1. Introduction and Database Review

1.1 Course Structure

  • Syllabus and evaluation criteria

1.2 Database Fundamentals

  • Definition and levels of abstraction (Conceptual, Logical, Physical) [Present in the Question Set]
  • Database users and the role of the DBA [Present in the Question Set]
  • Entity-Relationship (E-R) Model [Present in the Question Set]

1.3 Normalization of Relations

  • First and Second Normal Form (1NF, 2NF)
  • Third Normal Form (3NF) [Present in the Question Set]

2. Business Intelligence and DSS

2.1 Decision Supporting Systems (DSS)

  • Definition and objectives of DSS

2.2 Recommender Systems

  • Recommender Systems

3. Data Warehouse and ETL Processes

3.1 Components and Architecture

  • Differences between Database and Data Warehouse
  • Data sources, Staging area, Data marts [Present in the Question Set]
  • 2-tier and 3-tier architecture

3.2 Logical Modeling

  • MOLAP, ROLAP, and HOLAP Databases [Present in the Question Set]
  • Star Schema and Snowflake Schema [Present in the Question Set]

3.3 ETL Subsystems

  • Extraction, Transformation, and Loading [Present in the Question Set]

4. Dimensional Modelling

4.1 Fundamentals of the Multidimensional Model

  • Fact Tables, Measures, and Granularity [Present in the Question Set]
  • Dimension Tables and Attributes [Present in the Question Set]
  • Hierarchies and Levels [Present in the Question Set]

4.2 OLAP Operations

  • Drill Down and Roll Up [Present in the Question Set]
  • Slice, Dice, and Pivot [Present in the Question Set]
  • Resolution of aggregation problems [Present in the Question Set]

4.3 Practical Applications

  • Retail Sales (Additive measures) [Present in the Question Set]
  • Inventory [Present in the Question Set]
  • Procurement [Present in the Question Set]
  • Order Management [Present in the Question Set]

5. Data Visualization with Tableau

5.1 Basic Operations

  • Worksheets and sample projects (Superstore, Regional, Word indicators)
  • Drill Down, Sorting, and Grouping

5.2 Advanced Features

  • Filters and Parameters [Present in the Question Set]
  • Calculated fields and syntax [Present in the Question Set]
  • Level of Detail (LOD)

5.3 Report Creation

  • Creation of Dashboards and Stories [Present in the Question Set]

6. Data Mining

6.1 Data Preparation and Exploration

  • CRISP-DM Model
  • Data Exploration
  • Anomaly detection [Present in the Question Set]

6.2 Tools

  • Weka Software

6.3 Classification Models

  • Decision trees: depth and Gini Index [Present in the Question Set]
  • Rule-based classification

6.4 Analysis and Clustering

  • Association Analysis [Present in the Question Set]
  • K-means and DBSCAN algorithms [Present in the Question Set]