Timeroom: Spring 2021

Displaying 1271 - 1280 of 4581 Results for: Attributes%5B0%5D = EUNH
Durham   Paul College of Business&Econ :: Decision Sciences

DS 775 (04) - Corporate Project Experience

Corporate Project Experience

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   28  
CRN: 57353
Provides real-life experience in organizations. Work in groups on information systems and/or projects identified by sponsoring organizations. Integrate concepts and skills learned in prior business and technology courses. Learn project management concepts, work with project management tools, and use presentation techniques. Prereq: senior standing, DS 773, two additional Information Systems & Business Analytics Option courses.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed campus in section: Durham
Only listed classes in section: Senior
Only listed majors in section: BUSADM:INFSYSAN
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 F 8:10am - 10:00am PCBE 215
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: STAFF
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: STAFF
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: STAFF
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: STAFF
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: STAFF
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: STAFF
Start Date End Date Days Time Location
3/22/2021 5/13/2021 T 5:40pm - 9:15pm PBLANE 216
Durham   Paul College of Business&Econ :: Decision Sciences

DS 898 (02) - Topics in Business Analytics

Top/E-Business

Credits: 3.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   3  
CRN: 57098
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.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 12 credits.
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 T 5:10pm - 8:00pm PCBE 115
Additional Course Details: 

Covers the concepts, tools, and strategies for understanding the challenges and exploiting the opportunities associated with e-commerce/e-business. Provides an understanding of the technology platform and its components. Additional material covers various models of e-commerce/e-business and its impacts on the firm's performance. 

Durham   Paul College of Business&Econ :: Decision Sciences

DS 898 (1SY) - Topics in Business Analytics

Top/Forecasting Analytics

Credits: 3.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 57097
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.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 12 credits.
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 W 5:10pm - 8:00pm ONLINE
Additional Course Details: 

The course focuses on Predictive Analytics. Businesses and organizations need to be able to forecast effectively in order to make decisions. Students learn the background necessary to develop forecasts for real-world business situations. An applied, hands-on approach is used in the course. Students learn and use SAS to analyze data and fit models. Topics include regression analysis in forecasting, model building, residual checking, analysis of seasonal and cyclical trends, and times series models. 

Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 543 (01) - Introduction to Digital Systems

Intro to Digital Systems

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   12  
CRN: 50719
Fundamental analysis and design principles. Number systems, codes, Boolean algebra, and combinational and sequential digital circuits. Lab: student-built systems using modern integrated circuit technology and an introductory design session on a CAD workstation. Lab.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Equivalent(s): EE 543
Only listed campus in section: Durham, Manchester
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MWF 1:10pm - 2:00pm DEM 112
2/1/2021 5/11/2021 M 3:10pm - 5:00pm KING S216