Timeroom: Fall 2020

Durham Engineering&Physical Sciences :: Computer Science

CS 920 (01) - Distributed Systems and Algorithms

Distributed Sys and Algorithms

Credits: 3.0
Term: Fall 2020 - Full Term (08/31/2020 - 12/11/2020)
Grade Mode: Letter Grading

Class Size: 22
CRN: 16953

Covers fundamental topics in distributed systems: time, global state, synchronization, election, consensus, distributed file systems, security. Also includes a study of several distributed applications. Prereq: Operating System fundamentals or equivalent.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/31/2020	12/11/2020	MWF	10:10am - 11:00am	KING N121

Durham Engineering&Physical Sciences :: Computer Science

CS 927 (01) - Software Security Analysis

Software Security Analysis

Can be taken by students who are remote.

Credits: 3.0
Term: Fall 2020 - Full Term (08/31/2020 - 12/11/2020)
Grade Mode: Letter Grading

Class Size: 15
CRN: 16959

This course covers advanced research topics in software security. The main focus is automatic software analysis techniques, such as symbolic execution, taint analysis, and fuzz testing.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/31/2020	12/11/2020	TR	11:10am - 12:30pm	MURK G17

Durham Engineering&Physical Sciences :: Computer Science

CS 998 (01) - Independent Study

Independent Study

Credits: 1.0 to 6.0
Term: Fall 2020 - Full Term (08/31/2020 - 12/11/2020)
Grade Mode: Letter Grading

CRN: 10716

Section above not available for web registration; Check with dept for details.

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/31/2020	12/11/2020		Hours Arranged	TBA

Durham Engineering&Physical Sciences :: Computer Science

CS 999 (01) - Doctoral Research

Doctoral Research

Credits: 0.0
Term: Fall 2020 - Full Term* (08/31/2020 - 12/11/2020)
Grade Mode: Graduate Credit/Fail grading

CRN: 10386

Cr/F.

Section above not available for web registration; Check with dept for details.

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/31/2020	12/11/2020		Hours Arranged	TBA

Durham Engineering&Physical Sciences :: Computer Science

CS 999 (17) - Doctoral Research

Doctoral Research

Credits: 0.0
Term: Fall 2020 - Full Term* (08/31/2020 - 12/11/2020)
Grade Mode: Graduate Credit/Fail grading

Class Size: 1
CRN: 14449

Cr/F.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/31/2020	12/11/2020		Hours Arranged	TBA

Durham Graduate School :: Analytics

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

Intro: Applied Analytic Stats

Credits: 3.0
Term: Fall 2020 - E-term I (08/10/2020 - 10/02/2020)
Grade Mode: Letter Grading

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.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

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

Credits: 3.0
Term: Fall 2020 - E-term I (08/10/2020 - 10/02/2020)
Grade Mode: Letter Grading

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.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

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

Credits: 3.0
Term: Fall 2020 - E-term II (10/13/2020 - 12/08/2020)
Grade Mode: Letter Grading

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.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

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

Credits: 3.0
Term: Fall 2020 - E-term II (10/13/2020 - 12/08/2020)
Grade Mode: Letter Grading

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.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Mutual Exclusion : ADMN 872

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

Start Date	End Date	Days	Time	Location
10/13/2020	12/8/2020		Hours Arranged	ONLINE

Durham Graduate School :: Development Policy&Practice

DPP 902 (1ON) - Economic Analysis for Development

Economic Analysis

Credits: 3.0
Term: Fall 2020 - Full Term (08/31/2020 - 12/11/2020)
Grade Mode: Letter Grading

Class Size: 30
CRN: 14916

This course provides the practitioner with tools of economic analysis that are necessary for effective community development practice. Drawing upon principles of macroeconomics, the course explores how markets, property rights, political institutions, government policies, environmental conditions and cultural values interact to produce development outcomes.

Department Approval Required. Contact Academic Department for permission then register through Webcat.

Only listed majors in section: COM DEV PLC PRT, DEV PLICY PRCT

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/31/2020	12/11/2020		Hours Arranged	ONLINE

Additional Course Details:

COURSE CONTEXT AND DESCRIPTION

This course provides tools and concepts from the field of economics (mostly microeconomics) that participants can apply to their work in community development. Topics we will touch on include consumer choice theory, the theory of efficient markets, theories of market failure, behavioral economics, game theory, economic base theory, cost-benefit analysis, and economic inequality. For each of these topics, we will examine how these concepts can inform community development practitioners as they develop and implement strategies to improve people’s lives and communities. We will also explore various techniques for working with data and conducting empirical research to support community development strategy development. The course is not a complete overview of the field of economics or even a complete introductory course in microeconomics or econometrics. Rather, it seeks to show how “thinking like an economist” can yield insights to support community development practitioners in their work. No previous coursework in economics is required, and no advanced skills in mathematics are required beyond basic high-school algebra.

Course meets over ZOOM on Wednesdays, 5:30 PM - 7:00 PM, every two weeks starting Wednesday September 9.

Sept 9

Sept 23

Oct 7

Oct 21

Nov 4

Nov 18

Dec 2

Dec 16