Corporate Data Warehousing and Business Intelligence Training Course

1. Need for data warehousing

• The evolution of data storage
• Comparing traditional databases and data warehouses
• Business benefits of data warehousing

2. Data warehousing architectures

• Single-tier, two-tier, and three-tier architectures
• Inmon versus Kimball approaches in data warehousing
• Considerations for selecting a data warehousing architecture

3. Extract, Transform, Load (ETL)

• Understanding the ETL process
• Tools and technologies for ETL

4. Data marts

• Definition and purpose of data marts
• Differences between data marts and data warehouses
• Implementing data marts in a data warehousing strategy

5. Operational Data Stores (ODS)

• Role of ODS in data warehousing
• Characteristics and functions of an ODS
• ODS versus a data warehouse

6. Data warehousing in the cloud

• Benefits of cloud-based data warehousing
• Key cloud data warehousing solutions

1. Data modeling for data warehousing

• Introduction to data modeling concepts
• Logical versus physical data models

2. Multidimensional data modeling

• Understanding dimensions, facts, and measures
• Principles of multidimensional modeling
• Benefits and challenges of multidimensional models

3. Star schema

• Components of a star schema
• Advantages of using a star schema
• Designing a star schema for data warehousing

4. Snowflake schema

• Understanding the snowflake schema
• Comparing snowflake and star schemas
• Implementing a snowflake schema in data warehousing

5. NoSQL, big data, data lakes, and data warehousing

• Role of NoSQL databases in data warehousing
• Understanding big data and its impact on data warehousing
• Data lakes versus data warehouses: Definitions, differences, and decisions

1. Overview of data mining for BI

• Introduction to data mining
• Data mining and its importance in business intelligence
• Key concepts and terminology in data mining

2. Data mining process

• Steps in the data mining process
• Tools and techniques for effective data mining

3. Data mining methods

• Classification, regression, and forecasting
• Decision trees, neural networks, and support vector machines
• Evaluating and selecting data mining methods

4. Data mining algorithms for predictive modeling

• Overview of common predictive modeling algorithms
• Application of algorithms in business scenarios

1. Unsupervised data mining for explanatory modeling

• Differences between supervised and unsupervised data mining
• Introduction to clustering and its use in data mining
• Overview of association rule mining

2. Clustering and segmentation

• Techniques and algorithms for clustering
• Applications of clustering in market segmentation
• Evaluating the effectiveness of clustering models

3. Association and market basket analysis

• Understanding association rules in data mining
• Conducting market basket analysis