- Identify what kinds of technologies are used for different application.
- Apply data preprocessing techniques.
- Describe data warehouse and OLAP technology.
- Mine frequent patterns and association.
- Apply data classification, clustering, and outlier detection.
Target group: Graduates and professionals
Prerequisite:
- Basic statistics and mathematics with algebra
- Basic knowledge in computer applications and programming will be an advantage.
Learning platform: Python, Weka
(Main Topics, Subtopics supposed to be covered by the course)
- Introduction to data, data sources, and data pipelines for data mining system
- Data warehousing concepts (DBMS, RDBMS, OLTP, OLAP, ETL)
- Methods for data preprocessing in data mining
- Data dimensions and dimensional reduction
- PCA and LDA with python
- Frequent pattern mining algorithms and mining association rules
- GSP, Apriori, and FP Growth Algorithms
- Correlation Analysis
- Data classification and prediction techniques
- Regression, KNN, SVM, ANN with python
- Graph pattern mining techniques, data clustering, and cluster analysis
- K-Means, DBSCAN, and STING with python
- Outlier analysis
👉Account No: 086100130008638
👉Account Name: Institute of Applied Statistics Sri Lanka
👉 People’s Bank, Thimbirigasyaya.
Payment should be made on or before 26th November 2023.
LKR. 15,000.00
Dr. A. M. R. Ravimal Bandara. Department of Computer Science, Faculty of Applied Sciences, University of Sri Jayewardenepura.