Timeroom: Spring 2024

Displaying 511 - 520 of 792 Results for: Attributes = EUNH
Law   Franklin Pierce School of Law :: Skills (LAW)

LSK 943 (1LH) - Appellate Advocacy

Appellate Advocacy

Online Course Delivery Method: Online Asynchronous
Credits: 2.0
Term: Spring 2024 - Law Hybrid (01/16/2024 - 05/10/2024)
Grade Mode: Letter Grading
Class Size:   14  
CRN: 54206
Appellate Advocacy is a writing intensive course designed to teach the different components of appellate brief writing, as well as effective appellate oral advocacy. One or two case problems (depending upon the particular professor) are assigned throughout the semester, modeled after actual court cases. Students will be taught how to master the facts of a case, the rule of law applicable to the particular legal problem, and the policy underpinning the rule of law. Paramount goals of the course include professionalism and instructing students on clear, persuasive, organized, and strategic written and oral communication skills necessary for effective legal advocacy. While AA focuses on the appellate practice setting, the written and oral advocacy skills students will acquire are applicable to all settings of legal practice. Grading will be based on one or two appellate briefs, oral arguments, meaningful class participation and other assignments. This course cannot be taken for an S/U grade.
Instructor Approval Required. Contact Instructor for permission then register through Webcat.
Prerequisite(s): (LSK 919 or LSK 919) and (LSK 920 or LSK 920) and (LSK 900 or LSK 900)
Majors not allowed in section: LAW JD DWS, LAW: JD, LAW: JD ADV, LAW: JD MBA, LAW: JD MPP, LAW: JD SW
Attributes: Online (no campus visits), Law Upper Level Writing, Law Experiential Learning, EUNH
Instructors: Elizabeth Mone
Start Date End Date Days Time Location
1/16/2024 5/10/2024 Hours Arranged ONLINE
Law   Franklin Pierce School of Law :: Skills (LAW)

LSK 953 (1LH) - Writing for Practice

Writing for Practice

Online Course Delivery Method: Online Asynchronous
Credits: 3.0
Term: Spring 2024 - Law Hybrid (01/16/2024 - 05/10/2024)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 54207
This course is designed to help second- and third-year students develop the kinds of writing, organization, critical thinking, editing and collaborative work skills essential to law practice and passing the bar. Students will work on multiple short (less than 5 pages) weekly assignments, engaging them in reading the law; conceptualizing, outlining, writing, editing, and revising legal documents; practicing writing concisely and clearly; researching and using samples, templates, and other practice-based resources; and working on related tasks. These assignments are designed to help students sharpen their ability to write any kind of legal document, using the appropriate format for the intended audience. The course will focus primarily on civil matters and will include some writing on criminal issues. The course's focus on essential skills, organization, analysis, doctrine, precision and conciseness, will transfer to writing in any legal setting.
Majors not allowed in section: LAW JD DWS, LAW: JD, LAW: JD ADV, LAW: JD MBA, LAW: JD MPP, LAW: JD SW
Attributes: Online (no campus visits), Law Upper Level Writing, Law Experiential Learning, Bar Elective Course, EUNH
Instructors: Sophie Sparrow
Start Date End Date Days Time Location
1/16/2024 5/10/2024 Hours Arranged ONLINE
Manchester   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 418 (M1) - Analysis and Applications of Functions

Analysis & Appl of Functions

Online Course Delivery Method: Online Asynchronous
Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   25  
CRN: 52268
Analysis and applications of algebraic and transcendental functions, with special emphasis on exponential, logarithmic, and trigonometric functions. Graphical analysis. Written projects are required on some or all of the following topics: rates of change, optimization, logarithmic or exponential modeling, and trigonometric functions. Intended for students planning to take MATH 425. Not offered for credit if credit is received for MATH 424 or MATH 425.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Attributes: Online (no campus visits), EUNH
Instructors: Donald Plante
Start Date End Date Days Time Location
1/23/2024 5/6/2024 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 740 (01) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online Asynchronous
Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 51451
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.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 539 or MATH 644
Cross listed with : MATH 840.01
Classes not allowed in section: Freshman, Sophomore
Attributes: Online (no campus visits), EUNH
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/23/2024 5/6/2024 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 756 (02) - Principles of Statistical Inference

Princpls Statistical Inference

Online Course Delivery Method: Online Synchronous
Credits: 4.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 50526
Introduces the basic principles and methods of statistical estimation and model fitting. One- and two-sample procedures, consistency and efficiency, likelihood methods, confidence regions, significance testing, Bayesian inference, nonparametric and re-sampling methods, decision theory.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 755
Cross listed with : MATH 856.02
Classes not allowed in section: Freshman, Sophomore
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: Pei Geng
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 11:10am - 12:30pm ONLINE
Final Exam 5/10/2024 5/10/2024 F 10:30am - 12:30pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 836 (02) - Advanced Statistical Modeling

Advanced Statistical Modeling

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 53779
This is a course on statistical models behind normal linear model. Topics covered in this course include generalized linear model, linear mixed model, generalized additive model, generalized linear mixed model, generalized additive mixed model, and smoothing methods if time allows.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): (MATH 835 with minimum grade of B- or MATH 839 with minimum grade of B- )
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: Qi Zhang
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 8:10am - 9:30am ONLINE
Final Exam 5/13/2024 5/13/2024 M 8:00am - 10:00am ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 838 (02) - Data Mining and Predictive Analytics

Data Mining & Pred Analytics

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 52160
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 Tress (CART), Random Forests, Neural Nets, Support Vector Machines, Logistic 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. Students must have completed a calculus-based introductory statistics course.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MW 12:40pm - 2:00pm ONLINE
Final Exam 5/13/2024 5/13/2024 M 3:30pm - 5:30pm ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 840 (01) - Design of Experiments I

Design of Experiments I

Online Course Delivery Method: Online Asynchronous
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 51452
First 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. Students must have completed an introductory statistics course.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Cross listed with : MATH 740.01
Attributes: Online (no campus visits), EUNH
Instructors: Philip Ramsey
Start Date End Date Days Time Location
1/23/2024 5/6/2024 Hours Arranged ONLINE
Durham   Engineering&Physical Sciences :: Mathematics&Statistics

MATH 856 (02) - Principles of Statistical Inference

Princpls Statistical Inference

Online Course Delivery Method: Online Synchronous
Credits: 3.0
Term: Spring 2024 - Full Term (01/23/2024 - 05/06/2024)
Grade Mode: Letter Grading
Class Size:   5  
CRN: 50527
Introduces the basic principles and methods of statistical estimation and model fitting. One- and two-sample procedures, consistency and efficiency, likelihood methods, confidence regions, significance testing, Bayesian inference, nonparametric and re-sampling methods, decision theory.
Registration Approval Required. Contact Instructor or Academic Department for permission then register through Webcat.
Prerequisite(s): MATH 855 with minimum grade of B-
Cross listed with : MATH 756.02
Attributes: Scheduled meeting time, Online (no campus visits), EUNH
Instructors: Pei Geng
Start Date End Date Days Time Location
1/23/2024 5/6/2024 MWF 11:10am - 12:30pm ONLINE
Final Exam 5/10/2024 5/10/2024 F 10:30am - 12:30pm ONLINE
CPS Online   Coll of Professional Studies :: Management-CPSO

MGMT 410 (01) - Principles of Management

Principles of Management

Online Course Delivery Method: Online Asynchronous
Credits: 4.0
Term: Spring 2024 - Term 3 (01/23/2024 - 03/15/2024)
Grade Mode: Letter Grading
Class Size:   30  
CRN: 55642
This course examines a wide range of business theories and practical applications related to effective management. In addition to learning about what management is and what managers do, students also explore and assess their own management skills and styles. Students apply critical thinking skills to core business functions. Focus is on contributing factors to management styles such as communication, the role of the manager, design of the organization, ethical issues, social responsibility and globalization. Course format may include the application of these management roles and competencies through experiential activities, group exercises and case analysis.
Advisor Approval Required. Contact your Academic Advisor for approval and registration.
Equivalent(s): MGMT 500G
Campuses not allowed in section: Durham
Attributes: Writing Intensive Course, Online (no campus visits), EUNH
Instructors: Sunil Ramlall
Start Date End Date Days Time Location
1/23/2024 3/15/2024 Hours Arranged ONLINE