Timeroom: Fall 2020

Displaying 1 - 6 of 6 Results for: Subject = DATA
Manchester   UNH-Manchester :: Analytics

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

Introduction to Analytics

Course Delivery Method: Online with some campus visits, EUNH
Can be taken by students who are remote.
Credits: 4.0
Term: Fall 2020 - UNHM Credit (15 weeks) (08/31/2020 - 12/11/2020)
Class Size:   20  
CRN: 13391
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.
Section Comments: Course can be taken remotely.
You must sign up in the Dept Office before registering through WEBCAT.
Attributes: Environment,Tech&Society(Disc)
Instructors: Jeremiah Johnson
Start Date End Date Days Time Location
8/31/2020 12/11/2020 M 1:01pm - 2:50pm PANDRA P149
Manchester   UNH-Manchester :: Analytics

DATA 690 (M1) - Internship Experience

Internship Experience

Course Delivery Method: Online with some campus visits, EUNH
Can be taken by students who are remote.
Credits: 3.0
Term: Fall 2020 - UNHM Credit (15 weeks) (08/31/2020 - 12/11/2020)
Class Size:   2  
CRN: 15686
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. Course can be taken remotely.
You must sign up in the Dept Office before registering through WEBCAT.
Repeat Rule: May be repeated for a maximum of 8 credits.
Only listed campus in section: Manchester
Classes not allowed in section: Freshman, Sophomore
Only listed majors in section: ANLYTC&DS:ANLY
Instructors: Karen Jin
Start Date End Date Days Time Location
8/31/2020 12/11/2020 T 9:01am - 11:50am PANDRA P126
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 visit the UNH Manchester Career and Professional Success Office with questions.

Durham   Graduate School :: Analytics

DATA 800 (1ON) - Introduction to Applied Analytic Statistics

Intro: Applied Analytic Stats

Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2020 - E-term I (08/10/2020 - 10/02/2020)
Class Size:   30  
CRN: 14879
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.
You must sign up in the Dept Office before registering through WEBCAT.
Instructors: Bogdan Gadidov
Start Date End Date Days Time Location
8/10/2020 10/2/2020 Hours Arranged ONLINE
Durham   Graduate School :: Analytics

DATA 820 (1ON) - Programming for Data Science

Programming for Data Science

Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2020 - E-term I (08/10/2020 - 10/02/2020)
Class Size:   30  
CRN: 14878
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.
You must sign up in the Dept Office before registering through WEBCAT.
Instructors: Phani Kidambi
Start Date End Date Days Time Location
8/10/2020 10/2/2020 Hours Arranged ONLINE
Durham   Graduate School :: Analytics

DATA 821 (1ON) - Data Architecture

Data Architecture

Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2020 - E-term II (10/13/2020 - 12/08/2020)
Class Size:   30  
CRN: 14880
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.
Instructors: Scott Valcourt
Start Date End Date Days Time Location
10/13/2020 12/8/2020 Hours Arranged ONLINE
Durham   Graduate School :: Analytics

DATA 822 (1ON) - Data Mining and Predictive Modeling

Data Mining & Pred Modeling

Course Delivery Method: Online (no campus visits), EUNH
Credits: 3.0
Term: Fall 2020 - E-term II (10/13/2020 - 12/08/2020)
Class Size:   30  
CRN: 14877
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
Instructors: Bogdan Gadidov
Start Date End Date Days Time Location
10/13/2020 12/8/2020 Hours Arranged ONLINE