Computational Thinking and Data Science (includes Applied Statistics)
This course aims to provide students with an understanding of the role that computation can play in solving problems and to help students to feel confident in writing small programs, which allow them to accomplish useful goals.
Pre-requisite: Introduction to Programming or Data Visualization or Data Curation
Weekly - Available soon
Total - Available soon
- Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data. 2nd ed. MIT Press, 2016. ISBN: 9780262529624.
- Taking into consideration the fundamental purpose of this course, the learning method most suitable to this course is learning-by-example as well as learning-by-doing.
- Your final grade is based on in-class exercises (20%), two problem sets (20% each), and the final exam (40%).
- Late assignments are not accepted. Submissions that do not run will receive at most 20% of the points.
1. Introduction to Computational Thinking
2. Optimization Problems and Graph-theoretic Models
3. Random Walks and Monte Carlo Simulation
4. Sampling and Confidence Intervals
5. Understanding Experimental Data
Programs where the course is taught: