Data Mining and Warehousing
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Follow on Instagram for UpdatesData Mining and Warehousing Syllabus — Unit-wise Content
The official GTU curriculum for Data Mining and Warehousing (subject code DI04016081) as prescribed for Biomed · Semester 4 divides the course into 5 units, covering 18 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (DI04016081)Unit 1 Fundamental Concepts of Data Mining
- 1.1 Data Mining 1.1.1 Define data mining 1.1.2 Difference between data mining and database 1.1.3 Common terms used: patterns
- & predictions 1.2 Steps in Knowledge Discovery Process (KDD) 1.2.1 Data selection
- Data preprocessing
- Data mining 1.2.2 Interpretation and evaluation of results 1.3 Importance of Data Mining 1.4 Applications of Data Mining in real life 1.4.1 Education: analyzing student performance
- Business: customer purchase analysis
- Healthcare: disease prediction
- Banking: fraud detection
Unit 2 Basics of Data Warehousing
- 2.1 Meaning of data
- information, and knowledge 2.1.1 Definition and examples of data 2.1.2 How data becomes information 2.1.3 What is knowledge 2.1.4 Real-life examples 2.2 Introduction to Data Warehouse 2.2.1 Meaning and purpose of a data warehouse 2.2.2 Key features 2.2.3 Difference between database and data warehouse 2.2.4 Importance in decision-making 2.3 Difference between OLTP and OLAP 2.3.1 Meaning of OLTP and OLAP 2.3.2 Major differences and Examples 2.4 Data Marts 2.4.1 Meaning of a data mart 2.4.2 Difference between data warehouse and data mart 2.4.3 Types of data marts 2.4.4 Uses of data marts in organizations Program Name: Diploma Engineering Level: Diploma Branch: Information Technology Subject Code: DI04016081 Subject Name: Data Mining and Warehousing
Unit 3 Data Mining Techniques
- 3.1 Classification 3.1.1 Meaning of classification 3.1.2 Working of Decision Tree and Naïve Bayes 3.1.3 Real-life examples 3.2 Clustering 3.2.1 Meaning of clustering 3.2.2 Working of K-means 3.2.3 Applications of clustering 3.3 Association Rule Mining 3.3.1 Meaning of association rule mining 3.3.2 Basics of Apriori algorithm 3.3.3 Market Basket Analysis
Unit 4 Data Warehouse Design
- 4.1 Star Schema and Snowflake Schema 4.1.1 Meaning of Star and Snowflake Schema 4.1.2 Key differences between Star and Snowflake
- Examples 4.2 Fact Table and Dimension Table 4.2.1 Definition of Fact Table and Dimension Table 4.2.2 Relationship between fact and dimension tables
- Examples 4.3 ETL (Extract
- Load) process 4.4 Basic OLAP operations 4.4.1 Roll-up
Unit 5 Tools and Applications
- 5.1 Introduction to data mining tools 5.1.1 Orange
- KNIME 5.2 Case studies of Data Mining 5.2.1 Business: Market Basket Analysis
- Education: Student performance analysis
- Healthcare: Disease prediction and patient data analysis 5.2.2 Results and insights from each case study 5.3 Future scope of Data Mining and Warehousing 5.3.1 Emerging applications 5.3.2 Role in decision-making and business intelligence 5.3.3 Ethical considerations
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