BCS058 Data Warehousing and Data Mining Syllabus
AKTU B.Tech · 3rd Year · Unit-wise syllabus (effective 2024-25)
BCS058 Data Warehousing and Data Mining (also written as KCS058) is a subject in the 3rd year of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University with an L-T-P of 3-0-0. The syllabus is divided into 5 units: Data Warehousing, Data Warehouse Process and Technology, Data Mining and Data Reduction, Classification and Clustering. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCS058 Subject Details
| Subject Code | BCS058 / KCS058 |
|---|---|
| Subject Name | Data Warehousing and Data Mining |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year |
| L-T-P | 3-0-0 |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-17 |
BCS058 Data Warehousing and Data Mining Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: Data Warehousing
Data Warehousing: Overview, Definition, Data Warehousing Components, Building a Data Warehouse, Warehouse Database, Mapping the Data Warehouse to a Multiprocessor Architecture, Difference between Database System and Data Warehouse, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept
Unit 2: Data Warehouse Process and Technology
Data Warehouse Process and Technology: Warehousing Strategy, Warehouse /management and Support Processes, Warehouse Planning and Implementation, Hardware and Operating Systems for Data Warehousing, Client/Server Computing Model & Data Warehousing. Parallel Processors & Cluster Systems, Distributed DBMS implementations, Warehousing Software, Warehouse Schema Design,
Unit 3: Data Mining · Data Reduction
Data Mining: Overview, Motivation, Definition & Functionalities, Data Processing, Form of Data Pre-processing, Data Cleaning: Missing Values, Noisy Data, (Binning, Clustering, Regression, Computer and Human inspection), Inconsistent Data, Data Integration and Transformation. Data Reduction:-Data Cube Aggregation, Dimensionality reduction, Data Compression, Numerosity Reduction, Discretization and Concept hierarchy generation, Decision Tree.
Unit 4: Classification · Clustering
Classification: Definition, Data Generalization, Analytical Characterization, Analysis of attribute relevance, Mining Class comparisons, Statistical measures in large Databases, Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based Algorithms. Clustering: Introduction, Similarity and Distance Measures, Hierarchical and Partitional Algorithms. Hierarchical Clustering- CURE and Chameleon. Density Based Methods-DBSCAN, OPTICS. Grid Based Methods- STING, CLIQUE. Model Based Method –Statistical Approach, Association rules: Introduction, Large Item sets, Basic Algorithms, Parallel and Distributed Algorithms, Neural Network approach.
Unit 5
Data Visualization and Overall Perspective: Aggregation, Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Data Mining interface, Security, Backup and Recovery, Tuning Data Warehouse, Testing Data Warehouse. Warehousing applications and Recent Trends: Types of Warehousing Applications, Web Mining, Spatial Mining and Temporal Mining
BCS058 Course Outcomes
- CO 1 At the end of course , the student will be able to understand K1 , K2
- CO 2 Be familiar with mathematical foundations of data mining tools..
- Understand and implement classical models and algorithms in data warehouses and data mining K3
- Characterize the kinds of patterns that can be discovered by association rule mining, K1 , K2
- classification and clustering.
- Master data mining techniques in various applications like social, scientific and environmental K3
- CO 4 context.
- CO 5 Develop skill in selecting the appropriate data mining algorithm for solving practical problems. K1 , K2
BCS058 Reference Books
- Alex Berson, Stephen J. Smith “Data Warehousing, Data-Mining & OLAP”, TMH
- Mark Humphries, Michael W. Hawkins, Michelle C. Dy, “ Data Warehousing: Architecture and Implementation”,
- Margaret H. Dunham, S. Sridhar,”Data Mining:Introductory and Advanced Topics” Pearson Education
- Arun K. Pujari, “Data Mining Techniques” Universities Press
- Pieter Adriaans, Dolf Zantinge, “Data-Mining”, Pearson Education
Frequently Asked Questions about BCS058
What is the syllabus of BCS058 Data Warehousing and Data Mining?
The AKTU BCS058 Data Warehousing and Data Mining syllabus is divided into 5 units: Data Warehousing; Data Warehouse Process and Technology; Data Mining and Data Reduction; Classification and Clustering. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCS058?
BCS058 Data Warehousing and Data Mining has 5 units in the AKTU syllabus, with an L-T-P (Lecture-Tutorial-Practical) structure of 3-0-0.
Is BCS058 the same subject as KCS058?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCS058 and KCS058 depending on the scheme year. The syllabus content is the same.
Where can I download BCS058 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCS058?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-17.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-17.