| Course ID: | STAT 8470. 3 hours. |
| Course Title: | Advanced Network Data Analysis and Graphical Models |
Course
Description: | Network structures are increasingly common across the sciences, such as brain connectivity, gene-gene interaction, protein-protein interaction, and the spread of diseases. This course will introduce state-of-the-art concepts and algorithms concerning networks in statistics and machine learning. Students will read the latest research articles on novel theories, algorithms, and applications. |
| Oasis Title: | Adv Net Data Graph Model |
| Prerequisite: | [STAT 6420 and (STAT 4510/6510 or STAT 6810)] or permission of department |
Semester Course
Offered: | Offered fall and spring semester every even-numbered year. |
| Grading System: | A-F (Traditional) |
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| Course Objectives: | This course will provide training in network data analysis using R and Python. Students who take this course will learn to:
1. Produce statistical graphics to visualize complex network structures;
2. Model networks and make inferences;
3. Know key libraries and modules;
4. Find and use appropriate libraries and modules;
5. Efficiently manipulate data types and objects;
6. Write programs to implement more complex data analysis and data manipulation;
7. Know the pros and cons of each language relative to each other and other programming languages; and
8. Know the proper use of output from statistical software to communicate the results of statistical analyses through oral and written means.
Students will demonstrate competence through individual or group programming projects, labs, and examinations throughout the course. |
| Topical Outline: | Specific topics include graph theory, network mapping and visualization, characterization of network structure, network sampling, the modeling, inference, and prediction of networks, network processes, graph neural networks, deep learning, and causal inference. |