Data Warehousing With Mining Techniques detailed Syllabus for Computer Technology (CM), I – scheme has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below.
For all the other Computer Technology CM syllabus for 6th Sem, do visit Diploma in Computer Technology 6th Sem I – Scheme. For all the Computer Technology Elective-II subjects refer to Computer Technology Elective-II Scheme. The detail syllabus for data warehousing with mining techniques is as follows.
Rationale:
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Competency:
The aim of this course is to help the student to develop required skills so that they are able to acquire following competency:
Use Data Mining Tools for data analysis to maintain Datawarehouse
Course Outcomes:
The theory, practical experiences and relevant soft skills associated with this course are to be taught and implemented, so that the student demonstrates the following industry oriented COs associated with the above mentioned competency:
- Identify the scope and necessity of Data Mining & Warehousing for various applications.
- Use concept of data mining components and techniques in designing data mining systems.
- Use data mining tools for different applications.
- Solve basic Statistical calculations on Data
- Design a data mart or data warehouse for any organization
Suggested Exercises:
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Instruments Required:
The major equipment with broad specification mentioned here will usher in uniformity in conduct of experiments, as well as aid to procure equipment by authorities concerned.
Equipment Name with Broad Specifications
- Computer system (Any computer system with basic configuration.
Unit 1
Introduction to Data Warehousing
Total Teaching Hours – 04
Distribution of Theory Marks
R Level – 02
U Level – 02
A Level – 04
Total Marks – 08
Unit Outcomes (UOs) (in cognitive domain.
- Identify need of data warehousing.
- Describe architecture of datawarehouse.
- State the benefits of datawarehousing.
- Describe Datawarehouse Models.
- Describe Extraction ,Transformation and Loading
Topics and Sub-topics
- What is Data warehousing
- Difference between Operational Database System and Data warehouse
- Need for data warehousing
- A Multitiered Architecture of data warehousing.
- Data Warehouse Models: Enterprise Warehouse, Data Mart, and Virtual Warehouse.
- Extraction, Transformation, and Loading.
- Metadata Repository
- Benefits of Data warehousing.
Unit 2
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Unit 3
Online Analytical Processing
Total Teaching Hours – 10
Distribution of Theory Marks
R Level – 02
U Level – 02
A Level – 08
Total Marks – 12
Unit Outcomes (UOs) (in cognitive domain.
- Describe Various OLAP operations
- Compare OLAP and OLTP tools.
- State the benefits of OLAP tool.
- Explain Bitmap and Join Index for OLAP.
- Compare different OLAP server Architectures.
Topics and Sub-topics
- OLAP : Need of OLAP, OLAP Guidelines
- Typical OLAP Operations
- From Online Analytical Processing to Multidimensional Data Mining
- Data Warehouse ImplementationEfficient Data Cube Computation: An Overview.
- Indexing OLAP Data: Bitmap Index and Join Index, Efficient Processing of OLAP Queries
- OLAP Server Architectures: ROLAP Versus MOLAP versus HOLAP .
Unit 4
Introduction to Data Mining
Total Teaching Hours – 12
Distribution of Theory Marks
R Level – 02
U Level – 02
A Level – 10
Total Marks – 14
Unit Outcomes (UOs) (in cognitive domain.
- Explain concept of Data Mining.
- Describe steps in the process of Knowledge Discovery of Database.
- State Major issues in data mining.
- Explain data objects and attributes types.
- Describe methods of Data Preprocessing.
Topics and Sub-topics
- Data Mining: Why Data Mining ? What is Data Mining? Steps in the process of knowledge discovery of Database(KDD)
- What Kind of data can be mined? Major issues in data mining
- Data Objects and Attributes types
- Data Preprocessing: Why Preprocess the data? Major Tasks in Data Preprocessing
- Introduction to- Data Cleaning , Data Integration, Data Reduction and Data Transformation and Discretization.
Unit 5
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Suggested Student Activities:
Other than the classroom and laboratory learning, following are the suggested student-related co-curricular activities which can be undertaken to accelerate the attainment of the various outcomes in this course:
- Prepare journal of practicals.
- Undertake micro-projects.
Suggested Special Instructional Strategies:
These are sample strategies, which the teacher can use to accelerate the attainment of the various learning outcomes in this course:
- Massive open online courses (MOOCs) may be used to teach various topics/sub topics.
- L’ in item No. 4 does not mean only the traditional lecture method, but different types of teaching methods and media that are to be employed to develop the outcomes.
- About 15-20% of the topics/sub-topics which is relatively simpler or descriptive in nature is to be given to the students for self-directed learning and assess the development of the COs through classroom presentations (see implementation guideline for details).
- With respect to item No.10, teachers need to ensure to create opportunities and provisions for co-curricular activities.
- Guide students in undertaking micro-projects.
- Demonstrate students thoroughly before they start doing the practice.
- Encourage students to refer different websites to have deeper understanding of the subject.
- Observe continuously and monitor the performance of students in Lab.
Suggested Micro-Projects
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Suggested Learning Resources:
- Data mining concepts and techniques Jiawei Han and Micheline Kamber, Third Edition, Elsevier, 2012 Morgan Kaufmann Publications.
- Data warehousing , data mining and OLAP Alex Berson, Hill Edition, Thirteenth Reprint 2008, Tata McGraw Hill
- The Data warehouse life cycle tool Kit Ralph Kimball. John Wiley
Software/Learning Websites:
- To be added
Course Curriculum Development Committee
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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For detail Syllabus of all other subjects of Computer Technology, 6th Sem, scheme I -, visit Computer Technology 6th Sem Syllabus for I – Scheme.
For all Computer Technology results, visit MSBTE Computer Technology all semester results.