Featured Programs
Experiential Learning Opportunities
Claim up to $1,000 in flight credits!
Get up to $1,000 in flight credits or grants toward study or internship programs abroad when you apply by January 01, 2025. See our Official Rules for full details.
Big Data for Business Intelligence
History, Politics & International Studies Program
Madrid, Spain
Dates: mid Jan 2026 - early Jun 2026
Big Data for Business Intelligence
OVERVIEW
CEA CAPA Partner Institution: Universidad Carlos III de Madrid
Location: Madrid, Spain
Primary Subject Area: Business
Instruction in: English
Course Code: 14451
Transcript Source: Partner Institution
Course Details: Level 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: Statistics I, Statistics II, Data analytical techniques for business, Introduction to data mining for business intelligence
DESCRIPTION
1. Introduction.
2. Data collection, sampling and preprocessing.
2.1. Types of data.
2.2. Sampling.
2.3. Data visualization tools.
2.4. Missing values.
2.5. Outlier detection and treatment.
2.6. Data transformations.
2.7. Dimension reduction.
2.8. Application: Risk management in the stock market.
3. Supervised learning: regression.
3.1. Linear and polynomial regression.
3.2. Cross-validation.
3.3. Model selection and regularization methods (ridge and lasso).
3.4. Nonlinear models, splines and generalized additive models.
3.5. Application: credit-scoring prediction.
4. Supervised learning: classification.
4.1. Bayes classifiers
4.2. Logistic regression.
4.3. K-nearest neighbors.
4.4. Random forest.
4.5. Support-vector machines.
4.6. Boosting.
4.7. Application: Credit risk.
4.8. Application: Fraud detection.
4.9. Application: Bankruptcy prediction
2. Data collection, sampling and preprocessing.
2.1. Types of data.
2.2. Sampling.
2.3. Data visualization tools.
2.4. Missing values.
2.5. Outlier detection and treatment.
2.6. Data transformations.
2.7. Dimension reduction.
2.8. Application: Risk management in the stock market.
3. Supervised learning: regression.
3.1. Linear and polynomial regression.
3.2. Cross-validation.
3.3. Model selection and regularization methods (ridge and lasso).
3.4. Nonlinear models, splines and generalized additive models.
3.5. Application: credit-scoring prediction.
4. Supervised learning: classification.
4.1. Bayes classifiers
4.2. Logistic regression.
4.3. K-nearest neighbors.
4.4. Random forest.
4.5. Support-vector machines.
4.6. Boosting.
4.7. Application: Credit risk.
4.8. Application: Fraud detection.
4.9. Application: Bankruptcy prediction