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Course Descriptions
 
Graduate
 
Fall 2008
QTM8400 DATA & DECISION MODELING 4.00 credits
  There will not be a Saturday meeting date for this section of QTM8400 QTM8400 Data and Decision Modeling 4 Credits This course is an introduction to basic quantitative tools that enable managers to analyze data and make informed decisions. Topics include descriptive analysis of survey data, introductory probability, decision analysis, sampling and sampling distributions, hypothesis testing, simple and multiple regression, simulation, and optimization. Case studies drawn from marketing, finance, and operations management, illustrate the use of these quantitative tools in applied context. The course requires use of spreadsheets and statistical, optimization, and simulation software. This course is equivalent to QTM7010 - Statistics and QTM8200 - Applied Decision Models Prerequisite: NONE

Summer II 2008
QTM7580 INDEPENDENT RESEARCH 3.00 credits
  XXX7580 Independent Research ******Independent research is available for all academic divisions.Registration is manual for students through Graduate Programs and Student Affairs****** Independent Research provides an opportunity to conduct in-depth research in areas of a student's own specific interest. Students may undertake Independent Research for academic credit with the approval of a student-selected faculty advisor, the appropriate division chair, and Graduate Programs and Student Affairs. Please note that a student is responsible for recruiting a faculty advisor through the student's own initiative and obtain the advisor's prior consent/commitment before applying for an independent research project. Authorization for such a project requires submission of a formal proposal written in accordance with standards set forth by the Graduate School. The research project normally carries 1.5 or 3 credits. For more information and a proposal outline please visit: www.babson.edu/grad/gpsa

Summer I 2008
QTM8400 DATA & DECISION MODELING 4.00 credits
  Saturday Meeting Date: May 31st 8AM - 3PM QTM8400 Data and Decision Modeling 4 Credits This course is an introduction to basic quantitative tools that enable managers to analyze data and make informed decisions. Topics include descriptive analysis of survey data, introductory probability, decision analysis, sampling and sampling distributions, hypothesis testing, simple and multiple regression, simulation, and optimization. Case studies drawn from marketing, finance, and operations management, illustrate the use of these quantitative tools in applied context. The course requires use of spreadsheets and statistical, optimization, and simulation software. This course is equivalent to QTM7010 - Statistics and QTM8200 - Applied Decision Models Prerequisite: NONE

QTM9501 BUSINESS FORECASTING 1.50 credits
  Meeting Dates and Times: Friday May 30 (8:30 - 3:30), Saturday May 31 (8:30 - 3:30), and Friday June 6 (8:30 - 12:30) SUMMER I 2008 QTM9501: BUSINESS FORECASTING Credits: 1.5 Cost: $1561.50 Payment for This Course is Due: May 14, 2008 Registering for QTM9501: Registration for Intensive Electives will take place through online course registration. Students will be notified of Summer 2008 registration dates via their Babson email account. If online add/drop is closed and a space becomes available in this course, students must email intensiveelectives@babson.edu to register for it. These emails will be processed on a first-come-first-serve basis and we will not retain emails for future consideration. Professor: Norean Sharpe Meeting Dates and Times: Friday May 30 (8:30 - 3:30), Saturday May 31 (8:30 - 3:30), and Friday June 6 (8:30 - 12:30) Time Conflicts: Students are responsible to check the meeting dates for all Intensive Electives. If a student is registered for Intensive Electives that have conflicting dates and times, the Registrar's Office will drop one of the these courses. Intensive Electives Limit: The maximum number of Intensive Electives a student may take while at Babson is 4. It is the student's responsibility to adhere to this policy. If the student exceeds this limit, we will drop that student from an Intensive Elective of our choosing. Meeting Room: TBA Dropping QTM9501: If online add/drop is closed, students can email intensiveelectives@babson.edu to drop this course. Students can drop this course so long as an email is sent to intensiveelectives@babson.edu before the end of the day (11:59PM) of the first class meeting. Students must email from their Babson email account. Capacity: 36 This course will introduce elementary time series models and discuss advanced forecasting methods in the context of real business data and decision-making situations." The objectives of the course are to provide experience in using time series data (e.g., sales, profits, stock prices, economic indicators, industry sector indicators) to explain the impact of various internal and external factors and predict future trends; to provide a framework for comparing alternative forecasting models for validity, accuracy, and feasibility; to enhance an appreciation for the limitations of forecasting models; to provide exposure and experience in using statistical software to develop forecasting models; and to develop skills at communicating statistical concepts, methods, results, and inferences effectively in a managerial context." Teamwork and professional presentation of analysis and recommendations will be required during this course. Prerequisite: QTM8400 or (QTM7010 and QTM7020 or QTM7010 and QTM8200) or completion of the One Year, Two Year or Fast Track modules
Spring 2008
MIS7580 INDEPENDENT RESEARCH 3.00 credits
  XXX7580 Independent Research ******Independent research is available for all academic divisions.Registration is manual for students through Graduate Programs and Student Affairs****** Independent Research provides an opportunity to conduct in-depth research in areas of a student's own specific interest. Students may undertake Independent Research for academic credit with the approval of a student-selected faculty advisor, the appropriate division chair, and Graduate Programs and Student Affairs. Please note that a student is responsible for recruiting a faculty advisor through the student's own initiative and obtain the advisor's prior consent/commitment before applying for an independent research project. Authorization for such a project requires submission of a formal proposal written in accordance with standards set forth by the Graduate School. The research project normally carries 1.5 or 3 credits. For more information and a proposal outline please visit: www.babson.edu/grad/gpsa

QTM7571 BUSINESS INTEL & APPLIED DATA MINING 3.00 credits
  QTM7571 Business Intelligence and Applied Data Mining (formerly Applied Data Mining) This course will examine the methods and challenges faced in extracting meaningful information from large databases and the growing number of applications for these techniques. Students will learn new techniques for data gathering and data analysis including neural nets, correlative webs, ruleset development using classification nodes, and cluster analysis . These analysis techniques will be used to understand how to turn the information into a knowledge base, to synthesize enormous amounts of data, and to identify new or surprising patterns which can't be found using classical statistical analysis. We will analyze databases from marketing, finance, accounting (auditing) as well as genomic and on-line clickstreams data and online surveys. Instructional modes (e.g. lecture, laboratory, seminar, internship, film, etc.): The course will include hybrid delivery where some classes will take place online and others will be face-to-face. The course will utilize a hands-on approach to understanding the methods of data mining, as well as understanding its links to Statistics and Information Systems and its Applications in business, finance, economics, physical and social science. Lectures, case presentations, computer labs and guest speakers will be used. Students will learn Clementine, a widely used data mining software package. Prerequisite: QTM7010 or QTM8400 or completion of one-year or two-year modules

QTM7580 INDEPENDENT RESEARCH 3.00 credits
  XXX7580 Independent Research ******Independent research is available for all academic divisions.Registration is manual for students through Graduate Programs and Student Affairs****** Independent Research provides an opportunity to conduct in-depth research in areas of a student's own specific interest. Students may undertake Independent Research for academic credit with the approval of a student-selected faculty advisor, the appropriate division chair, and Graduate Programs and Student Affairs. Please note that a student is responsible for recruiting a faculty advisor through the student's own initiative and obtain the advisor's prior consent/commitment before applying for an independent research project. Authorization for such a project requires submission of a formal proposal written in accordance with standards set forth by the Graduate School. The research project normally carries 1.5 or 3 credits. For more information and a proposal outline please visit: www.babson.edu/grad/gpsa

QTM8200 APPLIED DECISION MODELS 2.00 credits
  QTM 8200 Applied Decision Models This is a core class for the Evening MBA Program only. Students must complete or have advanced standing credit or pass a waiver exam in QTM7010, Statistics, in order to take this class. The focus of this class is on developing decision support models and using these tools to enhance decision making. It is an application-oriented introduction to the modeling techniques used to structure the way we think about managerial decision situations. Methodologies considered are decision analysis, simulation, optimization, and sensitivity analysis. Spreadsheet models are developed with applications to finance, operations management, logistics, and resource allocation. Some of the classes will be distance-learning classes and some will be held on campus. The asynchronous distance-learning classes provide the flexibility to learn at one's convenience. The on-campus classes will provide instantaneous feedback and allow interaction with fellow students and the professor. Prerequisite: QTM7010

QTM8400 DATA & DECISION MODELING 4.00 credits
  Saturday Meeting Date: 4/26 QTM8400 Data and Decision Modeling 4 Credits This course is an introduction to basic quantitative tools that enable managers to analyze data and make informed decisions. Topics include descriptive analysis of survey data, introductory probability, decision analysis, sampling and sampling distributions, hypothesis testing, simple and multiple regression, simulation, and optimization. Case studies drawn from marketing, finance, and operations management, illustrate the use of these quantitative tools in applied context. The course requires use of spreadsheets and statistical, optimization, and simulation software. This course is equivalent to QTM7010 - Statistics and QTM8200 - Applied Decision Models *****A total of 8 hours of Saturday meetings is required, Dates to Follow.***** Prerequisite: NONE

Winter 2008
QTM9510 OPTIMIZATION 1.50 credits
  Registering for QTM9510: Registration for Intensive Electives will take place through online course registration. After add/drop closes on December 11, if a space becomes available in this course, students must email intensiveelectives@babson.edu to register for it. These emails will be processed on a first-come-first-serve basis and we will not retain emails for future consideration. Professor: Lori Houghtalen Meeting Dates and Times: Friday January 11 (8:30 - 3:30), Saturday January 12 (8:30 - 3:30), and Friday January 18 (8:30 - 12:30) Time Conflicts: Students are responsible to check the meeting dates for all Intensive Electives. If a student is registered for Intensive Electives that have conflicting dates and times, the Registrar's Office will drop one of the these courses. Meeting Room: TBA Drop Date: To drop an Intensive Elective after add/drop ends on December 11, students must email intensiveelectives@babson.edu before midnight of the day of the first class meeting. This course provides an introduction to optimization models and their applications to a variety of business decision problems. Linear, non-linear, integer, and network models will be discussed. Emphasis will be placed on understanding the relevant applications for various models, the strengths and limitations associated with the models, using software tools to solve optimization problems, and interpreting and analyzing solution results. In-class analysis of problems and/or cases will be utilized to reinforce material covered in lecture. Teamwork outside the class will be required in order to complete assignments. Evaluation will be based on both individual and team performance.



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