Timeroom: Spring 2023

Displaying 261 - 270 of 1182 Results for: Level = All Graduate

DPP 983 (1ON) - Project Evaluation

Project Evaluation

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Spring 2023 - E-term IV (03/20/2023 - 05/11/2023)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 55064
This semester students will conduct an evaluation of their project and manage closure processes. At the end students will submit a final written report and present it to the faculty and peers. This final project and the final report detailing the project will serve as the capstone course of the program. Prereq: DPP 980.
Only listed majors in section: COM DEV PLC PRT, DEV PLICY PRCT
Instructors: Jolan Rivera, Sanjeev Sharma
Start Date End Date Days Time Location
3/20/2023 5/11/2023 Hours Arranged ONLINE

DPP 990 (01) - Independent Study

Independent Study

Credits: 1.0 to 4.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 53886
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.
Repeat Rule: May be repeated for a maximum of 6 credits.
Instructors: Michael Swack, Sanjeev Sharma
Start Date End Date Days Time Location
1/24/2023 5/8/2023 Hours Arranged TBA
Durham   Paul College of Business&Econ :: Decision Sciences

DS 805 (01) - Statistical Learning

Statistical Learning

Credits: 3.0
Term: Spring 2023 - E-term III (01/17/2023 - 03/10/2023)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 54099
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. Prereq: DS 803.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Instructors: Burcu Eke Rubini
Start Date End Date Days Time Location
1/17/2023 3/10/2023 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 2023 - E-term III (01/17/2023 - 03/10/2023)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 54100
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
1/17/2023 3/10/2023 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 2023 - E-term IV (03/20/2023 - 05/11/2023)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 54101
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. Prereq: DS 805.
Instructors: Burcu Eke Rubini
Start Date End Date Days Time Location
3/20/2023 5/11/2023 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 2023 - E-term IV (03/20/2023 - 05/11/2023)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 54102
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.
Instructors: Alison Chen
Start Date End Date Days Time Location
3/20/2023 5/11/2023 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 2023 - E-term III (01/17/2023 - 03/10/2023)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 54103
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. Prereq: DS 803.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Instructors: Tevfik Aktekin
Start Date End Date Days Time Location
1/17/2023 3/10/2023 W 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 810 (01) - Big Data and AI: Strategy and Analytics

Big Data

Credits: 3.0
Term: Spring 2023 - E-term IV (03/20/2023 - 05/11/2023)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 54104
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 knowledge and skills to manage and model vast quantities of data for business analytics. The course covers deep neural networks and large-scale data processing using ecosystems of computing tools such as TensorFlow and Apache Spark. Students learn how to store, analyze, and derive insights from large-scale datasets and develop an understanding of the implications of deep learning for business. As a part of the capstone experience, students complete a team project that focuses on using big data and artificial intelligence for business insights, and present and discuss their work. Prereq: DS 801, DS 804, DS 805.
Start Date End Date Days Time Location
3/20/2023 5/11/2023 W 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 898 (01) - Topics in Business Analytics

Top/Ped Analy: Regress Model

Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 55228
Special Topics; may be repeated. Pre- and co-requisite courses vary. Please consult time and room schedule for the specific 898 topics section you are interested in for details.
Section Comments: Full Title: Top/Predictive Analytics: Regression Model
Repeat Rule: May be repeated for a maximum of 12 credits.
Start Date End Date Days Time Location
1/24/2023 5/8/2023 M 5:10pm - 8:00pm PCBE 215
Durham   Paul College of Business&Econ :: Decision Sciences

DS 898 (02) - Topics in Business Analytics

Top/E-Business

Credits: 3.0
Term: Spring 2023 - Full Term (01/24/2023 - 05/08/2023)
Grade Mode: Letter Grading
Class Size:   3  
CRN: 54439
Special Topics; may be repeated. Pre- and co-requisite courses vary. Please consult time and room schedule for the specific 898 topics section you are interested in for details.
Repeat Rule: May be repeated for a maximum of 12 credits.
Instructors: Inchan Kim
Start Date End Date Days Time Location
1/24/2023 5/8/2023 T 5:10pm - 8:00pm PCBE 235
Additional Course Details: 

This course studies diverse ebusiness firms and how they are successful utilizing technology, data, and analytics.