Programme Parameters
Foundation
3 Days (4.5 Hrs)
Online (e-LMS)
• No prior experience required — basic computer literacy is sufficient.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Introduction to Data Mining & Warehousing course provides a foundational understanding of data storage, management, and analytics. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Introduction to Data Mining & Warehousing course provides a foundational understanding of data storage, management, and analytics.
📋 Course Objectives
1. Put AI Enablement techniques to work on real datasets and case studies.
2. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in AI Enablement
• R&D engineers and working professionals applying AI Enablement in industry
• Academics and educators building research or teaching capacity in AI Enablement
🚀 Key Learning Outcomes
• A portfolio-grade AI Enablement deliverable you can defend and extend.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Warehouse Fundamentals
• OLTP versus OLAP workloads and why they need different structures
• Dimensional modelling: facts, dimensions, star and snowflake schemas
• Slowly changing dimensions and preserving history correctly
ETL and Data Quality
• Extract, transform, load versus ELT in modern cloud warehouses
• Incremental loading, idempotency and late-arriving data
• Data quality testing and the cost of discovering errors downstream
Analytical SQL
• Aggregation, window functions and analytical query patterns
• Partitioning, clustering and query cost control
• Materialised views and pre-aggregation trade-offs
Pattern Discovery Techniques
• Association rule mining and interpreting support, confidence and lift
• Clustering and segmentation on warehouse data
• Classification and prediction using warehouse features
Trustworthy Analytics
• Lineage, cataloguing and documentation of derived tables
• Access control, masking and privacy in a central warehouse
• Avoiding metric divergence when every team defines revenue differently
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Microsoft Excel |
| Covered Tool / Platform | Relevant Online Databases |
Frequently Asked Questions
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