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Summary - Business Intelligence & Business Analytics (BIBA) - Master Information Management
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Summary - Business Intelligence &Business Analytics - Master Information
Management
Sven van Alem
, Table of contents
1. Lecture 1: Introduction to Data Management & Business Intelligence .......................................... 3
1.1 (Recorded) lecture notes ......................................................................................................... 3
1.2 Database systems: design, implementation, and management (book).................................. 4
2. Lecture 2: SQL & Data warehousing .............................................................................................. 10
2.1 (Recorded) lecture notes: Database trends and SQL ............................................................ 10
2.2 Data Warehouse Design: Modern Principle s and Methodologies (book) ............................ 12
3. Lecture 3: ETL, OLAP business databases, Business dashboards .................................................. 20
3.1 (Recorded) lecture notes ....................................................................................................... 20
3.2 Multidimensional Database Technology (paper) .................................................................. 23
3.3 Business Intelligence & Analytics (book, chapter: 4.6, 4.7, 4.8, 4.9) ..................................... 25
4. Lecture 4: Data mining introduction ............................................................................................. 28
4.1 Part A: Data Mining Intro ...................................................................................................... 28
4.2 Part B: Steps of Data Mining Process .................................................................................... 29
5. Lecture 5: Regression models ....................................................................................................... 30
6. Lecture 6: Naïve Bayes (classification) .......................................................................................... 32
7. Lecture 7: K-nearest neighbors (classification) ............................................................................. 34
8. Lecture 8: Performance measures ................................................................................................ 36
9. Lecture 9: Decision Tree (classification) ........................................................................................ 40
10. Lecture 10: Association Rules .................................................................................................... 42
11. Lecture 11: Clustering................................................................................................................ 44
5. Lecture 12: Neural Networks ........................................................................................................ 47
6. Lab session notes – SQL & Python................................................................................................. 51
6.1 SQL – Static Query Language ................................................................................................. 51
6.2 Python ................................................................................................................................... 53
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