Crash Course in Data Science
📅 March 20th, 2019
🌍 English
Course outline (9am-5pm)
- Definitions
- Examples
- X-selling
- Credit scoring
- Fraud Detection
- Recommender system
- Customer journey analysis
- Analytics Process Model
- Data scientist
- Key definitions (customer, target, etc.)
- Data Preprocessing
- Types of data
- Types of variables
- Denormalizing data
- Sampling and Exploratory analysis
- Missing values
- Outlier detection and handing
- Categorization
- Variable transformation
- Types of Analytics
- Predictive Analytics
- Linear regression
- Logistic regression
- Decision trees
- Random forests
- Neural networks (deep learning)
- Other predictive analytics techniques
- Evaluating Predictive Models
- Performance measures for classification models
- Out of sample versus Out of time
- Cross-validation
- Classification accuracy, classification error, sensitivity, specificity, precision, recall, F-measure
- Receiver Operating Characteristic curve (ROC)
- Area under Receiver Operating Characteristic curve (AUC)
- Cumulative Accuracy Profile (CAP)
- Accuracy ratio (Gini)
- Lift curve
- Top-decile lift
- Performance benchmarks
- Performance measures for regression models
- Scatter plot
- Pearson correlation
- R-squared
- Mean Squared Error (MSE)
- Mean Absolute Deviation (MAD)
- Other performance measures for predictive analytical models
- Interpretability
- Operational efficiency
- Economical cost
- Descriptive Analytics
- Association rules
- Support
- Confidence
- Post-processing
- Applications (recommender systems)
- Sequence rules
- Support
- Confidence
- Applications (customer journey analysis, process analytics)
- Clustering
- Hierarchical clustering
- Distance measures (Euclidean, Manhattan)
- Agglomerative versus divisive methods
- Dendrogram
- Non-hierarchical clustering
- k-means clustering
- Evaluating clustering solutions
- Social Network Analytics
- Social Network Examples (churn, credit card fraud, identity theft)
- Social Network Definitions
- Nodes versus edges
- Social Network Metrics
- Geodesic
- Degree
- Closeness
- Betweenness
- Graph theoretic center
- Social Network Learning
- Featurization
- Post Processing of Analytical models
- Model Interpretation (Visual Analytics, Sankey plots, Traffic light indicator approach, Nomograms, Decision tables)
- Model Documentation
- Model Backtesting
- Model Benchmarking
- Model Stress Testing
- Model Deployment
- Model Governance
- Model Ethics
- Economic Perspective
- Total Cost of Ownership (TCO)
- Return on Investment (ROI)
- In- versus Outsourcing
- On-Premise versus Cloud Analytics
- Open Source versus Commercial Software
- Improving ROI
- New sources of data
- Data quality
- Management support
- Cross-Fertilization
- Privacy and Security
- Overall considerations
- RACI Matrix
- Accessing Internal Data
- Privacy Regulation (GDPR)
👩🏫 Lecturers
Prof. dr. Wouter Verbeke
Professor at KU Leuven
Prof. dr. Bart Baesens
Professor at KU Leuven
🏢 Location
Van der Valk Hotel Brussels Airport (Belgium)
Culliganlaan 4b
1831 Diegem
Belgium
hotelbrusselsairport.com
🏫 Organizer
💼 Register
This course is in the past, registration is no longer possible.
Price and Registration
This course is in the past, registration is no longer possible.