Timeroom: Fall 2018

Durham Graduate School :: Analytics

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

Intro: Applied Analytic Stats

Credits: 3.0
Term: Fall 2018 - E-term I (08/13/2018 - 10/05/2018)
Grade Mode: Letter Grading

Class Size: 15
CRN: 17453

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.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/13/2018	10/5/2018		Hours Arranged	ONLINE

Durham Graduate School :: Analytics

DATA 812 (01) - Health Analytics

Credits: 3.0
Term: Fall 2018 - Full Term (08/27/2018 - 12/10/2018)
Grade Mode: Letter Grading

Class Size: 15
CRN: 16174

This course introduces students to the field of health analytics and data science. It expands upon introductory statisitcal and data manipulation methods to include data mining, predictive analytics, cluster analysis, trend and pattern recognition, and data visualization. It couples data skills with interpretive and communication skills. Students will also be exposed to basic statistical programming. There will be a graduate component of the course (812) where students will work on advanced concepts and complete a separate culminating project.

Equivalent(s): HMP 812

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/27/2018	12/10/2018	MW	2:10pm - 3:30pm	PETT 114
8/27/2018	12/10/2018	T	3:40pm - 5:00pm	HEW 301

Durham Graduate School :: Analytics

DATA 820 (1ON) - Programming for Data Science

Credits: 3.0
Term: Fall 2018 - E-term I (08/13/2018 - 10/05/2018)
Grade Mode: Letter Grading

Class Size: 15
CRN: 17452

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.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/13/2018	10/5/2018		Hours Arranged	ONLINE

Durham Graduate School :: Analytics

DATA 821 (1ON) - Data Architecture

Credits: 3.0
Term: Fall 2018 - E-term II (10/15/2018 - 12/11/2018)
Grade Mode: Letter Grading

Class Size: 15
CRN: 17454

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.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

Start Date	End Date	Days	Time	Location
10/15/2018	12/11/2018		Hours Arranged	ONLINE

Durham Graduate School :: Analytics

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

Data Mining & Pred Modeling

Credits: 3.0
Term: Fall 2018 - E-term II (10/15/2018 - 12/11/2018)
Grade Mode: Letter Grading

Class Size: 15
CRN: 17451

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.

Attributes: Online (no campus visits), EUNH

Instructors: STAFF

Start Date	End Date	Days	Time	Location
10/15/2018	12/11/2018		Hours Arranged	ONLINE

Durham Graduate School :: Analytics

DATA 896 (01) - Self-Designed Analytics Lab I

Analytics Lab I

Credits: 3.0
Term: Fall 2018 - Full Term (08/27/2018 - 12/10/2018)
Grade Mode: Letter Grading

Class Size: 5
CRN: 14773

This is the first of a two course self-designed thesis sequence offered under the master's of science degree in analytics. The nature of the class will be applied learning directly around a student directed analytic thesis project. Students will have a choice of either bringing an analytical problem of their interest or one assigned by the instructor out of the ongoing projects in the lab. The student chosen problem will be vetted thoroughly and a decision will be made based on the depth of the proposed data management and analysis proposed submitted in the proposal. Once approved by the committee, the students will collect, clean, merge and create readable analytical files for the project and write a formal 2000+ words report on the data mining part of the project. Prereq: DATA 803 and permission.

Only listed majors in section: ANALYTICS, ANALYTICS CERT

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/27/2018	12/10/2018		Hours Arranged	TBA

Durham Graduate School :: Analytics

DATA 900 (01) - Data Architecture

Credits: 3.0
Term: Fall 2018 - Full Term (08/27/2018 - 12/10/2018)
Grade Mode: Letter Grading

Class Size: 44
CRN: 14330

The module-driven course builds off previous introductory analytics coursework and exposes students to advanced level concepts and techniques with respect to big data, data management, architecture, mining, privacy, and security concerns. Prereq: DATA 800.

Only listed majors in section: ANALYTICS, ANALYTICS CERT

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/27/2018	12/10/2018		Hours Arranged	PBLANE 216

Durham Graduate School :: Analytics

DATA 901 (01) - Analytics Applications I

Credits: 3.0
Term: Fall 2018 - Full Term (08/27/2018 - 12/10/2018)
Grade Mode: Letter Grading

Class Size: 44
CRN: 14331

This is the second of the four advanced core courses. This course is partly geared towards analytical business problem solving. This course covers the following broad topics areas: Text Mining, Visualization, Customer analytics and Segmentation, Financial Analytics, Optimization, and Risk analytics. The course is taught by different faculty and industry experts. Prereq: DATA 800.

Only listed majors in section: ANALYTICS, ANALYTICS CERT

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/27/2018	12/10/2018		Hours Arranged	PBLANE 216

Durham Graduate School :: Analytics

DATA 911 (01) - Analytics Practicum I

Credits: 3.0
Term: Fall 2018 - Full Term (08/27/2018 - 12/10/2018)
Grade Mode: Letter Grading

Class Size: 44
CRN: 14332

This course introduces students to the practicum project and synthesizes learning from the curriculum into the analysis of their team projects. It includes applied skills in data cleaning, data mining, and analysis, but also professionalization, including business writing, presentation skills and messaging. Prereq: DATA 800.

Only listed majors in section: ANALYTICS, ANALYTICS CERT

Instructors: STAFF

Start Date	End Date	Days	Time	Location
8/27/2018	12/10/2018		Hours Arranged	PBLANE 216