Timeroom: Spring 2021

Displaying 101 - 110 of 160 Results for: Subject = MATH
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 645 (02R) - Linear Algebra for Applications

Linear Algebra for Application

Online Course Delivery Method: Remote Section
Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 57412
Fundamental notions of vector space theory, linear independence, basis, span, scalar product, orthogonal bases. Includes a survey of matrix algebra, solution of systems linear equations, rank, kernel, eigenvalues and eigenvectors, the LU- and QR-factorizations, and least squares approximation. Selected applications in mathematics, science, engineering and business. Prereq: MATH 426.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Mutual Exclusion : MATH 545, MATH 762
Only listed campus in section: Durham, Manchester
Only listed colleges in section: Engineering&Physical Sciences
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MW 6:10pm - 8:00pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 696 (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:   1  
CRN: 53421
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
Only listed campus in section: Durham, Manchester
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 Hours Arranged TBA
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: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 54000
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.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed campus in section: Durham, Manchester
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 TR 4:10pm - 6:00pm HORT 215
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 709 (01) - Teaching of Mathematics in Grades 6-12

Teaching of Mathematics, 6-12

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 53329
Methods of teaching mathematics at the middle and high school levels; uses of technology, manipulatives, models, and diagrams; developing unit and lesson plans; assessment; instructional formats; teaching reading and writing in mathematics. Prereq: MATH 700; or permission.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Equivalent(s): MATH 791
Only listed campus in section: Durham, Manchester
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 TR 9:10am - 11:00am KING W387
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 736 (01) - Advanced Statistical Methods for Research

Adv Stat Methods for Research

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   9  
CRN: 56047
An introduction to multivariate statistical methods, including principal components, discriminant analysis, cluster analysis, factor analysis, multidimensional scaling, and MANOVA. Additional topics include generalized linear models, general additive models, depending on the interests of class participants. The use of statistical software, such as JMP, S PLUS, or R, is fully integrated into the course. Prereq: MATH 739.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed campus in section: Durham, Manchester
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MWF 8:10am - 9:30am KING S320
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 736 (1SY) - Advanced Statistical Methods for Research

Adv Stat Methods for Research

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   16  
CRN: 56048
An introduction to multivariate statistical methods, including principal components, discriminant analysis, cluster analysis, factor analysis, multidimensional scaling, and MANOVA. Additional topics include generalized linear models, general additive models, depending on the interests of class participants. The use of statistical software, such as JMP, S PLUS, or R, is fully integrated into the course. Prereq: MATH 739.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed campus in section: Durham, Manchester
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MWF 8:10am - 9:30am ONLINE
Additional Course Details: 

MATH 736 (1SY)  is a SYNC course, an online course offered synchronously & archived. Campus visits may be required for exams. Coursework may be completed 100% online. Students may choose to attend the classes on campus or may log in remotely from their computers to interact with the class. It is expected that students are available during the scheduled class time.

Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 738 (01) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 56049
An introduction to supervised and unsupervised methods for exploring large data sets and developing predictive models. Unsupervised methods include: market basket analysis, principal components, clustering, and variables clustering. Important statistical and machine learning methods (supervised learning) include: Classification and Regression Trees (CART), Random Forests, Neural Nets, Support Vector Machines, Logistics Regression and Penalized Regression. Additional topics focus on metamodeling, validation strategies, bagging and boosting to improve prediction or classification, and ensemble prediction from a set of diverse models. Required case studies and projects provide students with experience in applying these techniques and strategies. The course necessarily involves the use of statistical software and programming languages. Undergraduate students are required to have junior or senior status to in enroll in this course. Prereq: MATH 539 (or MATH 644); or permission.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Mutual Exclusion : CS 750, IT 630
Only listed campus in section: Durham, Manchester
Classes not allowed in section: Freshman, Sophomore
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MW 12:40pm - 2:00pm KING N129
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 738 (1SY) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   28  
CRN: 53129
An introduction to supervised and unsupervised methods for exploring large data sets and developing predictive models. Unsupervised methods include: market basket analysis, principal components, clustering, and variables clustering. Important statistical and machine learning methods (supervised learning) include: Classification and Regression Trees (CART), Random Forests, Neural Nets, Support Vector Machines, Logistics Regression and Penalized Regression. Additional topics focus on metamodeling, validation strategies, bagging and boosting to improve prediction or classification, and ensemble prediction from a set of diverse models. Required case studies and projects provide students with experience in applying these techniques and strategies. The course necessarily involves the use of statistical software and programming languages. Undergraduate students are required to have junior or senior status to in enroll in this course. Prereq: MATH 539 (or MATH 644); or permission.
Section Comments: This course is a synchronous online course. In-class exams may be scheduled; for remote access of these, contact the instructor.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Mutual Exclusion : CS 750, IT 630
Only listed campus in section: Durham, Manchester
Classes not allowed in section: Freshman, Sophomore
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MW 12:40pm - 2:00pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 740 (1ON) - Design of Experiments I

Design of Experiments I

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   75  
CRN: 52007
Course in design of experiments with applications to quality improvement in industrial manufacturing, engineering research and development, or research in physical and biological sciences. Experimental factor identification, statistical analysis and modeling of experimental results, randomization and blocking, full factorial designs, random and mixed effects models, replication and sub-sampling strategies, fractional factorial designs, response surface methods, mixture designs, and screening designs. Focuses on various treatment structures for designed experimentation and the associated statistical analyses. Use of statistical software. Prereq: MATH 539 (or 644); or permission.
Section Comments: Offered 100% online.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed campus in section: Durham, Manchester
Classes not allowed in section: Freshman
Attributes: Online (no campus visits), EUNH
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 743 (01) - Time Series Analysis

Time Series Analysis

Credits: 4.0
Term: Spring 2021 - Full Term (02/01/2021 - 05/11/2021)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 56050
An introduction to univariate time series models and associated methods of data analysis and inference in the time domain and frequency domain. Topics include: auto regressive (AR), moving average (MA), ARMA and ARIMA processes, stationary and non-stationary processes, seasonal ARIMA processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, diagnostic checking of fitted models, forecasting, spectral density function, periodogram and discrete Fournier transform, linear filters, parametric spectral estimation, dynamic Fournier analysis. Additional topics may include wavelets and long memory processes (FARIMA) and GARCH Models. The use of statistical software, such as JMP, or R, is fully integrated into the course. Prereq: MATH 739. Offered in alternate years in the spring semester.
Department Approval Required. Contact Academic Department for permission then register through Webcat.
Only listed campus in section: Durham, Manchester
Instructors: STAFF
Start Date End Date Days Time Location
2/1/2021 5/11/2021 MWF 9:40am - 11:00am KING S320