Featured Programs
-
Engineering
See details
-
Pre-Health Seminar & Shadowing
See details
-
International Business
See details
Experiential Learning Opportunities
Introduction to Data Mining for Business Intelligence
Business, Management & Finance Program
Madrid, Spain
Dates: 8/26/24 - 12/21/24
Introduction to Data Mining 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: 13478
Transcript Source: Partner Institution
Course Details: Level 300, 400
Recommended Semester Credits: 3
Contact Hours: 42
Prerequisites: This course assumes that the student knows the contents of Statistics I and Statistics II and the lesson of Properties of Matrices in Mathematics for Economics II. Some notions in Multivariate Statistics
DESCRIPTION
1. Learning the R Statistical Language.
1.1 Basic commands.
1.2 Graphics in R.
1.3 Statistical functions in R and basic programming.
2. Visualization Techniques for complex business data.
2.1 Principal component analysis theory.
2.2 Basic examples with R code.
2.3 Case studies.
3. Multidimensional Scaling.
3.1 Metric scaling theory.
3.2 Examples with R code.
3.3 Perceptual mappings in R.
4. Cluster Analysis.
4.1 Hierarchical methods.
4.2 Centroid methods: k-means.
4.3 Case studies.
5. Classification Trees.
5.1 Information theory.
5.2 Classification trees algorithms.
5.3 Real case: credit scoring.
6. Real Case Studies.
6.1 Comprehensive real cases involving all the studied techniques
8. Real Case Studies.
8.1 Comprehensive real cases involving all the studied techniques.
1.1 Basic commands.
1.2 Graphics in R.
1.3 Statistical functions in R and basic programming.
2. Visualization Techniques for complex business data.
2.1 Principal component analysis theory.
2.2 Basic examples with R code.
2.3 Case studies.
3. Multidimensional Scaling.
3.1 Metric scaling theory.
3.2 Examples with R code.
3.3 Perceptual mappings in R.
4. Cluster Analysis.
4.1 Hierarchical methods.
4.2 Centroid methods: k-means.
4.3 Case studies.
5. Classification Trees.
5.1 Information theory.
5.2 Classification trees algorithms.
5.3 Real case: credit scoring.
6. Real Case Studies.
6.1 Comprehensive real cases involving all the studied techniques
8. Real Case Studies.
8.1 Comprehensive real cases involving all the studied techniques.