Timeroom: Spring 2021

Displaying 251 - 260 of 1175 Results for: Level = All Graduate
Durham   Graduate School :: Analytics

DATA 897 (01) - Self Designed Analytics Thesis Lab II

Self-Designed Analytics Lab II

Credits: 3.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 53073
This is the second of a two course self-designed thesis sequence offered under the master's of science degree in analytics. The nature of the class is applied learning directly around a student directed analytic thesis project. The class requires competency in two areas for the successful completion of the course. Students will have completed the data collection, cleaning and management and created readable analytic files for the project of their choice in the first of the two course sequence. Students are primarily responsible to apply modern analytical tools and techniques like predictive modeling, segmentation, and network analysis etc. They are also required to write a formal 2000+ word report on the findings of the project. The report is expected to include modern data visualization synthesized with analysis results. Prereq: DATA 803.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed majors in section: ANALYTICS, ANALYTICS CERT, ANALYTICS PP
Instructors: Joanna Gyory
Start Date End Date Days Time Location
2/1/2021 5/11/2021 Hours Arranged PBLANE 216

DPP 905 (1ON) - Fiscal Management for Development Organizations

Fiscal Management

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 54071
Budgeting, goal setting, financial planning and financial analysis for development organizations.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed majors in section: COM DEV PLC PRT, DEV PLICY PRCT
Instructors: Robrecht Vanrijkel, Sanjeev Sharma
Start Date End Date Days Time Location
2/1/2021 5/11/2021 Hours Arranged ONLINE

DPP 982 (1ON) - Project Implementation and Monitoring

Proj. Implementation & Monitor

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 51887
Students will begin implementation activities in field placement communities. Regular progress reports ad online postings will be required. Prereq: DPP 980.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed majors in section: COM DEV PLC PRT, DEV PLICY PRCT
Instructors: Jolan Rivera, Sanjeev Sharma
Start Date End Date Days Time Location
2/1/2021 5/11/2021 Hours Arranged ONLINE

DPP 990 (01) - Independent Study

Independent Study

Credits: 1.0 to 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 55404
Under the guidance of an MCD Faculty member, the Independent Study Course (DPP 990) provides students with the opportunity to study a unique topic in-depth that is not offered as a traditional course. Often this topic is a relevant aspect of their capstone project which they wish to explore in more depth.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 6 credits.
Instructors: Michael Swack, Sanjeev Sharma
Start Date End Date Days Time Location
2/1/2021 5/11/2021 Hours Arranged TBA
Durham   Paul College of Business&Econ :: Decision Sciences

DS 805 (01) - Statistical Learning

Statistical Learning

Credits: 3.0
Term: Spring 2021 - E-term III (01/19/2021 - 03/12/2021)
Grade Mode: Letter Grading
Class Size:   24  
CRN: 56156
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, model diagnostics, cross-validation techniques, penalized regression methods such as ridge and LASSO, nonlinear models, logistic regression, random forests, 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 with discussion, and hands on data analyses. Prereq: DS 803.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: Burcu Eke Rubini
Start Date End Date Days Time Location
1/19/2021 3/12/2021 M 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 806 (01) - Optimization Methods I

Optimization Methods I

Credits: 3.0
Term: Spring 2021 - E-term III (01/19/2021 - 03/12/2021)
Grade Mode: Letter Grading
Class Size:   24  
CRN: 56157
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.)
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: Melda Ormeci Matoglu
Start Date End Date Days Time Location
1/19/2021 3/12/2021 R 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 807 (01) - Modeling Unstructured Data

Unstructured Data

Credits: 3.0
Term: Spring 2021 - E-term IV (03/22/2021 - 05/13/2021)
Grade Mode: Letter Grading
Class Size:   24  
CRN: 56158
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 generalized linear models, decision trees for discrete data, k-means clustering, mixture models, and topic models. The course integrates numerous case studies to demonstrate practical approaches to analyzing large unstructured collections of data. Application areas include Marketing (Yelp and Trip Advisor reviews), Human Resources (healthcare plan analysis), Social media (Twitter, YouTube, and Instagram). The course delivery will be a mix of lectures, readings with discussion, and hands-on data analysis. Prereq: DS 805.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: Burcu Eke Rubini
Start Date End Date Days Time Location
3/22/2021 5/13/2021 M 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 808 (01) - Optimization Methods II

Optimization Methods II

Credits: 3.0
Term: Spring 2021 - E-term IV (03/22/2021 - 05/13/2021)
Grade Mode: Letter Grading
Class Size:   24  
CRN: 56159
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. Prereq: DS 806.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: Seyed Ali Hojjat
Start Date End Date Days Time Location
3/22/2021 5/13/2021 R 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 809 (01) - Time Series Analysis

Time Series Analysis

Credits: 3.0
Term: Spring 2021 - E-term III (01/19/2021 - 03/12/2021)
Grade Mode: Letter Grading
Class Size:   24  
CRN: 56160
The course is designed to introduce analytical techniques needed in the analysis of temporal data in various business disciplines. The first half of the course focuses on traditional stationary univariate and multivariate time series models and the second half will focus on non-stationary (state space) models. Both classic and Bayesian inference points of view are considered. Some examples of the business application areas include demand forecasting in ride-sharing platforms, stochastic volatility modeling of financial indexes, mortgage default risk assessment, online webpage click-rate modeling, customer demand forecasting, and call center volume forecasting for optimal staffing. Prereq: DS 803.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: Tevfik Aktekin
Start Date End Date Days Time Location
1/19/2021 3/12/2021 W 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 810 (01) - Enterprise Level Analytics

Enterprise Analytics

Credits: 3.0
Term: Spring 2021 - E-term IV (03/22/2021 - 05/13/2021)
Grade Mode: Letter Grading
Class Size:   24  
CRN: 56161
This course is designed to be a capstone experience with emphasis on the integration of materials covered in prior courses. In addition, the course provides students with the necessary knowledge and skills to manage vast quantities of business data. By the end of the course students will understand how big data systems are developed and used to support the operations and decision-making functions within a business organization. The course begins with a framework for understanding big data systems are developed and used. It continues with an emphasis on "experiential learning" where students build big data systems using contemporary technologies such as Hadoop, MapReduce, Spark etc. Finally, students learn how to analyze large-scale data sets and reveal valuable business insights. As part of the capstone experience, students develop these systems in groups, make several presentations and discuss cases during the semester. Prereq: DS 801, DS 804, and DS 807.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Instructors: Phani Kidambi
Start Date End Date Days Time Location
3/22/2021 5/13/2021 T 5:40pm - 9:15pm PBLANE 216