Inhoud
BUSINESS
INTELLIGENCE
UGENT
, Inhoudstabel BI
1. Introductie business intelligence
2. Data-analytic thinking
2.1. Intro
2.2. Waarom data-analytical thinking en data science?
2.2.1. Data opportuniteiten
2.2.2. Naleving van de voorschriften
2.2.3. Mogelijke toepassingen
2.3. Voorbeelden
2.3.1.1. Hurricane Frances – Walmart
2.3.1.2. Pregnancy prediction – Target
2.3.1.3. Churn prediction – Megatrends
2.4. Wat is data-analytical thinking
2.4.1. Data science capability as strategic asset
2.4.2. Signet bank vs capital one
2.4.3. Amazon
2.4.4. Harrah’s casinos
2.4.5. Waardering van Facebook en Twitter
2.5. Wat is data science of datawetenschap?
3. Business problems and data science solutions
3.1. Verschillende datamining taken
3.1.1. Classification & class probability estimation
3.1.2. Regression
3.1.3. Similarity matching
3.1.4. Clustering
3.1.5. Co-occurrence grouping
3.1.6. Profiling
3.1.7. Link prediction
3.1.8. Data reduction
3.1.9. Casual modeling
3.1.10. Conclusion
3.1.11. Two high-level primary goals: prediction and description
3.2. Supervised versus unsupervised methods
3.3. Het datamining process
3.3.1. Belangrijk onderscheid
3.3.2. Knowledge discovery in databases
3.3.3. Business understanding
1
BUSINESS
INTELLIGENCE
UGENT
, Inhoudstabel BI
1. Introductie business intelligence
2. Data-analytic thinking
2.1. Intro
2.2. Waarom data-analytical thinking en data science?
2.2.1. Data opportuniteiten
2.2.2. Naleving van de voorschriften
2.2.3. Mogelijke toepassingen
2.3. Voorbeelden
2.3.1.1. Hurricane Frances – Walmart
2.3.1.2. Pregnancy prediction – Target
2.3.1.3. Churn prediction – Megatrends
2.4. Wat is data-analytical thinking
2.4.1. Data science capability as strategic asset
2.4.2. Signet bank vs capital one
2.4.3. Amazon
2.4.4. Harrah’s casinos
2.4.5. Waardering van Facebook en Twitter
2.5. Wat is data science of datawetenschap?
3. Business problems and data science solutions
3.1. Verschillende datamining taken
3.1.1. Classification & class probability estimation
3.1.2. Regression
3.1.3. Similarity matching
3.1.4. Clustering
3.1.5. Co-occurrence grouping
3.1.6. Profiling
3.1.7. Link prediction
3.1.8. Data reduction
3.1.9. Casual modeling
3.1.10. Conclusion
3.1.11. Two high-level primary goals: prediction and description
3.2. Supervised versus unsupervised methods
3.3. Het datamining process
3.3.1. Belangrijk onderscheid
3.3.2. Knowledge discovery in databases
3.3.3. Business understanding
1