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Introduction to Data Science
Engineering & Social Sciences Program
Madrid, Spain
Dates: 8/29/26 - 12/23/26
Introduction to Data Science
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Computer Sciences
Instruction in: English
Course Code: 16475
Transcript Source: Partner Institution
Course Details: Level 100
Recommended Semester Credits: 3
Contact Hours: 42
DESCRIPTION
1. The Importance of Data Science
2. Understanding the Data: Case Studies of Exploratory Data Analysis and Visualization Techniques I
3. Understanding the Data: Case Studies of Exploratory Data Analysis and Visualization Techniques II
4. Importance of a good design of the experiment and choice of performance measures: precision, sensitivity, specificity, ROC curves. Over-fitting.
5. Introduction to unsupervised techniques: case studies of clustering I
6. Case studies of clustering II
7. Introduction to unsupervised classification: case studies on decision trees and random forests.
8. Case studies on data reduction techniques (Principal Component Analysis, Independent Component Analysis, Fisher Discriminant Analysis).
9. Introduction to Regression: Case Studies of Linear Regression.
10. Case studies of Logistic Regression.
11. Case studies on probabilistic models.
12. Introduction to the state of the art: case studies on Support vector machines.
13. Case studies on Deep Learning.
2. Understanding the Data: Case Studies of Exploratory Data Analysis and Visualization Techniques I
3. Understanding the Data: Case Studies of Exploratory Data Analysis and Visualization Techniques II
4. Importance of a good design of the experiment and choice of performance measures: precision, sensitivity, specificity, ROC curves. Over-fitting.
5. Introduction to unsupervised techniques: case studies of clustering I
6. Case studies of clustering II
7. Introduction to unsupervised classification: case studies on decision trees and random forests.
8. Case studies on data reduction techniques (Principal Component Analysis, Independent Component Analysis, Fisher Discriminant Analysis).
9. Introduction to Regression: Case Studies of Linear Regression.
10. Case studies of Logistic Regression.
11. Case studies on probabilistic models.
12. Introduction to the state of the art: case studies on Support vector machines.
13. Case studies on Deep Learning.