| Course Title | Introduction To Data Science |
|---|---|
| Time | Mondays, 6:30pm-9:00PM |
| Location | NAC 5/110 |
| Credits & Hours | 3 credits, 3 hours |
| Instructor | Grant Long |
This course consists of a survey of analytical tools and concepts in data science, with goal of equipping students with an understanding of the best practices used by professional data scientists and analysts in top companies in technology, finance, and media. The course begins with an overview of fundamentals in data handling and exploratory data analysis, followed by an introduction to core concepts in statistical modeling and machine learning, and concludes with a brief introduction advanced concepts in data science.
Prerequisites: Intro to Programming (CSc102/103) or equivalent and Probability and Statistics (CSc217).
- Required Text: Data Science from Scratch, Joel Grus. 2nd Edition, April 2015 (O'Reilly). Available online.
- Additional web materials, including lecture notes, related to course work.
| The student acquires the ability to: | 1 | 2 | 3 | 4 | 5 | 6 |
|---|---|---|---|---|---|---|
| 1. Explain the key steps in a data science project. | R | I | I | |||
| 2. Apply Python to load, clean, and process data sets. | I | P | I | |||
| 3. Identify key elements of and patterns in a data set using computational analysis and statistical methods. | R | R | I | |||
| 4. Explain and visualize empirical findings using with Python and other resources. | R | R | ||||
| 5. Explain fundamental principles of machine learning. | I | I | ||||
| 6. Apply predictive algorithms to a data set. | I | I | I | |||
| 7. Work effectively in a team dedicated to analyzing data. | I | R | ||||
| I - introductory-level; R - reinforced-level; P - program-level |
- Elements of data projects
- Python Packages for data science
- Data Visualization
- Classification and Regression
- Regularization, Variance/Bias, Feature Importance
- Decision Trees
- Ensemble Methods
- Bayesian Analysis
- Text Analysis
- Unsupervised Learning Methods