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Data Warehousing and Mining
New
Rating: 5.0 out of 5(7 ratings)
10 students

Data Warehousing and Mining

From data storage to knowledge discovery, ETL, OLTP, Clustering and LZW algorithm
Created byMunazza Khan
Last updated 7/2026
English

What you'll learn

  • what is data warehousing and mining and why it is needed
  • Get in-depth knowledge of its methods and algorithms
  • learn concepts like ETL, DM schemas, OLTP, Clustering and more
  • understanding of complex topics with real life examples.

Course content

1 section19 lectures1h 24m total length
  • Introduction1:27
  • Agenda5:38
  • Data Warehousing4:16
  • Intro to ETL4:07
  • ETL vs ELT4:45
  • Difference between Data Lake, warehouse and Mining3:35
  • Star Schema5:04
  • Snowflake Schema4:06
  • Difference between Star and Snowflake Schema3:09
  • OLTP2:50
  • Data Cleaning and Preprocessing3:41
  • Types of Data2:05
  • Types of data7:21
  • Data Discretization4:54
  • Clustering4:26
  • Anomalies, Outliers3:34
  • LZW Algorithm6:43
  • Apriori Algorithm8:34
  • Sampling and its types4:02

Requirements

  • just the basic knowledge of IT and rest you'll learn here.

Description

From Data Storage to Knowledge Discovery

Explore the core concepts behind how data is organised, stored, and analysed. This theory-driven course covers data warehouse architecture, ETL processes, OLAP, and fundamental mining techniques like classification, clustering, and association. Designed for curious CS students and beginners who want to understand how and why data systems work before touching the tools.

No coding, just concepts. Learn the principles behind building data warehouses and extracting meaningful patterns from data. We’ll break down architectures, data models, mining algorithms, and evaluation methods in plain language. Perfect for CS beginners who want a strong conceptual foundation in data analytics.

The Theory Behind Data-Driven Decisions

Understand the science behind big data. This course walks you through warehouse design principles, data modelling, multidimensional analysis, and the key algorithms used in data mining. Built for computer science students who are curious about analytics but want to master the theory first.

Every company is sitting on a goldmine of data. In this course you’ll learn how to build the warehouse to store it, and use mining tools to dig out the patterns that matter. Built for beginners in computer science who are curious about AI, analytics, and how data drives the world.

Course focus:

Concepts: DW architecture, schemas, ETL, OLAP

Techniques: Classification, Clustering, Association, Prediction 

Thinking: How data turns into knowledge

Who this course is for:

  • For curious beginners and students of computer science who want to turn raw data into real insight.