Timeroom: Fall 2022

Displaying 191 - 200 of 384 Results for: Campus = Manchester

CPRM 880 (M1) - Cybersecurity Metrics and Evaluation

Cybersec Metrics & Evaluation

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term I (08/15/2022 - 10/07/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 14290
This course provides an overview of analytical techniques for the documentation and evaluation of cybersecurity metrics, and the incorporation of such assessments in organizational risk management. Students will become familiar with methods for cybersecurity evaluation and the translational impacts to function and mission success of an organization (business, public administration, homeland security, etc.); as well as processes for security measurements, comparisons, and reassessments for purposes of risk management. Pre- or Co-req: CPRM 870.
Section Comments: Tech requirements: microphone & webcam
Only listed majors in section: CYBR SEC PRM
Instructors: Tnishia Dials
Start Date End Date Days Time Location
8/15/2022 10/7/2022 Hours Arranged ONLINE
Additional Course Details: 

Students in other programs are welcome to request permission by contacting the program administrator Kathy Carlman at katherine.carlman@unh.edu or (603) 641-4102. Note the early start date.

Short 8-week format. Firm deadlines for assignments spread throughout the 8 weeks.

CPRM 890 (M1) - Organizations, Change Management, and Leadership

Org Change & Leadership

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term II (10/17/2022 - 12/13/2022)
Grade Mode: Letter Grading
Class Size:   15  
CRN: 14297
This course examines both private and public institutions as systems whose effectiveness depends on how an organization adapts to opportunities, threats, and demands (external and internal). Students explore the design and leadership of ethical and socially responsible organizations. In course examples and exercises, students will apply this knowledge to their respective research interests (e.g., cybersecurity, analytics, criminal justice, public health, etc.).
Section Comments: Tech requirements: microphone & webcam.
Only listed majors in section: CYBR SEC PRM
Instructors: Raymond Tramposch
Start Date End Date Days Time Location
10/17/2022 12/13/2022 Hours Arranged ONLINE
Additional Course Details: 

Students in other programs are welcome to request permission by contacting the program administrator Kathy Carlman at katherine.carlman@unh.edu or (603) 641-4102.

Short 8-week format. Firm deadlines for assignments spread throughout the 8 weeks.

CPRM 898 (M2) - Capstone: Non-Thesis Option

Capstone: Non-Thesis Option

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term II (10/17/2022 - 12/13/2022)
Grade Mode: Letter Grading
Class Size:   10  
CRN: 16551
This capstone integrates all disciplines and competencies that have been learned in this degree program, plus the student's past experiences, areas of specialization, and professional goals, into a single work-based project, internship experience, or other appropriate activity. In consultation with an advisor, each student develops a project plan, establishes goals and objectives; collects and analyzes information; and prepares and delivers a final project agreed upon by the student and advisor. Prereq: CPRM 720 / CPRM 820 and CPRM 880. Pre- or Co-req: CPRM 790 / CPRM 890.
Section Comments: Tech requirements: microphone & webcam
Instructor Approval Required. Contact Instructor for permission then register through Webcat.
Only listed majors in section: CYBR SEC PRM
Instructors: Karl Grindal
Start Date End Date Days Time Location
10/17/2022 12/13/2022 Hours Arranged ONLINE
Manchester   UNH-Manchester :: Analytics

DATA 557 (M1) - Introduction to Data Science and Analytics

Introduction to Analytics

Online Course Delivery Method: Online with some campus visits, EUNH
Credits: 4.0
Term: Fall 2022 - UNHM Credit (15 weeks) (08/29/2022 - 12/12/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 12452
An introduction to data science and analytics. The landscape of analytics, including an overview of industries and sectors using analytics or expected to use analytics in the near future. Data generation, data management, data cleaning, and data preparation. Ethical use of data. Focus on visual and exploratory analysis. Project-based, with an emphasis on collaborative, experiential learning. Programming and statistical software will be used, but previous experience is not required.
Attributes: Environment,Tech&Society(Disc)
Instructors: STAFF
Start Date End Date Days Time Location
8/29/2022 12/12/2022 T 6:01pm - 7:50pm PANDRA P367
8/29/2022 12/12/2022 Hours Arranged ONLINE
Manchester   UNH-Manchester :: Analytics

DATA 674 (M1) - Predictive and Prescriptive Analytics I

Predictive Analytics I

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Fall 2022 - UNHM Credit (15 weeks) (08/29/2022 - 12/12/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 15097
A first course in predictive and prescriptive analytics. Supervised learning models including linear models and CART models. Model assessment and scoring methods, including cross-validation. Regularization and model tuning. Unsupervised learning models including k-means clustering. Project-based, with an emphasis on collaborative, experiential learning. Statistical software will be used and programming required. Prereq: MATH 425, COMP 570, DATA 557.
Instructors: Jeremiah Johnson
Start Date End Date Days Time Location
8/29/2022 12/12/2022 M 9:01am - 11:50am ONLINE
Manchester   UNH-Manchester :: Analytics

DATA 690 (M1) - Internship Experience

Internship Experience

Online Course Delivery Method: Scheduled meeting time, Online (no campus visits), EUNH
Credits: 4.0
Term: Fall 2022 - UNHM Credit (15 weeks) (08/29/2022 - 12/12/2022)
Grade Mode: Credit/Fail Grading
Class Size:   2  
CRN: 15142
A field-based learning experience via placement in a business, non-profit, or government organization using analytics. Under the guidance of a faculty advisor and workplace supervisor, students gain practical experience solving problems and improving operational processes using analytics. May be repeated but no more than 4 credits may fill major requirements. Prereq: UMST 582.
Section Comments: Cross listed with COMP 690, COMP 891, COMP 892
Repeat Rule: May be repeated for a maximum of 8 credits.
Instructors: Andrea Kokolis
Start Date End Date Days Time Location
8/29/2022 12/12/2022 T 9:01am - 11:50am ONLINE
Additional Course Details: 

Registering for academic credit does not complete your required internship approval process. Students must register and “request an experience” in the UNH online platform of Handshake once they have their internship. Visit https://app.joinhandshake.com/experiences/new to complete your approval process.

For more information on how to complete the Handshake approval process visit, https://manchester.unh.edu/student-internships or contact the UNH Manchester Career and Professional Success (CaPS) Office with questions.

Manchester   UNH-Manchester :: Analytics

DATA 800 (M1) - Introduction to Applied Analytic Statistics

Intro: Applied Analytic Stats

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term I (08/15/2022 - 10/07/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 15646
This course is designed to give students a solid understanding of the experience in probability, and inferential statistics. The course provides a foundational understanding of statistical concepts and tools required for decision making in a data science, business, research or policy setting. The course uses case studies and requires extensive use of statistical software.
Only listed majors in section: ANALYT DS CERT, ANALYT DS CERT
Instructors: Bogdan Gadidov
Start Date End Date Days Time Location
8/15/2022 10/7/2022 Hours Arranged ONLINE
Manchester   UNH-Manchester :: Analytics

DATA 820 (M1) - Programming for Data Science

Programming for Data Science

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term I (08/15/2022 - 10/07/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 15647
In this class, students will build their foundational toolbox in data science: upon completion, students will be able to use the computer from the command line; practice version control with GIT & GitHub; gain a mastery of programming in Python; data wrangling with Python and perform an exploratory data analysis (EDA) in Python. All learning objectives are achieved through active application of these techniques to real world datasets. Pre- or Coreq: DATA 800.
Only listed majors in section: ANALYT DS CERT, ANALYT DS CERT
Instructors: Phani Kidambi
Start Date End Date Days Time Location
8/15/2022 10/7/2022 Hours Arranged ONLINE
Manchester   UNH-Manchester :: Analytics

DATA 821 (M1) - Data Architecture

Data Architecture

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term II (10/17/2022 - 12/13/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 15648
In this class, students will learn the foundations of databases and large datasets: upon completion, students will be able to explore out-of-ram datasets though traditional SQL databases and NoSQL databases. Students will also be introduced to distributed computing. All learning objectives are achieved through active application of these techniques to world datasets. Prereq: DATA 800; DATA 820.
Only listed majors in section: ANALYT DS CERT, ANALYT DS CERT
Instructors: Timothy Chadwick
Start Date End Date Days Time Location
10/17/2022 12/13/2022 Hours Arranged ONLINE
Manchester   UNH-Manchester :: Analytics

DATA 822 (M1) - Data Mining and Predictive Modeling

Data Mining & Pred Modeling

Online Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2022 - E-term II (10/17/2022 - 12/13/2022)
Grade Mode: Letter Grading
Class Size:   20  
CRN: 15649
In this class, students will learn foundations of practical machine learning: upon completion, students will be able to understand and apply supervised and unsupervised learning in Python to build predictive models on real world datasets. Techniques covered include (but not limited to): preprocessing, dimensionality reduction, clustering, feature engineering and model evaluation. Models covered include: generalized linear models, tree-based models, bayesian models, support vector machines, and neural networks. All learning objectives are achieved through active application of these techniques to real world datasets. Prereq: DATA 800; DATA 820 Pre- or Coreq: DATA 821.
Mutual Exclusion : ADMN 872
Only listed majors in section: ANALYT DS CERT, ANALYT DS CERT
Instructors: Bogdan Gadidov
Start Date End Date Days Time Location
10/17/2022 12/13/2022 Hours Arranged ONLINE