X hits on this document

84 views

0 shares

0 downloads

0 comments

14 / 27

  • 1.

    Paul Punnian, “Data Warehousing Fundamentals”, John Wiley Pub

  • 2.

    Han, Kamber, "Data Mining Concepts and Techniques", Morgan Kaufmann .

  • 3.

    Alex Berson, S.J. Smith, “Data Warehousing, Data Mining and OLAP”, Tata McGraw Hill

  • 4.

    Margaret Dunham, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Pub.

  • 5.

    Ralph Kimball, "The Data Warehouse Lifecycle toolkit', John Wiley.

  • 6.

    Jiawei Han, Micheline Kamber, “Data Mining : Concepts and Techniques”, 2nd edition, Morgan Kaufmann, ISBN 1558609016, 2006.

  • 7.

    A B M Shaukat Ali, Saleh A Wasimi, “Data Mining: Methods and Techniques”, Cengage Learning Pub.

  • 8.

    Ian Witten and Eibe Frank, Data Mining, “Practical Machine Learning Tools and Techniques with Java Implementations”, Morgan Kaufman, ISBN 1558605525, 1999,

Term Work: The term work shall consist of at least 10 experiments/ assignments based on the syllabus above. Assessment of term work should be done which will consider the points below and the marks should be awarded accordingly.

  • *

    Continuous lab assessment

    • *

      Actually performing practicals in the laboratory during the semester

Practical Examination: The Practical Examination shall consist of writing and performing an experiment / assignment and oral based on the syllabus as per the journal record. Duration of examination is three hours.

Suggestive List of experiments:

  • 1.

    Evolution of data management technologies, introduction to data warehousing concepts

  • 2.

    Develop an application to implement defining subject areas, design of fact and dimension

tables, data marts.

  • 3.

    Develop an application to implement OLAP, roll-up, drill-down, slice, and dice operations.

  • 4.

    Develop an application to construct a multidimensional data

  • 5.

    Develop an application to implement data generalization and summarization techniques

  • 6.

    Develop an application to extract association mining rules.

  • 7.

    Develop an application for classification of data.

  • 8.

    Develop an application for implementing one of the clustering technique

  • 9.

    Develop an application for implementing Naïve Bayes classifier

  • 10.

    Develop an application for Decision tree classifier

Title of the Subject: Microsoft Technologies Laboratory –II (ASP.NET) Course Code: MCA 210

Teaching Scheme: Practical: 4 Hrs/ week

Objectives:

  • -

    To Study website development using GUI environment.

  • -

    To develop programming skills with ASP.NET

Contents:

Examination Scheme: Practical Exam: 50 Marks Term Work: 50 Marks

Unit 1: Introduction Internet terminology, Web Server, Browser, Client Vs Server Side Scripting Introduction to Java Script (Client Side Script) – Variables, Document Object Model, Functions, Event Handling.

Document info
Document views84
Page views84
Page last viewedWed Dec 07 17:23:39 UTC 2016
Pages27
Paragraphs1177
Words8520

Comments