop a mindset to help decision makers prepare for, and even profit from, an uncertainty future;

 Develop an ability to communicate and justify the rationale underlying a decision policy.

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COURSE MATERIAL

Course material, including Reading Assignments, Software Tools, Software Tutorials, Practice Exercises, Excel Solutions, and other files will be posted on Blackboard. The work to do in preparation for each session, as well as assignments due, will be indicated on Blackboard.

SOFTWARE TOOLS

The course will rely on spreadsheets as a platform for modeling and analyzing risk and decisions. Therefore, basic familiarity with Excel is assumed in this course. We will augment Excel with “add-in” tools specialized for decision and risk analysis. Full instructions regarding software access and use will be provided as we progress through the course.

TEXTS The material provided in the course will be self-sufficient. There is no required textbook for the course. However If you should find it helpful, the following are optional (not required) references will be suggested.

GRADING Course grades will be based on

- Two team assignments (20% each): 40%

The deliverable for team assignments will be a short printed report, which will be evaluated based on content (e.g., analytical rigor, technical soundness, insights and conclusions) and presentation (e.g., clarity, conciseness). - A take-home individual assignment: 50%

- Class participation: 10%

Full details about the deliverables (format, turn-in method, etc.) will be specified with each assignment.

Final course grades will be assigned in accordance with prevailing GWSB standards for grade distribution to avoid grade inflation.

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COURSE SCHEDULE (TENTATIVE)

SESSION TOPICS (Preparation material, assignments, and deliverables posted on Blackboard)

Session 1 Critical Thinking about Decisions under Risk

The role of judgment in understanding and framing decisions Introduction to decision modeling tools

Session 2 Modeling Decisions under Uncertainty

Decision Tree analysis; Strategy formulation

Session 3 Profiting from Uncertainty:

The Value of Perfect and Imperfect Information;

Bayesian revision of probabilities based on new information; Real Options and Flexibility

Session 4 Risk-Attitude and Expected Utility Analysis

Certainty Equivalents; Risk Premium; Measuring Risk Attitude

Team Assignment #1 due

In class presentation and debriefing

Session 5 Implications of Expected Utility for Risk Management

Risk sharing; Diversification; Pricing Insurance

Session 6 Risk Analysis via Mean-Risk Modeling:

Portfolio selection problems; Mean-Variance efficiency vs. Expected Utility maximization; Alternative Measures of Risk

Session 7 Behavioral Issues in Expected Utility Analysis

Consistency of Risk Tolerance; Rationality and Paradoxes in risk taking

Team Assignment #2 due

In class presentation and debriefing

Take-home Individual Assignment

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Page 4 of 4 APPLICABLE POLICIES AND OTHER INFORMATION Attendance:

As stated in 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 http://www.gwu.edu/~ntegrity/code.html. Cell phones and electronic equipment:

As a courtesy please turn off all cell phones. You may quietly use a laptop or tablet 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 202-994-8250. For additional information refer to http://gwired.gwu.edu/dss/. Changes:

This syllabus represents the current plan of the course 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 email, or on Blackboard and students are expected to complete the deliverables incorporating such clarifications and additions. Thus, students should check email and Blackboard announcements and discussion forums frequently before submitting deliverables.

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Risk Analytics Syllabus (1.5 credit hours)

In general, the term “risk” refers to uncertain events and their impacts, but more specifically, its meaning depends on the situation. For consumers, the risk of rising prices is an unwelcome prospect because higher prices reduce purchasing power, whereas for investors, the possibility of higher prices is seen as an opportunity because higher prices mean increased profit. The risk paradigm has become a fundamental approach to understanding issues involving uncertainty and weighing related alternatives in a wide range of private and public sector applications. In the private sector, these include finance, marketing, information systems, and supply chain operations, while in the public sector, they include environmental policy, food and drug regulation, and healthcare legislation on the civilian side, and defense strategy and counter-terrorism programs on the national security side. This course introduces the concepts, methods, and applications of risk analysis. The textbook readings help reinforce and deepen the understanding of each topic, while the case studies—which involve the application of simulation software—serve to illustrate real-world situations in which risks must be identified, assessed, managed, and communicated.

Pre-Requisites Statistics

1) To present the various interpretations of the term risk.

2) To introduce the models used to express and calculate risk and the formats used to display and communicate risks.

3) To illustrate how risk information is used in the private and public sectors. Learning Objectives

1) To understand how risk is measured and estimated.

2) To be able to evaluate and present risk-related decision alternatives for decision-making. 3) To be aware of prevailing risk analysis practices in industry and government.

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

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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 Texts  Principles of Risk Analysis, by C.E. Yoe, CRC Press (2011)  Guide to Using @RISK

http://www.palisade.com/downloads/manuals/EN/RISK5_EN.pdf

Software @RISK

Grading

The grades earned will be assigned based on the point total at the end of the semester, as indicated below.

Grade A A- B+ B B- C+ C

Points 930 900 870 830 800 770 730 Assignments and Due Dates

The total course grade of 1000 points will be determined by the following assignments:

Week Topic Assignment Points Effort

1 Quantifying Uncertainty Read Chapters 1,2,11,12

Case Study 1 100 Individual

2 Modeling & Calculating Risk Read Chapters 4,10,14,15

3 Visualizing Risk Read Chapters 5,17,18

4 Reduction, Avoidance, & Transference of Risk

Handout I

5 Making Risk-Related Decisions

Read Chapters 3,8,9

6 Financial Applications Handout II

7 Health & Safety Applications Handout III

Final Exam None 250 Individual

Attendance & Participation None 50 Individual

The George Washington University Bulletin, Graduate Programs, 2009–2010: "Regular attendance is expected. Students may be dropped from any class for undue absence…. Students are held

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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."

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Department of Decision Sciences

Course Title: Computational Analytics Course Name: DNSC ____

Instructor: Shivraj Kanungo Room: Funger 415

Phone: (202) 994-3735 Email: [email protected].

Course description

The ability to design and implement decision aids is a sought after capability in the context of any analytics position in the industry. Students taking this course are expected to develop a working knowledge of how to provide workable solutions in the context of business analytics.

This is an application-oriented course and students will learn how to develop and deploy end-user oriented applications for descriptive, predictive and prescriptive analytical models. The emphasis will be on learning design and implementation techniques that allow the integration of data, models and user-interfaces. Students will (individually and in groups) deploy well-known models (e.g. forecasting, optimization, simulation etc.) and develop decision support systems.

Prerequisites

None; however, some exposure to basic programming skills is useful.

Course objectives

1. To provide students with a working knowledge of VBA and R

2. To enable students to develop the skill sets to design and develop prototypical solutions using both VBA and R

3. To provide students with an understanding of contemporary and emerging frameworks to incorporate analytics in business decision frameworks

Learning objectives

Students who complete this course will be able to

1. Translate a structured decision problem into a prototype solution with VBA or R or both.

2. Use language constructs in VBA and R (including control flow and data structures) 3. Develop and deploy a user form in VBA (including data validation and functionality

associated with widgets)

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5. Take design decisions on partitioning functionality between VBA and R.

Course delivery

Each class session will include a lecture component and an instructor-led model development and implementation exercise. Students will use Visual Basic for

Applications (VBA) and R and will work, for the most part, in teams. We will cover both environments because while Excel is very popular tool, familiar to many, and relatively easy to use, the computational support is relatively limited. So it makes sense to merge R’s functionality and language with Excel’s interface and visual programming metaphor.

Course material

1. All course material will be provided. It will be provided in the form of slides, tutorials, and program files. The slides and tutorials will be available as pdf files. 2. For the VBA portion the following book is strongly recommended:

Albright, S. Christian (2012) “VBA for Modelers: Developing Decision Support Systems Using Microsoft® Excel” (ISBN-13: 9781133190875)

Software used

1. MS Excel and VBA for Excel 2. R (http://www.r-project.org/) Grading Component Weight Individual Assignments (6) 30 Final Exam 30 Group Project 40 Assignments

Six individual assignments, each worth 5% of the final grade, are designed to reinforce learning.

Final exam

The final exam will be comprehensive in coverage and will be held after all classes are completed.

Course calendar

Session Date Topic Assignment

1 VBA data structures and control flow; R