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.
2.
3.
4.
5.
Understand why Business Analytics is a key competency essential for business success
Understand how to assess the Analytics competency of a Business Enterprise
Understand how businesses can organize, enhance and store their business data
Interpret and analyze web data to derive actionable customer intelligence.
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.
Texts and Software
Required Competing on Analytics, The New Science of Winning,
Thomas H. Davenport & Jeanne G. Harris, Harvard
Text
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
Syllabus and Deliverables
Session
1
2
3
4
5
6
7
Date
Subject/Topic
Introduction to Business Analytics
Business Data Overview: Sources and Uses
of Business Data.
“BIG DATA”
Competing on Analytics in today’s Business
Landscape. Assessing the Analytical
Competency of a Business Enterprise
Data Warehouse Modeling: OLAP and
Reporting. Data Cube Technology
Introduction to IBM Cognos
How Businesses can utilize their Data:
Overview on Data Mining Techniques
Introduction to Web Analytics – Part I : Web
Data and Analysis of Online Behaviors,
Web Analytic Tracking Tools: Google
Analytics, IBM Coremetrics and Omniture
Introduction to Web Analytics – Part II
Web Analytic Applications, Analytical
Methods and Tools for Website
Effectiveness Testing – Google Optimizer
Deliverable Due
Quiz 1
Quiz 2
Quiz 3
Quiz 4
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.
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.
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
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)
2)
3)
4)
Statistical inference.
Statistical analysis of probability models.
Role of statistical inference in model building.
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.
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
1
2-3
4
5
6
7
8
Date
Subject/Topic
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.
Deliverable Due
Analysis of categorical data and statistical
inference. Hypothesis testing for proportions.
Quiz 1-2, Assignment
Contingency table analysis. Discrete
1-2
variables and measures of association.
Analysis of continuous data and statistical
inference. Introduction to analysis of
Quiz 3, Assignment 3
bivariate continuous data. Introduction to
regression models.
Regression models with continuous and
categorical independent variables. Analysis
of variance. Introduction to multiple Quiz 4, Assignment 4
regression models.
Multiple regression models and their
Quiz 5, Assignment 5
analysis. Applications of regression models.
Regression
models
with
categorical
dependent variables. Logit and probit Assignment 6
analysis.
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.
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.
Decision Sciences Department
Business Analytics Program
Decision Sciences xxxx – Stochastic Foundations: Probabilistic Models
1.5 credit hours
Course Description
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
Grading
(30%) Individual assignments
(35%) Class quizzes
(35%) Final Exam
Session
1
2-3
4
5
6
7
Final
Exam
Date
Subject/Topic
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.
Deliverable Due
Quiz 1 – Session 2
Assignment 1
Assignment 2
Assignment 3
Quiz 2
Assignment 4
Quiz 3
Assignment 5
Quiz 4
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.
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.
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
Grading
(30%) Individual assignments
(35%) Class quizzes
(35%) Final Exam
Session
1
2-3
4-5
6
7
Final
Exam
Date
Subject/Topic
Deliverable Due
Discrete and continuous probability models
and their characterizations.
Some distributional results. Introduction to
moment generating functions and their use.
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.
Quiz 1 – Session 2
Assignment 1
Assignment 2-3
Quiz 2- Session 4
Introduction to continuous time stochastic
processes. Poisson process and its
Assignment 4
extensions. Statistical analysis of Poisson
Quiz 3
processes.
Other continuous time stochastic processes.
Introduction to Brownian motion and its
applications.
Assignment 5
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.
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.
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: 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 2006. Morgan Kaufmann Title.
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
reference books.
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
0
Jan 13
Topic / Readings
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.
1
Jan 20
Jan 27
Inauguration Day - Holiday
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 1997.
2
Feb 3
Data Pre-Processing / Intro to SAS
Project Team Formation / Initial Proposal
3
Feb 10
• Han and Kamber, Chapter 2
• Berry and Linoff: Chapter 17
Building Predictive Models
Regression / Stepwise / Logistic Regression
Readings
• Han and Kamber. Chapter 6 (certain sections)
•
4
Feb 17
5
Feb 24
6
Mar 3
Berry and Linoff: Chapters 5 and 9
Enterprise Miner Reference: Regression Node, Predictive Modeling,
•
Classification/ Prediction/Decision Trees
Readings
• Han and Kamber. Chapter 6 (certain sections)
•
Berry and Linoff: Chapter 6
•
Enterprise Miner Reference: Association Node
• Enterprise Miner Reference: Tree Node.
Decision Trees
Readings
• Berry and Linoff: Chapter 6
Association Analysis
• Han and Kamber. Chapter 5
8
Mar 10
Mar 17
Mar 24
Exam (1) – In class
Spring Break - Holiday
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
7
•
Berry and Linoff: Chapter 11
• Enterprise Miner Reference: Clustering Node
10
Apr 7
Clustering / Memory Based Reasoning
Readings
• Ηan and Kamber, Chapter 7
•
11
Apr 14
•
12
Apr 21
Berry and Linoff: Chapter 8
• Enterprise Miner Reference: Memory Based Reasoning Node
Genetic Algorithms, Link Analysis
Readings
• Han and Kamber, Chapter 9
Berry and Linoff: Chapters 10 and 13
Enterprise Miner Reference: Link Analysis Node
•
Ethical Issues in Data Mining
Special Applications : Bayesian Data Mining
13
14
15
Apr 28
Apr 30
(Make
up day)
May 5
Readings
• Han and Kamber, Chapters 8 and 10
Exam (2) – In -Class
Project Presentations
Exam (3) - Take Home Due
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/
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 Practical Time Series Forecasting, by Galit Shumeli,
2011, 2nd Edition,
Text
Software SAS Forecasting System software will be the main
software tool for this course.
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
1
2
3
4
5
6
7
8
Date
Subject/Topic
Characteristics of time series data.
Visualization of time series. Introduction to
SAS forecasting system.
Comparison of models. Evaluation of
forecasts. Retrospective versus predictive
analysis.
Introduction to basic concepts and models.
Autocorrelations and white noise series.
Naive forecasts.
Modeling trends and seasonality. Forecasting
using deterministic time series models.
Detrended and deseasonalized time series.
Differencing.
Smoothing methods for forecasting. Simple
smoothing and exponential smoothing
methods. Dealing with trends and seasonality
by smoothing.
Modeling autocorrelated time-series.
Autoregressive processes: Identification and
forecasting.
Moving average and ARMA models. Model
identification and forecasting. Role of
differencing. Forecasting from regression. Ith
correlated error terms.
FINAL EXAM
Deliverable Due
Quiz 1, Assignment 1
Quiz 2, Assignment 2
Quiz 3, Assignment 3
Quiz 4, Assignment 4
Quiz 5, Assignment 5
Assignment 6
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.
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.
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
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 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
2
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
1
Week 1
2
Week 2
3
Week 3
4
Subject/Topic
Linear Programming Models
Spreadsheet Modeling
Linear Programming Models
Spreadsheet Modeling
Modeling using AMPL
Readings
Deliverable Due
4.1-4.5
4.6
Handout
In-class problem
Simplex Algorithm
5.1-5.5
Assignment 1
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
7.6-7.7
10.1-10.2
In-class problem
7
Week 7
Duality & Sensitivity
Characterization of Network Flows
Characterization of Network Flows
Network Simplex
Classification of Network Models
10.3-10.7,10.9
Assignment 3
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.
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/.
3
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.
4
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
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 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
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
11.1-11.7
2
Week 2
Integer Programming Methods I
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
12.1-12.4
9.1-9.8
In-class problem
Assignment 3
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.
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.
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.
Page 1 of 4 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.
Page 2 of 4 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
Given out right after Session 7, due during exam week
Page 3 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 202994-8250. 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.
Page 4 of 4 Decision Sciences Department
Business Analytics Program
Risk Analytics
Syllabus
(1.5 credit hours)
Course Description
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 counterterrorism 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
Course Objectives
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.
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 Texts
Software
 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
@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
1
Quantifying Uncertainty
2
Modeling & Calculating Risk
3
Visualizing Risk
4
5
Reduction, Avoidance, &
Transference of Risk
Making Risk-Related
Decisions
6
Financial Applications
7
Health & Safety Applications
Assignment
Read Chapters 1,2,11,12
Case Study 1
Read Chapters 4,10,14,15
Case Study 2
Read Chapters 5,17,18
Case Study 3
Handout I
Case Study 4
Read Chapters 3,8,9
Case Study 5
Handout II
Case Study 6
Handout III
Case Study 7
Points
Effort
100
Individual
100
Individual
100
Individual
100
Individual
100
Individual
100
Individual
100
Individual
Final Exam
None
250
Individual
Attendance & Participation
None
50
Individual
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.
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.
Department of Decision Sciences
Course Title: Computational Analytics
Course Name: DNSC ____
Instructor: Shivraj Kanungo
Room: Funger 415
Phone: (202) 994-3735
Email: kanungo@gwu.edu.
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)
4. Seamlessly integrate VBA and R using RDCOM
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
1
Topic
VBA data structures and control flow; R
computing environment and data structures
Assignment
Assignment 1
2
3
4
5
6
7
User Forms in VBA; Subroutines and data
import; RDCOM and data exchange
Simulation and optimization applications
(queuing and inventory) in Excel VBA
R graphics; interoperable programs in VBA using
R functionalities
Forecasting model in Excel VBA; integrating R
{forecast}
Integrating solver into applications (Blending,
product mix and scheduling) in Excel VBA
Portfolio optimization application; employ
solveLP {linprog} and solveLP {linprog}
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Other information
1. Students can expect to spend at least 5 hours per week outside the classroom. This
could vary depending on their prior preparation and background.
2. Students are expected to do their assigned readings before class
3. Assignments are to be turned in on the day they are due. Late assignments will not be
accepted.
4. It is important for all students to be familiar with and adhere to the GW Code of
Academic Integrity (http://www.gwu.edu/~ntegrity/code.html).
Decision Sciences Department
Business Analytics Program
Computational Optimization - Syllabus
3 credit hours
Course Description
The course will acquaint students with the software and optimization solvers used by analytics
professionals to code and model industry-size optimization problems. The course will provide
students guidance to efficiently formulate optimization problems and to implement simple
algorithms for the solution of large-scale / data-driven optimization problems that are hard to
solve (both theoretically and practically). Examples of such problems include telecommunication
and transportation network design, integrated circuit layout, vehicle/crew routing and scheduling,
asset allocation, facility location and capacity allocation problems. The course will be highly
interactive with individual and group assignments and with intensive hands-on computer
practice. The students will be presented with various problems and projects to work on during
the semester.
Pre-Requisites
MSBA Program Candidacy or Instructor approval.
Course Objectives
1) To get acquainted with the software, programming and modeling languages, and methods
used by professionals to solve large-scale optimization problems;
2) To develop confidence in designing and implementing advanced optimization methods
using software packages, such as AMPL, Matlab, and C++;
3) To be able to recognize the structure and properties of mathematical optimization
problems;
4) To become familiar with the efficient formulation of practical problems taking the form
of convex, stochastic, and mixed-integer nonlinear mathematical programming problems.
Course Structure
The course is structured in four modules. Each is focused on a particular coding, modeling,
and/or computational tool. The modules are respectively devoted to:
1) Algebraic modeling languages. The AMPL modeling language will be used to:




compactly formulate large-scale optimization models,
interact with optimization solvers;
develop algorithms complementing optimization solvers and allowing for the
efficient solution of large-scale problems;
build dynamic link libraries (DLL) and to employ them in the optimization
process.
2) Optimization solvers:
 commercial solvers, such as Cplex and Gurobi;
 open-source solvers, such as Cbc, Ipopt, Couenne, Bonmin
are used and/or supplemented to solve complex optimization problems.
3) Matlab software. The CVX and Yalmip Matlab software packages will be used to model
and solve convex and nonlinear optimization problems using the Matlab interface;
4) C++ programming language. It will be used to:
 formulate optimization problems;
 allow for an optimal interaction with the callable libraries of optimization solvers.
Module
Week
1
Module 1: AMPL
2
3
4
Module 2: Optimization Solvers
5
6
7
Module 3: MATLAB
8
9
10
Topics
Assignments
Introduction
AMPL Syntax
Formulation of Optimization Problems
with AMPL
Coding of Algorithms with AMPL
Typology of Optimization Problems:
Description of Key Features
Use of Commercial and Open-Source
Optimization Solvers Through the AMPL
Interface
Basic Matlab Syntax
Formulation of Optimization Problems
with MATLAB
Solution of Problems with Matlab
Interface between AMPL, Matlab and
Optimization Solvers
Convex Optimization Problems
CVX Software Package
CVX Software Package
Mixed-Integer Nonlinear Optimization
Problems
Yalmip Software Package
Individual
Homework
Group
Project
Project
Presentation
Individual
Homework
Group
Project
Module 4: C++
11
Basic C++ Syntax
Project
Presentation
12
Formulation and Solution of Optimization
Problems with C++
Individual
Homework
13
Use of Callable Libraries with C++
Group
Project
14
Final Presentation
Project
Presentation
Reading and Programming 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. Additional reading, including technical papers and on-line
material, may be assigned during the course.
Texts and Software
Suggested books:
[1] Chapra S.C. 2012. Applied Numerical Methods with Matlab for Engineers and Scientists.
Third Edition. McGraw-Hill.
[2] D. Gilly and the staff of O'Reilly & Associates, Inc. 1992. UNIX in a Nutshell. O'Reilly &
Associates, Inc., Sebastopol, CA,
[3] IBM. 2012. IBM ILOG AMPL Version 12.2 - User’s Guide.
[4] R. Fourer, D.M. Gay, B.W. Kernighan. 2002. AMPL: A Modeling Language for
Mathematical Programming, Brooks/Cole Publishing Company.
[5] B. Stroustrup. 1997. C++ Programming Language. Addison-Wesley, Reading, MA.
Software:
[1] Algebraic Modelling Language AMPL
[2] Matlab
[3] C++
[4] Commercial optimization solvers: Cplex, Gurobi
[5] Open-source optimization solvers: Bonmin, Couenne, Cbc, Ipopt
[6] CVX: Matlab Software Package for Disciplined Convex Programming
[7] Yalmip: Matlab Software Package
Grading
The grading will be based on a set of homework assignments and projects. Tentatively, 40% of
the grade will be based on the homework assignments and 60% will be based on projects.
Assignments
The total course grade will be based on:
•
homework assignments, and
•
projects.
Due Dates
Deliverables must be turned in by the due date and time given in the syllabus unless noted
otherwise. Late submission will not be accepted. Deliverables will earn zero points if submitted
beyond the due date.
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.
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 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 during office hours.
COURSE SYLLABUS 1
Spring 2013
Marketing Metrics and Marketing Analytical Tools
Instructor: Alexander V. Krasnikov, PhD
Office: Funger 301B
Office hours: T 6:00-7:00 PM and by appointment
Email: avkrasn@gwu.edu
Phone: (202) 994 4916
Teaching Assistant: TBD
Course Objectives:
Effectiveness and productivity of marketing are fundamental to stock market
valuations, which often rest upon the aggressive assumptions about customer
acquisitions and market growth. Despite its importance, marketing is one of the
least understood, least measured functions at many companies. As a profession,
marketing must evolve beyond relying almost exclusively on conceptual content to
drive decisions and actions. In today’s environment, marketing managers require
tools and techniques to both quantify the strategic value of marketing initiatives,
and to maximize marketing campaign performance. This course is designed to help
marketers demonstrate the return on investment (ROI) of marketing and leverage
data from marketing analytics to make better and more informed marketing
decisions. In particular, this course is designed to introduce students to marketing
metrics and apply it for decisions in the following areas:
• Market Selection
• Targeting and Positioning
• Customer choices
• Customer Profitability and Lifetime Value
• Product Design
1
This is preliminary syllabus as of July 2012 and significant changes may be made by the time this
course is offered in Spring 2013. Please check up with instructor before this course is offered.
• Advertisement and Promotion
• Marketing Mix
• Internet Marketing
• Word-of-Mouth and Social Media Marketing
Content covered is especially applicable to those pursuing careers as marketing and
brand managers. Through the use of both lecture material, and case applications,
students will apply their knowledge and experience in these areas to solve
marketing problems.
Class Format
Class meetings represent a mixture of lectures, case discussions and in-class
exercises. In this course I am using Marketing Engineering software and students
are expected to learn to use it and apply during classes. The bulk of class sessions
will be devoted to the discussions of different marketing models, their applications,
and cases analyses.
Grading
Grading will be based on both individual and team inputs. At the end of class,
grades for team assignments will be weighted by team evaluations for individual
members.
Case quizzes (5)
Case write-ups (5)
Midterm Exam
Final Exam
Class Participation
Individual
Team
Individual
Individual
Individual
15 %
25 %
25 %
25 %
10 %
Case quizzes (individual - 15%)
Students should be prepared to answer short questions related to the case
discussed on the particular date. This is open case quiz (20-25 min). Feel free to
make notes on case, write calculations, etc. You will need to read carefully cases
before class and, if necessary, complete analysis and calculations necessary to
answer case questions. For dates of quizzes – see schedule.
Case write-ups (team – 25%)
Each team will be required to prepare a short (not more than 5 pages) write up with
answers on case questions. During class meeting I may call randomly teams and ask
questions about case, analysis, calculations, etc.
Midterm and Final Exams (25% each)
Exams will be based on material covered in the preceding period. I will provide
more details on the nature of exam.
Class Participation (10%)
Class participation will be based on following criteria:
• Attendance
• Overall engagement in class
• Responding to questions as a part of team
Curse Materials
• Lecture notes will be posted on Blackboard a week prior to class meeting.
• Text: Principles of Marketing Engineering, by Gary L. Lilien, Arvind
Rangaswamy, and Arnaud De Bruyn (2007)
• Cases marked as ME-XL can be downloaded with Marketing Engineering
software at http://www.decisionpro.biz/
• There are three HBS/Kellogg cases for this course (more details are available
upon request).
Final points
• You are expected to attend class and are responsible for the academic
consequences of absence. For this class, due to the importance of being
available to your groups for the project, and because of the participation
requirement, attendance is particularly important.
• Try to seat together with your team members.
• Laptop use is permitted but is restricted for the purposes of class. Upon
request on instructor you may be required to close your laptop.
Date
01/15/2013
01/22/2013
01/29/2013
02/05/2013
02/12/2013
02/19/2013
Tentative Meeting Schedule (Subject to change)
Topics and Assignments
Class 1 (Week 1)
Marketing metrics and managerial decisions.
Review of Marketing Engineering.
Readings: Chapter 1
Class 2 (Week 1)
Definition of market. Market segmentation. Cluster analysis.
Readings: Chapter 3
In-class quiz: Segmentation - Connector PDA 2001 (ME-XL)
Class 3 (Week 2)
Positioning and Targeting strategies. Perceptual Maps.
Readings: Chapter 4
Team write-up: Infiniti G20 (ME-XL)
Class 4 (Week 2)
Understanding customer decisions. Choice models.
Readings: Chapter 2
In-class quiz: Bookbinders Book Club (ME-XL)
Class 5 (Week 3)
Growing customer value and customer profitability. CLV models.
Readings: Chapter 2
Team write-up: Northern Aero (ME-XL)
Class 6 (Week 3)
Product decisions. Developing new product and service offerings.
Conjoint analysis.
Readings: Chapter 6
In-class quiz: Forte hotel design (ME-XL)
Class 7 (Week 4)
Midterm exam
Class 8 (Week 4)
Forecasting models
Readings: Chapter 5
In-class quiz: Ford hybrid cars (ME-XL)
Class 9 (Week 5)
Designing marketing mix
Readings: Chapter 7
Team write-up: Syntex Labs (ME-XL)
Class 10 (Week 5)
Managing Brand Equity and Brand Architecture
Readings: Keller, Kevin Lane (2000), “The Brand Report Card”
Harvard Business Review, 78 (January/February), 147-157.
In-class: TBD
Class 11 (Week 6)
Web Metrics and Advertisement in Media
Case: Air France
Class 12 (Week 6)
02/26/2013
02/28/2013
Word-of-Mouth Metrics.
Readings: V. Kumar, J. Andrew Petersen, and Robert P. Leone (2007),
“How Valuable Is Word of Mouth?” Harvard Business Review,
85(October), 139‐146.
Class 13 (Week 7)
Deriving value from social media.
Case: Cisco Systems: Launching The ASR 1000 Series Router Using
Social Media Marketing
Class 14 (Week 7)
Final Exam
Decision Sciences Department
Business Analytics Program
Decision Sciences xxxx – Supply Chain Analytics
1.5 credit hours
Course Description
This course focuses on analytical models that are used in the planning, design and operation of
global supply chains. Students are exposed to concepts and models important in supply chain
management including topics such as forecasting, aggregate planning, sales and operations
planning, inventory management, supply chain network design and planning, and pricing and
revenue management. Emphasis will be on the increasingly important role of Analytics in
improving the performance of supply chains.
Prerequisites
MSBA Program candidacy or Instructor approval
Course Objectives
To provide students with an understanding of
1. The role of inventory in the supply chain, and techniques for effective inventory
management
2. Techniques for matching supply and demand, including aggregate planning, risk pooling
and inventory placement, integrated planning and collaboration, and information sharing
3. Tradeoffs and techniques for effective supply chain network design
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 and
will be posted on Blackboard.
Texts and Software
Required None
Text
Optional Ananth Raman, Marshall Fisher, The New Science of
Retailing: How Analytics Are Transforming the Supply
Text
Chain and Improving Performance
Harvard Business Press Books
272 pages. Publication date: Jun 22, 2010.
Simchi-Levi, David, Philip Kaminsky, and Edith
Simchi-Levi. Designing and Managing the Supply
Chain. McGraw Hill/Irwin, 2007. ISBN:
9780073341521.
Chopra, Sunil, and Peter Meindl. Supply Chain
Management. 3rd ed. Prentice Hall, 2006. ISBN:
9780131730427.
Shapiro, Jeremy F. Modeling the Supply Chain.
Southwestern College Pub, 2000. ISBN:
9780534373634.
Software
Wallace Hopp, Supply Chain Science, McGrawHill/Irwin Series Operations and Decision Sciences,
2007. ISBN-10: 0073403326 | ISBN-13: 9780073403328
Excel Solver, CPLEX
Grading
Assignments: 40%
Quizzes: 30%
Final Exam: 30%
Assignments
There will be 4 Assignments involving problem sets that will be posted on Blackboard.
Due Dates
All deliverables are due on the due dates posted on Blackboard. No late submissions will be
accepted.
Syllabus and Deliverables
Session
1
2
3-4
5
6
7
Date
Subject/Topic
Deliverable Due
Introduction to Supply Chain Management
(SCM)
Definition, Key issues, Performance Metrics.
Introducing to three levels of SCM:
Strategic: Network configuration
Tactical: Inventory Management
Operational: Factory
planning/scheduling/procurement
Forecasting and Aggregate Planning Models
Inventory Models and Risk Pooling
Supply Chain Inventory Systems
- EOQ
- Base-stock, (s,S) policies; Periodic
Review/Continuous Review singlestage systems
- Multi-echelon systems
- Effect of lead time; Assemble-ToOrder systems
Economics of SCM; Contracts and
Incentives; Supply Chain Collaboration,
Strategic Alliances, Procurement and
Outsourcing Strategies
Supply Chain Architecture, Network and
Product Design,
Supply Chain Integration and
Implementation
Discussion of various modules in a typical
supply chain architecture with an overall
goal of how everything fits together
Recent Trends in SCM
- International issues (Global cost
Quiz 1
Assignment 1
Assignment 2
Quiz 2
Assignment 3
Quiz 3
Assignment 4
-
factors, infrastructure, exchange rates
etc)
Real-time SCM (RFID
implementations)
Service supply chains
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.
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.
Decision Sciences Department
Business Analytics Program
Business Process Analytics (1.5 cr)
Course Description
Every firm needs to manage a variety of processes that generally encompass a number of
departments, and consist of several different functions and activities with various process
owners. A process has to be efficient and cost effective while serving its overall goal. Nonvalue added activities, or those that do not directly support the organization’s products, services
or customers need to be eliminated or modified.
In our times, firms often undertake large-scale, data-driven projects to improve their profitability
through, for example, initiatives in supply chain management, revenue management, resource
optimization, and other areas. However, most of the time, organizations take a “functional”
approach focusing narrowly on a particular department within the organization, such as
marketing or production, and placing emphasis on the IT improvement side, while ignoring the
business and process side. That can be one of the main reasons for project failure.
This course examines the key methods used to analyze, develop and improve processes in a
given organization. The objective is to develop an understanding of the trade-offs and limitations
involved in process design, as well as to develop an understanding of many of the basic tools
used to analyze and improve processes. In addition, students will learn how to test the
performance of existing and proposed processes by building simulation models using a powerful
discrete-event simulation tool used frequently in industry. The course is intended to be hands-on
and application oriented, and will help students acquire the requisite skills for adopting a
process-oriented approach when undertaking major projects.
Pre-Requisites
Probability and Statistics
Course Objectives
1) Learn how to come up with a comprehensive process map and question its various
aspects including its cycle time, resources involved and cost in order to streamline the
overall process
1
2) Learn how to design and build a process-oriented simulation model, validate its outcome
and measure the resulting performance
3) Apply the knowledge and skills gained in class on an assigned class project focused on
process improvement at a service or manufacturing organization
Texts and Software
Text: Business Process Modeling, Simulation, and Design, by M. Laguna, and J. Marklund.
Software:
The discrete-event simulation package, Extend (comes with the textbook).
Grading
The grade will be based on:
Class participation: 10%
Individual assignments: 20%
Case study reports: 30%
Class presentations: 10%
Final project report: 30%
Class participation
Class discussion is an important part of the learning process. You are required to contribute to
that through class discussions on the case studies, problems solved during lectures and other
assignments.
Individual Assignments
There will be two homework assignments with problems that will require modeling and analysis
based on topics covered in class. While you may exchange ideas to better understand the
problem or how to approach it, you are required to work out the details and the complete solution
on your own.
Case study reports
You are required to submit a report for the two case studies assigned in this class. You will be
working in groups of three, but will be required to submit only one report, listing all the names.
These reports will be graded for both content and presentation; they should not exceed singlespaced five pages (excluding appendices).
2
Final project:
It is very essential that students can apply what they learn in class on an actual industry project.
Every student will be assigned to a group and each group will be assigned to a process
improvement project. These projects will have to be arranged by the instructor before the class
starts. They could be for example, procurement process improvement at a manufacturer, patient
record entry process at a hospital or similar projects at a bank, restaurant, or government office.
Groups will be evaluated based on two class presentations and a final report. During their first
class presentation, they will be asked to present the current “as-is” process and what they think
can be the problem in this process. In their final presentation, they will propose improvements to
streamline that process and come up with their recommended “to-be” processes including any
technology proposals. During the final class, they will learn from each other’s project as well
and be exposed to different industries. The presentations and the final report will be graded based
on the significance of the issues identified, how robust and implementable the team’s solutions
are, and how the team is planning to measure the improvement.
Due Dates
Deliverables must be turned 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 Course Schedule
Session
1
Subject/Topic
Introduction: What is a business process?
Process vs. Organizations
Setting the Stage for Business Process
Improvement (BPI) projects
-Selecting process owners
-Organizing for process improvement
Deliverable Due
Flowcharting: Drawing process picture (“Asis”) (Introducing to Visio)
2
Understanding process characteristics
- Efficiency
- Cycle time
- Throughput
- Bottleneck analysis
Case 1 report
3
CASE 1: Value Chain and IT Transformation at
Desko (B) (Evaluated)
Streamlining the process (Determining “To-be”
processes)
3
Creative process design
Case 2 report
CASE 2: (TBD)
GROUP PRESENTATIONS
4
Introduction to Simulation Tool (%)
Random number generation
Fitting a distribution to a data
Simulation Tool (continued)
Model Design
5
Assignment 1
Validation; analysis of output data
Simulation Tool (Continued)
Discussion of various practical process
improvement examples using the tool
6
Assignment 2
Measurements
Benchmarking
7
FINAL GROUP PRESENTATIONS
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.
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 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.
5
Department of Decision Sciences
Course Title: Social Network Analytics
Course Name: DNSC ____
Instructor: Shivraj Kanungo
Room: Funger 415
Phone: (202) 994-3735
Email: kanungo@gwu.edu.
Course description
This course introduces the concepts, applications, and methods of understanding the
dynamics of networks, with a particular focus on social network analysis. The term
“social networks” has become a buzzword in popular culture. People now routinely talk
about “networking” to advance their careers, that we are connected by “six degrees of
separation,” and that it’s “who we know” rather than “what we know” that matters. Upon
taking this course students will be able to analyze and describe real networks (power
grids, WWW, social networks, etc.) as well as relevant phenomena such as disease
propagation, search, organizational performance, social power, and the diffusion of
innovations. Students will learn how to frame the research question, collect the data, run
the analysis, and interpret the results. In addition, they will learn how to design and
evaluate models of diverse networks to improve their understanding of the underlying
principles.
Prerequisites
None; however, some exposure to basic math is useful.
Course objectives
This is a course in social network analysis and methods. While the course places
emphasis on the theories associated with networks a working knowledge using
appropriate methods and tools is equally important. Over the course of the semester you
will be expected to develop the following competencies:
1. Familiarity and fluency in the language of social network analysis (SNA)
2. Communicating social network concepts and methods to specialists and laypersons
3. Proficiency in organizational social network analysis including data collection,
analysis, and reporting
4. Working knowledge of one software tool used in network analysis
Learning objectives
Students who complete this course will be able to
1. Recognize a problem that lends itself to the SNA approach
2. Identify and use different formats for network data and choose the appropriate one
3. Relate network and node metrics to real world phenomena like social capital and
boundary spanning individuals.
4. Obtain large scale data from well-known networks like Twitter and Facebook.
5. Interpret and synthesize the meaning of the results with respect to a question, goal, or
task.
Course delivery
Each class session will include a lecture component and, in some classes, instructor-led
case studies. Students will use NodeXL (and R). Every class will be followed by an
assignment that will be used to reinforce the concepts and tool-based skills covered in
class.
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. The following book will be used as the required text:
• Hansen, Derek, Shneiderman, Ben and Smith, Marc A. (2010). Analyzing Social
Media Networks with NodeXL: Insights from a Connected World, Burlington:
Elsevier Science. This is available as an electronic text book from with the
Gelman library system:
http://surveyor.gelman.gwu.edu/?q=Analyzing%20Social%20Medi
a%20Networks%20with%20NodeXL
Software used
1. NodeXL
2. R (primarily the sna package and igraph for visualization)
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
1
2
3
4
5
6
7
Topic
Network perspectives; types of networks;
Network analysis examples; Chapter 1 and 2
Mathematical foundations; graph theory; types of
graphs; Visualizing networks;
Network metrics; node level metrics and network
level metrics; dyads, cliques and subgroups
Data collection; collecting data from the internet;
Twitter and Facebook data pipes.
Cohesive Sub-Groups and teams; Using social
network data in hierarchical linear models
Analyzing Ego Networks; Brokerage & social
capital
Structural Equivalence and Block Modeling;
testing hypotheses
Assignment
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Other information
1. Students can expect to spend at least 5 hours per week outside the classroom. This
could vary depending on their prior preparation and background.
2. Students are expected to do their assigned readings before class
3. Assignments are to be turned in on the day they are due. Late assignments will not be
accepted.
4. It is important for all students to be familiar with and adhere to the GW Code of
Academic Integrity (http://www.gwu.edu/~ntegrity/code.html).
Decision Sciences Department
Business Analytics Program
DNSC 6217: Pricing and Revenue Management (1.5 cr)
Dr. Mehmet S. Altug
Course Description
Firms need to find answers for various questions that arise in the context of pricing such as:
Which sales channels should the firm use? How should a product be priced in different channels?
How can the firm prevent cannibalization across channels? How should prices be adjusted
throughout the season or after observing the initial demand?
Pricing and revenue management is concerned with having the right prices in place for all the
products a firm sells, to all its customers, through all their channels, all the time and is a tactical
decision. The most familiar example probably comes from the airline industry, where tickets for
the same flight may be sold at many different fares throughout the booking horizon depending on
product restrictions as well as the remaining time until departure and the number of unsold seats.
The use of such strategies has transformed the transportation and hospitality industries, and has
become increasingly important in retail, telecommunications, entertainment, financial services,
on-line advertising and manufacturing. Moreover, pricing and revenue management is a growing
practice in management consulting services and in software and IT development.
Through a combination of lecture notes, case studies, problem solving and guest speakers, the
course will review the main methodologies that are used in many of these areas. Most of the
topics covered in the course are either directly or indirectly related to pricing issues faced by
firms that have some degree of market power. Within the broader area of pricing theory, the
course places particular emphasis on tactical optimization of pricing and capacity allocation
decisions, tackled using quantitative models of consumer behavior and constrained optimization.
Pre-Requisites
I will assume that students feel comfortable with these topics in two areas:


Statistics: Basic understanding of probability, probability distributions, expected value
calculations and regression
Optimization: Some knowledge of spreadsheet modeling; linear optimization; how to
formulate these problems in excel and use solver to get a solution and interpret results
Course Objectives
1) To ensure that students learn to identify and exploit opportunities for revenue
management in different business contexts
2) To gain a deeper understanding of the fundamentals of managing prices and capacity in
the context of revenue optimization
3) To learn the quantitative models and techniques frequently used in pricing and revenue
management
Learning Objectives/Outcomes
1) Students will demonstrate knowledge of fundamental pricing and revenue management
concepts through different industry examples from reading assignments, case studies and
personal experience
2) Students will develop the following skills as well, which will be tested using homework
assignments and case studies:
-
Ability to identify business environments where revenue management is applicable and
can make an impact
Ability to formulate a revenue management problem
Ability to solve problems related to tactical optimization of pricing and capacity
allocation decisions using quantitative models of consumer behavior and constrained
optimization
Texts and Software
The recommended textbook for this course is “Pricing and Revenue Optimization”, by Robert L.
Phillips, Stanford University Press. As you will see in the “Course Schedule”, there is a list of
suggested readings from this book for various topics.
In some of the classes, we will be using Excel and its add-ins; hence you may all be required to
bring your laptop to those classes which will be announced the week before.
Grading
The grade will be based on:
Class participation: 20%
Individual assignments: 30%
Case study reports: 20%
Final exam: 30%
Class participation
Class discussion is an important part of the learning process. You may contribute to that in
various
i)
ii)
iii)
You will be assigned several case studies together with some discussion questions for
each case. While you are not required to submit a report for all the case studies, I
expect all of you to be prepared to discuss them in class. Sometimes I may start a
discussion and then call on one of you to lead it.
As you will notice, I may cover some of the topics by asking questions to understand
your points of view, and then use your answers as building blocks to reach a
conclusion or get the main message across. Some of those questions may require
precise answers, perhaps based on earlier lectures, while others may be quite openended.
We will also hold discussions based on the reading assignments.
Individual Assignments
There will be three homework assignments with problems that will require modeling and
analysis based on topics covered that week. While you may exchange ideas to better understand
the problem or how to approach it, you are required to work out the details and the complete
solution on your own.
Case study reports
You are required to submit a report for two of the case studies. You will be working in groups of
three, but will be required to submit only one report, listing all the names. These reports will be
graded for both content and presentation; they should not exceed single-spaced five pages
(excluding appendices).
Reading assignments
As listed below in the course schedule, I recommend that you read the chapters from PRO to be
better prepared for the upcoming lectures. I will also upload some articles on the subject and will
ask you to read them before coming to class which may be used to stimulate further discussion
on that week’s topic.
Final exam
There will be a take-home final exam that will be based on all the topics covered in class. This
will be an individual assignment where the majority of the questions will be problems similar in
nature to your individual assignments and you will have two days to complete it.
Due Dates
Deliverables must be turned 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.
Session
1
Tentative Course Schedule
Chapters
Subject/Topic
from
PRO
Introduction: Background; financial impact of
1, 2
pricing and revenue management; review of
basic price theory
Price response function; willingness-to-pay;
basic price optimization (Ch 3 of PRO)
2
Price differentiation; market segmentation (Ch 4
of PRO)
Consumer choice models
3
4
Pricing with constrained supply; variable pricing
(Ch 5 of PRO)
Mark-down optimization; determining the markdown schedule for short life cycle products; play
and debrief the retail pricing simulation exercise
Revenue Management concepts and systems;
booking control; nesting
5
6
7
Capacity allocation; optimal booking limits for
two-class problem
3.1.13.1.4
3.4.13.4.4
Reading assignment 1
Case 1 (discussion only)
4.1-4.4
4.6
3.2
5
Assignment 1
10
Reading assignment 2
Case 2 report
Assignment 2
6
Reading assignment 3
Case 3 report
Network management
7.1-7.2
7.5-7.6
8.1-8.3
Introduction to overbooking
9.1-9.3.1
Customized pricing
11.1-11.2
Pricing and revenue management and customer
acceptance; implementation issues
12
Guest speaker: TBD
Deliverable Due
Assignment 3
Case 4 (discussion only)
Reading assignment 4
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.
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.