Timeroom: Fall 2023

Displaying 91 - 100 of 145 Results for: Subject = MATH
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 647 (01) - Complex Analysis for Applications

Complex Analysis for Applictns

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 15941
Complex numbers, analytic functions, Cauchy-Riemann equations, conformal mapping, contour integration, Cauchy's integral formula, infinite series, residue calculus, Fourier and Laplace transforms. Prereq: MATH 528. (Not offered for credit if credit is received for MATH 788.)
Mutual Exclusion : MATH 788
Instructors: Marianna Shubov
Start Date End Date Days Time Location
8/28/2023 12/11/2023 TR 9:40am - 11:00am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 696 (01) - Independent Study

Independent Study

Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   1  
CRN: 14350
Individual projects of study developed by the student and a faculty sponsor. Intended for students with superior scholastic achievement. May be taken as writing intensive. Prereq: a written proposal, including goals and assessment, endorsed by a faculty sponsor and approved by the department chairperson.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Repeat Rule: May be repeated for a maximum of 8 credits.
Equivalent(s): MATH 696W
Instructors: Donald Hadwin
Start Date End Date Days Time Location
8/28/2023 12/11/2023 Hours Arranged TBA
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 700 (01) - Introduction to Mathematics Education

Intro to Mathematics Education

Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   13  
CRN: 10747
General background information about mathematics education, such as theories of learning and teaching mathematics, mathematics curricula, classroom management, and techniques for the teaching and learning of mathematics that are common to all levels of mathematics education K-12. Prereq: MATH 426 and EDUC 500; or permission.
Cross listed with : MATH 800.01
Classes not allowed in section: Freshman, Sophomore
Instructors: Orly Buchbinder
Start Date End Date Days Time Location
8/28/2023 12/11/2023 MW 4:10pm - 6:00pm KING W387
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 703 (01) - Teaching of Mathematics in Grades K-5

Teaching of Mathematics, K-5

Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 12947
Methods of teaching mathematics at the elementary school level; uses of technology, manipulatives, models, and diagrams; developing unit and lesson plans; assessment ; instructional formats; teaching reading and writing in mathematics. Prereq: MATH 621 (or MATH 601); or permission.
Section Comments: Teaching of Mathematics in Grades K-5
Cross listed with : MATH 803.01
Classes not allowed in section: Freshman, Sophomore
Instructors: Anne Wallace
Start Date End Date Days Time Location
8/28/2023 12/11/2023 MW 4:40pm - 6:30pm KING N310
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 734 (01) - Statistical Computing

Statistical Computing

Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 14965
This is a course on statistics-oriented programming and common computational methodologies used in statistics. Students will learn principles and techniques of sample-splitting, cross-validation, simulation, bootstrap, and optimization, and how to implement them in R. The students will gain experience of reading/modifying, writing and debugging code, and how to speed up computation. Prereq: MATH 738 or MATH 739.
Cross listed with : MATH 834.01
Classes not allowed in section: Freshman, Sophomore
Instructors: Qi Zhang
Start Date End Date Days Time Location
8/28/2023 12/11/2023 MWF 8:10am - 9:30am KING N310
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 734 (02) - Statistical Computing

Statistical Computing

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 14966
This is a course on statistics-oriented programming and common computational methodologies used in statistics. Students will learn principles and techniques of sample-splitting, cross-validation, simulation, bootstrap, and optimization, and how to implement them in R. The students will gain experience of reading/modifying, writing and debugging code, and how to speed up computation. Prereq: MATH 738 or MATH 739.
Cross listed with : MATH 834.02
Classes not allowed in section: Freshman, Sophomore
Instructors: Qi Zhang
Start Date End Date Days Time Location
8/28/2023 12/11/2023 MWF 8:10am - 9:30am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 737 (01) - Statistical Methods for Quality Improvement and Design

Stat Methods for QI & Design

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 15996
Six Sigma is a popular, data-focused methodology used worldwide by organizations to achieve continuous improvement of their existing processes, products and services or to design new ones. This course provides a thorough introduction to the Six Sigma principles, methods, and applications for continuous improvement (DMAIC process) and an overview of Design for Six Sigma (DFSS). Both manufacturing and non-manufacturing (transactional Six Sigma) applications are included. Emphasis is placed on the use of case studies to motivate the use of, as well as the proper application of, the Six Sigma methodology. Formal Six Sigma Green Belt certification from UNH may be attained by successfully completing TECH 696. Prereq: MATH 539,MATH 644; or permission.
Cross listed with : MATH 837.01
Classes not allowed in section: Freshman, Sophomore
Instructors: Philip Ramsey
Start Date End Date Days Time Location
8/28/2023 12/11/2023 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 739 (01) - Applied Regression Analysis

Applied Regression Analysis

Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 10191
Statistical methods for the analysis of relationships between response and input variables: simple linear regression, multiple regression analysis, residual analysis and model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, analysis of variance, analysis of covariance, examination of validity of underlying assumptions, logistic regression analysis. Emphasizes real applications with use of statistical software. Prereq: MATH 539 (or 644). Writing intensive.
Cross listed with : MATH 839.01
Classes not allowed in section: Freshman, Sophomore
Attributes: Writing Intensive Course
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2023 12/11/2023 MWF 8:10am - 9:30am KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 739 (02) - Applied Regression Analysis

Applied Regression Analysis

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 13534
Statistical methods for the analysis of relationships between response and input variables: simple linear regression, multiple regression analysis, residual analysis and model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, analysis of variance, analysis of covariance, examination of validity of underlying assumptions, logistic regression analysis. Emphasizes real applications with use of statistical software. Prereq: MATH 539 (or 644). Writing intensive.
Cross listed with : MATH 839.02
Classes not allowed in section: Freshman, Sophomore
Attributes: Writing Intensive Course
Instructors: STAFF
Start Date End Date Days Time Location
8/28/2023 12/11/2023 MWF 8:10am - 9:30am ONLINE
Manchester   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 739 (M1) - Applied Regression Analysis

Applied Regression Analysis

Credits: 4.0
Term: Fall 2023 - Full Term (08/28/2023 - 12/11/2023)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 14772
Statistical methods for the analysis of relationships between response and input variables: simple linear regression, multiple regression analysis, residual analysis and model selection, multi-collinearity, nonlinear curve fitting, categorical predictors, analysis of variance, analysis of covariance, examination of validity of underlying assumptions, logistic regression analysis. Emphasizes real applications with use of statistical software. Prereq: MATH 539 (or 644). Writing intensive.
Attributes: Writing Intensive Course
Start Date End Date Days Time Location
8/28/2023 12/11/2023 T 5:40pm - 8:30pm PANDRA P146