Data Warehousing and Data Mining

What you'll learn

The key areas this subject covers, mapped to the official curriculum.

Course units

The structured path through this subject.

1. 1

   Introduction to Data Warehousing

   What a data warehouse is, how OLTP differs from OLAP, the warehouse architecture, and the dimensional schemas that make queries fast.

   4 topics · 4 lessons available

2. 2

   Introduction to Data Mining

   What data mining is, the KDD process, the major mining tasks and methods, and the issues that trip up real mining projects.

   4 topics · 4 lessons available

3. 3

   Data Preprocessing

   Cleaning, integrating, transforming and reducing data before mining, plus discretization and concept hierarchies.

   4 topics · 4 lessons available

4. 4

   Data Cube Technology

   Data cubes, their computation, OLAP operations, and how cube technology generalizes beyond plain aggregation.

   4 topics · 4 lessons available

5. 5

   Mining Frequent Patterns

   Frequent itemsets and association rules, the Apriori and FP-Growth algorithms, and how correlation and lift separate real rules from noise.

   4 topics · 4 lessons available

6. 6

   Classification and Prediction

   Classification and prediction, decision tree induction, Bayesian classification, and honest evaluation through cross-validation.

   4 topics · 4 lessons available

7. 7

   Cluster Analysis

   Clustering: the idea, the k-means partitioning method, hierarchical methods, and density-based clustering with evaluation.

   4 topics · 4 lessons available

8. 8

   Graph Mining and Social Network Analysis

   Mining graphs: the fundamentals, community detection, influence and centrality, and real applications of social network analysis.

   4 topics · 4 lessons available

9. 9

   Mining Spatial, Multimedia, Text and Web Data

   Mining the world's messy data: spatial, multimedia, text and web data, each with its own structure and techniques.

   4 topics · 4 lessons available