Claim up to $500 in flight credits!
Get up to $500 in flight credits or grants toward study or internship programs abroad when you apply by November 17, 2024. See our Official Rules for full details.
Introduction to Data Science
Engineering & Social Sciences Program
Madrid, Spain
Dates: 8/28/22 - 12/23/22
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.