Computational Thinking and Data Science (includes Applied Statistics)
Objectives
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.
General characterization
Code
67905
Credits
3.5
Responsible teacher
Patrícia Xufre Gonçalves da Silva Casqueiro
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
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.
Bibliography
Guttag, John. Introduction to Computation and Programming Using Python: With Application to Understanding Data.
2nd ed. MIT Press, 2016. ISBN: 9780262529624.
Teaching method
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.
Evaluation method
ASSESSMENT
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.
Subject matter
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.