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  4. Computational Thinking and Data Science (includes Applied Statistics)

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.
Pre-requisite: Introduction to Programming or Data Visualization or Data Curation


General characterization

Code

2494

Credits

3.5

Responsible teacher

Patrícia Xufre

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

Available soon

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

-    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