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Semester 4ITM4401IT

Data Warehousing & Data Mining (ITM4401) crash course, Online MBA, Semester 4:

This subject covers how data warehouses are designed and built, how OLAP and reporting tools support business analysis, and the core data mining techniques of…

  • 16 video lessons
  • 2h 46m
  • Study notes
  • 70 quiz questions
  • Assignment
Enroll nowSee the lessons
Data Warehousing & Data Mining crash course

16 lessons in Data Warehousing & Data Mining

Each lesson pairs one topic with a hand-picked video, trimmed to the part that teaches it.

About this subject

This subject covers how data warehouses are designed and built, how OLAP and reporting tools support business analysis, and the core data mining techniques of preprocessing, clustering, association rule mining and classification. After it you will be able to evaluate a data warehouse architecture, choose suitable analysis tools, and apply mining methods to find patterns and make predictions from business data.

  1. 1

    Data Warehouse Components and Building a Data Warehouse

    Module I, DATA WAREHOUSING: Data warehousing Components, Building a Data warehouse

    DWDM Unit 1 Complete Lecture | Data Warehouse, OLAP, Data Cubes & Schemas | BCS058· Bitwise Learning· 17 min

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  2. 2

    Data Warehouse on Multiprocessor Architecture and DBMS Schemas

    Module I, DATA WAREHOUSING: Mapping the Data Warehouse to a Multiprocessor Architecture, DBMS Schemas for Decision Support

    DWM6: Mapping the Data Warehouse to a Multiprocessor Architecture | Shared Memory,Disk and Nothing· University Academy· 9 min

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  3. 3

    ETL Tools and Metadata in Data Warehousing

    Module I, DATA WAREHOUSING: Data Extraction, Cleanup, and Transformation Tools, Metadata

    Lec - 3: ETL (Extract, Transform, Load) | Data 📊Aggregation | Data Warehouse🏭 & Mining ⛏️· Gate Smashers· 7 min

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  4. 4

    Reporting and Query Tools and Cognos Impromptu

    Module II, BUSINESS ANALYSIS: Reporting and Query tools and Applications, Tool Categories, The Need for Applications, Cognos Impromptu

    Introduction to Cognous Impromptu Administrator· Mallikarjunarao Kosuri· 4 min

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  5. 5

    OLAP and the Multidimensional Data Model

    Module II, BUSINESS ANALYSIS: Online Analytical Processing (OLAP), Need, Multidimensional Data Model, OLAP Guidelines

    Multi dimensional data model, Data Cube, Star, Snowflake, Fact Constellation Schema· Engineer's Easy Pathshala· 23 min

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  6. 6

    MOLAP vs ROLAP and OLAP Tools on the Internet

    Module II, BUSINESS ANALYSIS: Multidimensional versus Multi relational OLAP, Categories of Tools, OLAP Tools and the Internet

    OLAP Servers 🔥· Perfect Computer Engineer· 9 min

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  7. 7

    Introduction to Data Mining and Types of Data

    Module III, DATA MINING AND TRENDS IN DATA MINING: Introduction, Data, Types of Data, Data Mining Functionalities, Interestingness of Patterns

    #1 Introduction To Data Mining, Types Of Data |DM| #dm #data #datamining #jntu #btech· Trouble- Free· 11 min

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  8. 8

    Data Mining Systems, Task Primitives and Warehouse Integration

    Module III, DATA MINING AND TRENDS IN DATA MINING: Classification of Data Mining Systems, Data Mining Task Primitives, Integration of a Data Mining System with a Data Warehouse, Issues

    #4 Classification Of Data Mining Systems |DM|· Trouble- Free· 5 min

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  9. 9

    Data Preprocessing, Cluster and Outlier Analysis

    Module III, DATA MINING AND TRENDS IN DATA MINING: Data Preprocessing, Based Cluster Analysis, Outlier Analysis, Data Mining Applications

    #8 Data Preprocessing In Data Mining - 4 Steps |DM|· Trouble- Free· 18 min

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  10. 10

    Cluster Analysis Basics and Clustering Methods

    Module IV, CLUSTERING AND APPLICATIONS: Cluster Analysis, Types of Data, Categorization of Major Clustering Methods

    #22 Cluster Analysis - Properties, Categories Of Methods |DM|· Trouble- Free· 5 min

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  11. 11

    K-Means, Partitioning and Hierarchical Clustering

    Module IV, CLUSTERING AND APPLICATIONS: K means, Partitioning Methods, Hierarchical Methods

    #24 Partitioning Clustering - K Means Algorithm |DM|· Trouble- Free· 10 min

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  12. 12

    Density, Grid and Model-Based Clustering

    Module IV, CLUSTERING AND APPLICATIONS: Density-Based Methods, Grid Based Methods, Model-Based Clustering Methods, Clustering High Dimensional Data, Constraint

    #26 Density Based Clustering - DBSCAN Algorithm |DM|· Trouble- Free· 7 min

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  13. 13

    Frequent Pattern and Association Rule Mining

    Module V, ASSOCIATION RULE MINING AND CLASSIFICATION: Mining Frequent Patterns, Associations and Correlations, Mining Methods, Mining Various Kinds of Association Rules, Correlation Analysis, Constraint Based Association Mining

    L42: Association Rule Mining(ARM) | Apriori Algorithm | Example | Data Mining Lectures in Hindi· Easy Engineering Classes· 11 min

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  14. 14

    Classification Basics and Decision Tree Induction

    Module V, ASSOCIATION RULE MINING AND CLASSIFICATION: Classification and Prediction, Basic Concepts, Decision Tree Induction

    #18 Decision Tree Induction - with Example |DM|· Trouble- Free· 7 min

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  15. 15

    Bayesian and Rule-Based Classification

    Module V, ASSOCIATION RULE MINING AND CLASSIFICATION: Bayesian Classification, Rule Based Classification

    Naive Bayesian Classification Algorithm· Perfect Computer Engineer· 13 min

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  16. 16

    Backpropagation, SVM and Prediction

    Module V, ASSOCIATION RULE MINING AND CLASSIFICATION: Classification by Back propagation, Support Vector Machines, Associative Classification, Other Classification Methods, Prediction

    Lec-40: Support Vector Machines (SVMs) | Machine Learning· Gate Smashers· 10 min

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Assignment

Mini Data Warehouse & Mining Case: FreshMart Retail

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What you get

Less scrolling. More passing.

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    Hand-picked video lessons

    16 short lessons, each with the best YouTube explanation we could find, trimmed to the part that matters.

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    Study notes

    15 lessons come with a summary, key points, key terms and takeaways for quick revision.

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    Quizzes

    70 questions across the course, with the right answer and why, the moment you pick.

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    Assignments

    1 subject assignments with clear tasks, so you practise what each subject actually asks of you.

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    Notebook

    Write notes beside the video, stamped with the exact time, and jump back to that moment later.

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    Progress that follows you

    Completed lessons, quiz scores and where you stopped, saved to your account on every device.

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    Live chat

    A members-only live chat beside every lesson: doubts, tips, stickers and GIFs with your batch. One ₹19 pass opens every live chat.

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A live chat right beside the video, shared by everyone studying Online MBA. Ask a Data Warehousing & Data Mining doubt mid-lesson, share a shortcut, celebrate a finished semester.

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Data Warehousing & Data Mining: common questions

This subject covers how data warehouses are designed and built, how OLAP and reporting tools support business analysis, and the core data mining techniques of preprocessing, clustering, association rule mining and classification. After it you will be able to evaluate a data warehouse architecture, choose suitable analysis tools, and apply mining methods to find patterns and make predictions from business data. It's the Data Warehousing & Data Mining subject of Online MBA, Semester 4, in 16 lessons: Data Warehouse Components and Building a Data Warehouse; Data Warehouse on Multiprocessor Architecture and DBMS Schemas; ETL Tools and Metadata in Data Warehousing; Reporting and Query Tools and Cognos Impromptu; OLAP and the Multidimensional Data Model; MOLAP vs ROLAP and OLAP Tools on the Internet; Introduction to Data Mining and Types of Data; Data Mining Systems, Task Primitives and Warehouse Integration; Data Preprocessing, Cluster and Outlier Analysis; Cluster Analysis Basics and Clustering Methods; K-Means, Partitioning and Hierarchical Clustering; Density, Grid and Model-Based Clustering; Frequent Pattern and Association Rule Mining; Classification Basics and Decision Tree Induction; Bayesian and Rule-Based Classification; Backpropagation, SVM and Prediction.

Yes. Every video lesson is free to watch without an account. A free login unlocks study notes, quizzes, the assignment, a notebook and progress that syncs across devices.

About 2h 46m of video across 16 lessons, plus time for notes and quizzes. Go at your own pace; your place is saved.

Yes: “Mini Data Warehouse & Mining Case: FreshMart Retail”. It comes after the last lesson, with a brief, clear tasks and how the work is judged.

A members-only live chat beside the video, shared by everyone studying Online MBA, where learners ask doubts, share tips and react with stickers and GIFs. One ₹19 pass, paid once from your InsideEdge wallet, opens the live chat for every degree.

No. It's a crash course to help you understand and revise the subject quickly. Use it alongside your official study material and assignments.

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