Timeroom: Spring 2020

Displaying 1 - 10 of 14 Results for: Subject = DATA
Durham   UNH-Manchester > Analytics

DATA 557 (1HY) - Introduction to Data Science and Analytics

Introduction to Analytics

Credits: 4.0
Term: Spring 2020 - Full Term (01/21/2020 - 05/04/2020)
Class Size:   20  
CRN: 54147
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: Online with some campus visits, EUNH, Environment,Tech&Society(Disc)
Instructors: Brennan Donnell
Start Date End Date Days Time Location
1/21/2020 5/4/2020 Hours Arranged ONLINE
1/21/2020 5/4/2020 T 3:40pm - 5:40pm PBLANE 216
Manchester   UNH-Manchester > Analytics

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

Introduction to Analytics

Credits: 4.0
Term: Spring 2020 - UNHM Credit (15 weeks) (01/21/2020 - 05/11/2020)
Class Size:   20  
CRN: 53652
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: Jeremiah Johnson
Start Date End Date Days Time Location
1/27/2020 5/11/2020 M 1:01pm - 2:50pm PANDRA P149
Manchester   UNH-Manchester > Analytics

DATA 750 (M1) - Neural Networks

Neural Networks

Credits: 4.0
Term: Spring 2020 - UNHM Credit (15 weeks) (01/21/2020 - 05/11/2020)
Class Size:   2  
CRN: 56995
Artificial neural networks power the recent advances in computer vision, speech recognition, and machine translation. This is a first course on neural networks with a focus on applications in computer vision and natural language processing. Topics will include generic feedforward neural networks, convolutional neural networks for computer vision tasks, and recurrent neural networks with application to natural language processing, with other topics to be selected based on the interests of the instructor and the class. Prereq: MATH 425, MATH 545 or MATH 645, COMP 490, or permission of the instructor. Also listed as COMP 750.
Section Comments: Cross listed with COMP 750 & COMP 850
Equivalent(s): DATA 751
Instructors: Jeremiah Johnson
Start Date End Date Days Time Location
1/24/2020 5/8/2020 F 10:01am - 11:50am PANDRA P149
Durham   Graduate School > Analytics

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

Intro: Applied Analytic Stats

Credits: 3.0
Term: Spring 2020 - E-term III (01/13/2020 - 03/06/2020)
Class Size:   15  
CRN: 54192
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, ANALYTICS CERT
Attributes: Online (no campus visits), EUNH
Instructors: Bogdan Gadidov
Start Date End Date Days Time Location
1/13/2020 3/6/2020 Hours Arranged ONLINE
Durham   Graduate School > Analytics

DATA 820 (1ON) - Programming for Data Science

Programming for Data Science

Credits: 3.0
Term: Spring 2020 - E-term III (01/13/2020 - 03/06/2020)
Class Size:   15  
CRN: 54189
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, ANALYTICS CERT
Attributes: Online (no campus visits), EUNH
Instructors: Phani Kidambi
Start Date End Date Days Time Location
1/13/2020 3/6/2020 Hours Arranged ONLINE
Durham   Graduate School > Analytics

DATA 821 (1ON) - Data Architecture

Data Architecture

Credits: 3.0
Term: Spring 2020 - E-term IV (03/16/2020 - 05/07/2020)
Class Size:   30  
CRN: 54190
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, ANALYTICS CERT
Attributes: Online (no campus visits), EUNH
Instructors: Scott Valcourt
Start Date End Date Days Time Location
3/16/2020 5/7/2020 Hours Arranged ONLINE
Durham   Graduate School > Analytics

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

Data Mining & Pred Modeling

Credits: 3.0
Term: Spring 2020 - E-term IV (03/16/2020 - 05/07/2020)
Class Size:   30  
CRN: 54191
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, ANALYTICS CERT
Attributes: Online (no campus visits), EUNH
Instructors: Bogdan Gadidov
Start Date End Date Days Time Location
3/16/2020 5/7/2020 Hours Arranged ONLINE
Durham   Graduate School > Analytics

DATA 888 (01) - Special Topics

Top/Health Analytics

Credits: 3.0
Term: Spring 2020 - Full Term (01/21/2020 - 05/04/2020)
Class Size:   15  
CRN: 54699
This course will explore the purpose, design, and analysis of a real-world data science project guided by faculty. Students will be provided a collection of data sets and systematically work through data cleaning, data merging, and the application of a variety of data science methods. The outcome of the course will be an iterative, faculty-guided exploration. The outcomes of the class will be a formal presentation for public consumption using data science visualizations. Prereq: Permission
See instructor for permission then sign up in the dept office before registering through WEBCAT.
Only listed majors in section: ANALYTICS, ANALYTICS PP
Instructors: Prashant Mittal
Start Date End Date Days Time Location
1/21/2020 5/4/2020 T 1:30pm - 4:30pm PBLANE 205
Durham   Graduate School > Analytics

DATA 897 (01) - Self Designed Analytics Thesis Lab II

Self-Designed Analytics Lab II

Credits: 3.0
Term: Spring 2020 - Full Term (01/21/2020 - 05/04/2020)
Class Size:   10  
CRN: 53786
This is the second of a two course self-designed thesis sequence offered under the master's of science degree in analytics. The nature of the class is applied learning directly around a student directed analytic thesis project. The class requires competency in two areas for the successful completion of the course. Students will have completed the data collection, cleaning and management and created readable analytic files for the project of their choice in the first of the two course sequence. Students are primarily responsible to apply modern analytical tools and techniques like predictive modeling, segmentation, and network analysis etc. They are also required to write a formal 2000+ word report on the findings of the project. The report is expected to include modern data visualization synthesized with analysis results. Prereq: DATA 803.
Only listed majors in section: ANALYTICS, ANALYTICS CERT, ANALYTICS PP
Instructors: Prashant Mittal
Start Date End Date Days Time Location
1/21/2020 5/4/2020 Hours Arranged PBLANE 216
Durham   Graduate School > Analytics

DATA 897 (02) - Self Designed Analytics Thesis Lab II

Analytics Thesis Lab II

Credits: 3.0
Term: Spring 2020 - Full Term (01/21/2020 - 05/04/2020)
Class Size:   10  
CRN: 55347
This is the second of a two course self-designed thesis sequence offered under the master's of science degree in analytics. The nature of the class is applied learning directly around a student directed analytic thesis project. The class requires competency in two areas for the successful completion of the course. Students will have completed the data collection, cleaning and management and created readable analytic files for the project of their choice in the first of the two course sequence. Students are primarily responsible to apply modern analytical tools and techniques like predictive modeling, segmentation, and network analysis etc. They are also required to write a formal 2000+ word report on the findings of the project. The report is expected to include modern data visualization synthesized with analysis results. Prereq: DATA 803.
Only listed majors in section: ANALYTICS, ANALYTICS CERT, ANALYTICS PP
Instructors: Joanna Gyory
Start Date End Date Days Time Location
1/21/2020 5/4/2020 Hours Arranged TBA