SEDS 501
Introduction to Data Science
This course will provide a foundation in the area of data science based on data curation and statistical analysis. It investigates data concepts, metadata creation and interpretation, general linear method, cluster analysis, and basics of information visualization. The course will cover fundamentals about data and data standards and methods for organizing, curating, and preserving data for reuse, inferential statistics: drawing conclusions and making decisions from data, data analysis tools, and diverse issues around data including technologies, behaviors, organizations, policies, and society.
| Week | Topics |
|---|---|
| 1 | Introduction: What is Data Science? |
| 2 | Statistical Inference |
| 3 | Exploratory Data Analysis and the Data Science Process |
| 4 | Basic Machine Learning Algorithms |
| 5 | One More Machine Learning Algorithm and Usage in Applications |
| 6 | Feature Generation and Feature Selection |
| 7 | Feature Learning |
| 8 | Recommendation Systems |
| 9 | Mining Social-Network Graphs |
| 10 | Link Analysis |
| 11 | Data Visualization |
| 12 | Natural Language Processing |
| 13 | Image Processing |
| 14 | Data Science and Ethical Issues |
