Timeroom: Spring 2024

Displaying 921 - 930 of 3620 Results for: Campus = Durham
Durham   Paul College of Business&Econ :: Decision Sciences

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
Durham   Paul College of Business&Econ :: Decision Sciences

DS 809 (02) - 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: 56836
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 2:10pm - 5:30pm 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 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 53436
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 801 and DS 804 and DS 805
Start Date End Date Days Time Location
3/25/2024 5/17/2024 W 5:40pm - 9:00pm PCBE 235
Durham   Paul College of Business&Econ :: Decision Sciences

DS 810 (02) - Big Data and AI: Strategy and Analytics

Big Data

Credits: 3.0
Term: Spring 2024 - Term 4 (03/25/2024 - 05/17/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 56837
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): DS 801 and DS 804 and DS 805
Start Date End Date Days Time Location
3/25/2024 5/17/2024 R 5:40pm - 9:00pm PCBE 215
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 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 54134
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 12 credits.
Cross listed with : DS 772.01
Start Date End Date Days Time Location
1/23/2024 5/6/2024 T 5:10pm - 8:00pm PCBE 185
Final Exam 5/14/2024 5/14/2024 T 6:00pm - 8:00pm PCBE 185
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 543 (01) - Introduction to Digital Systems

Intro to Digital Systems

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   12  
CRN: 50588
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Only listed colleges in section: Engineering&Physical Sciences
Instructors: Qiaoyan Yu
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 1:10pm - 2:00pm PARS N104
1/23/2024 5/6/2024 M 3:10pm - 5:00pm KING S216
Final Exam 5/15/2024 5/15/2024 W 3:30pm - 5:30pm PARS N104
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 543 (04) - Introduction to Digital Systems

Intro to Digital Systems

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   12  
CRN: 50952
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Only listed colleges in section: Engineering&Physical Sciences
Instructors: Qiaoyan Yu
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 1:10pm - 2:00pm PARS N104
1/23/2024 5/6/2024 T 3:40pm - 5:30pm KING S216
Final Exam 5/15/2024 5/15/2024 W 3:30pm - 5:30pm PARS N104
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 548 (01) - Electronic Design I

Electronic Design I

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 50591
Introduction to electronic design for analog signal processing. Basic Concepts of Semiconductor Materials (electrons and holes, n-type and p-type semiconductors), Diodes (Modeling, Biasing, Zener Diodes, and Rectifier Circuits), FETs (Device Structure, Modes of Operation, and I-V Characteristics), BJTs (Device Structure, Modes of Operation, and I-V Characteristics), Transistor Amplifiers (Biasing a Transistor, Small-Signal Modeling, and Configurations), Operational Amplifier circuits for amplification and filtering. Lab
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): ECE 541
Only listed colleges in section: Engineering&Physical Sciences
Instructors: Wayne Smith
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 10:10am - 11:00am KING N343
1/23/2024 5/6/2024 M 1:10pm - 3:00pm KING S245
Final Exam 5/15/2024 5/15/2024 W 10:30am - 12:30pm PARS N104
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 548 (02) - Electronic Design I

Electronic Design I

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 50592
Introduction to electronic design for analog signal processing. Basic Concepts of Semiconductor Materials (electrons and holes, n-type and p-type semiconductors), Diodes (Modeling, Biasing, Zener Diodes, and Rectifier Circuits), FETs (Device Structure, Modes of Operation, and I-V Characteristics), BJTs (Device Structure, Modes of Operation, and I-V Characteristics), Transistor Amplifiers (Biasing a Transistor, Small-Signal Modeling, and Configurations), Operational Amplifier circuits for amplification and filtering. Lab
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): ECE 541
Only listed colleges in section: Engineering&Physical Sciences
Instructors: Wayne Smith
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 10:10am - 11:00am KING N343
1/23/2024 5/6/2024 T 2:10pm - 4:00pm KING S245
Final Exam 5/15/2024 5/15/2024 W 10:30am - 12:30pm PARS N104
Durham   Engineering&Physical Sciences :: Electrical&Comp Engineering

ECE 548 (03) - Electronic Design I

Electronic Design I

Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 50594
Introduction to electronic design for analog signal processing. Basic Concepts of Semiconductor Materials (electrons and holes, n-type and p-type semiconductors), Diodes (Modeling, Biasing, Zener Diodes, and Rectifier Circuits), FETs (Device Structure, Modes of Operation, and I-V Characteristics), BJTs (Device Structure, Modes of Operation, and I-V Characteristics), Transistor Amplifiers (Biasing a Transistor, Small-Signal Modeling, and Configurations), Operational Amplifier circuits for amplification and filtering. Lab
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): ECE 541
Only listed colleges in section: Engineering&Physical Sciences
Instructors: Wayne Smith
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 10:10am - 11:00am KING N343
1/23/2024 5/6/2024 R 2:10pm - 4:00pm KING S245
Final Exam 5/15/2024 5/15/2024 W 10:30am - 12:30pm PARS N104