Timeroom: Spring 2024

Displaying 1201 - 1210 of 4570 Results for: %20Level = All%20Graduate
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 775 (03) - Corporate Project Experience

Corporate Project Experience

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 52057
Provides real-life experience in organizations. Work in groups on information systems and/or business analytics projects identified by sponsoring organizations. Integrate concepts and skills learned in prior business, analytics, and information systems courses. Learn project management concepts, work with project management tools, and use presentation techniques. Two ISBA Electives required prior to taking this course.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 673 with minimum grade of C-
Only listed campus in section: Durham
Only listed classes in section: Senior
Only listed majors in section: BUSADM:INFSYSAN
Attributes: Writing Intensive Course
Instructors: Peter Zaimes
Start Date End Date Days Time Location
1/23/2024 5/6/2024 F 9:10am - 12:00pm PCBE G85
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 805 (01) - Statistical Learning

Statistical Learning

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53431
This course introduces students to statistical tools for modeling and identifying patterns in complex data sets. The goal of statistical learning is to develop predictions informed by data. Topics to be covered include Gaussian linear models, cross-validation techniques, penalized regression methods such as ridge and LASSO, nonlinear models, logistic regression, tree-based models including random forests, bagging, and boosting, and support vector machines. Application areas include Marketing (e.g., effectiveness of advertising and customer satisfaction), Financial Economics (valuation), and Operations Management (resource allocation). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analyses.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 803
Instructors: Burcu Eke Rubini
Start Date End Date Days Time Location
1/23/2024 3/15/2024 R 2:10pm - 5:30pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 805 (02) - Statistical Learning

Statistical Learning

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   16  
CRN: 56832
This course introduces students to statistical tools for modeling and identifying patterns in complex data sets. The goal of statistical learning is to develop predictions informed by data. Topics to be covered include Gaussian linear models, cross-validation techniques, penalized regression methods such as ridge and LASSO, nonlinear models, logistic regression, tree-based models including random forests, bagging, and boosting, and support vector machines. Application areas include Marketing (e.g., effectiveness of advertising and customer satisfaction), Financial Economics (valuation), and Operations Management (resource allocation). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analyses.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 803
Instructors: Burcu Eke Rubini
Start Date End Date Days Time Location
1/23/2024 3/15/2024 T 2:10pm - 5:30pm PCBE G45
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 806 (01) - Optimization Methods I

Optimization Methods I

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53432
This course introduces students to fundamental quantitative methods for modeling, analyzing, and determining the best course of action in complex decision-making situations. Topics to be covered include decision trees and tables, price of uncertainty, utility theory, linear programming, LP sensitivity analysis, and network flow optimization. Application areas include Marketing and Operations management (e.g., advertising, production and inventory planning, project or personnel scheduling, shipping and distribution, routing, ride matching, etc.)
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Instructors: Melda Ormeci Matoglu
Start Date End Date Days Time Location
1/23/2024 3/15/2024 M 2:10pm - 5:30pm NESM 125
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 806 (02) - Optimization Methods I

Optimization Methods I

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 56833
This course introduces students to fundamental quantitative methods for modeling, analyzing, and determining the best course of action in complex decision-making situations. Topics to be covered include decision trees and tables, price of uncertainty, utility theory, linear programming, LP sensitivity analysis, and network flow optimization. Application areas include Marketing and Operations management (e.g., advertising, production and inventory planning, project or personnel scheduling, shipping and distribution, routing, ride matching, etc.)
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Instructors: Melda Ormeci Matoglu
Start Date End Date Days Time Location
1/23/2024 3/15/2024 W 5:40pm - 9:00pm PCBE 235
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 807 (01) - Modeling Unstructured Data

Unstructured Data

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53433
This course introduces students to statistical and machine learning tools for modeling unstructured data; including emails, documents, text messages, and social media data. Topics to be covered include text mining, clustering, mixture models, deep learning, and topic models. The course integrates numerous applications to demonstrate practical approaches to analyzing large unstructured collections of data. Application areas include Marketing (Yelp and Trip Advisor reviews), Human Resources (health care plan analysis), Social Media (Twitter, YouTube, and Instagram). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analysis.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 805
Instructors: Courtney Paulson
Start Date End Date Days Time Location
3/25/2024 5/17/2024 R 2:10pm - 5:30pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 807 (02) - Modeling Unstructured Data

Unstructured Data

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 56834
This course introduces students to statistical and machine learning tools for modeling unstructured data; including emails, documents, text messages, and social media data. Topics to be covered include text mining, clustering, mixture models, deep learning, and topic models. The course integrates numerous applications to demonstrate practical approaches to analyzing large unstructured collections of data. Application areas include Marketing (Yelp and Trip Advisor reviews), Human Resources (health care plan analysis), Social Media (Twitter, YouTube, and Instagram). The course delivery will be a mix of lectures, readings/podcasts with discussion, and hands-on data analysis.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 805
Instructors: Courtney Paulson
Start Date End Date Days Time Location
3/25/2024 5/17/2024 W 8:10am - 11:30am NESM G13
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 808 (01) - Optimization Methods II

Optimization Methods II

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53434
This course introduces students to more advanced concepts and modeling techniques in mathematical programming. Topics to be covered include integer programming, nonlinear programming, multi-objective optimization, goal programming, and Monte Carlo simulation. Application areas include Marketing (e.g., pricing and revenue optimization), Finance (capital budgeting and portfolio optimization), and Operations management (e.g., production and inventory planning, shipping and distribution, routing, location selection, etc.). The course delivery will be a mix of lectures, hands-on problem solving, and case discussions.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 806
Instructors: Alison Chen
Start Date End Date Days Time Location
3/25/2024 5/17/2024 M 5:40pm - 9:00pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 808 (02) - Optimization Methods II

Optimization Methods II

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 56835
This course introduces students to more advanced concepts and modeling techniques in mathematical programming. Topics to be covered include integer programming, nonlinear programming, multi-objective optimization, goal programming, and Monte Carlo simulation. Application areas include Marketing (e.g., pricing and revenue optimization), Finance (capital budgeting and portfolio optimization), and Operations management (e.g., production and inventory planning, shipping and distribution, routing, location selection, etc.). The course delivery will be a mix of lectures, hands-on problem solving, and case discussions.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 806
Instructors: Alison Chen
Start Date End Date Days Time Location
3/25/2024 5/17/2024 T 2:10pm - 5:30pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences
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DS 809 (01) - Time Series Analysis

Time Series Analysis

Credits: 3.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 53435
The course is designed to introduce analytical techniques needed in the estimation and analysis of temporal (time series) data in various business disciplines. The course focuses on theoretical and application aspects of stationary/non-stationary univariate as well as multivariate time series models. Emphasis will be given to topics such as time series regression, random walks, ARIMA/SARIMA processes, ARCH/GARCH for modeling conditional volatility, Vector ARMA models, and transfer functions. Modern software implementation is fully integrated into the course. Some examples of the business application areas include demand forecasting, financial asset return modeling, stochastic volatility modeling of financial indexes and securities, mortgage default risk assessment, online webpage click-rate modeling, market share modeling.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 803
Instructors: David Reynolds
Start Date End Date Days Time Location
1/23/2024 3/15/2024 T 5:40pm - 9:00pm PBLANE 216