Data Mining

By Mr. L. Abraham David | St.John’s College, Palayamkottai

Learners enrolled: 279

Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Data mining is the extraction of hidden predictive information from large databases is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses.

Data mining methodology extracts hidden predictive information from large databases. With such a broad definition, however, an online analytical processing (OLAP) product or a statistical package could qualify as a data mining tool. Retail companies and the financial community are using data mining to analyze data and recognize trends to increase their customer base, predict fluctuations in interest rates, stock prices, and customer demand.

Summary

Course Status :	Upcoming
Course Type :	Core
Language for course content :	English
Duration :	12 weeks
Category :	Computer Science
Credit Points :	4
Level :	Undergraduate
Start Date :	14 Jul 2025
End Date :	31 Oct 2025
Enrollment Ends :	31 Aug 2025
Exam Date :
NCrF Level	5.5
Industry Details :	Software development
Industry Alignment :	Information Technology

Page Visits

Course layout

Week – I

1. Introduction to Data Mining

2. Introduction to Data Warehousing

3. OLAP

4. Trends in Data Warehousing

Week – II

5. Applications in Data Warehousing

6. Data Warehousing Architecture-I

7. Data Warehousing Architecture-II

8. Data Warehousing Architecture-III

Week – III

9. Data Warehousing Architecture-IV

10.Data Warehousing Architecture-V

11. Data Mining-I

12. Data Mining-II

Week – IV

13. Data Mining-III

14. Data Mining-IV

15. Data Mining-V

16. Data Mining-VI

Week – V

17. Data Mining-VII

18. Association Rule Mining-I

19. Association Rule Mining-II

Week – VI

20. Association Rule Mining-III

21. Association Rule Mining-IV

22. Association Rule Mining-V

Week – VII

23. Classification Techniques-I

24. Classification Techniques-II

Week – VIII

25. Classification Techniques-III

26. Classification Techniques-IV

Week – IX

27. Classification Techniques-V

28. Classification Techniques-VI

Week – X

29. Classification Techniques-VII

Week – XI

30. Other Data Mining Methods-I

31. Other Data Mining Methods-II

Week – XII

32. Other Data Mining Methods-III

33. Other Data Mining Methods-IV

Books and references

1. Jiawel Han and Micheline Kamber, “Data mining concepts and Techniques”

2. Berson, “Data Warehousing,Data Mining, OLAP”

3. Pang-Ning Tan, Michael Steinbach, Vipin Kumar “ Introduction to Data Mining”

4. Arun K Pujari, “Data Mining Techniques”

5. Dunham M H, “Data Mining-Introductory and Advanced Topics”

Instructor bio

Mr. L. Abraham David

St.John’s College, Palayamkottai

Mr.Abraham David L has been associated with Department Of Computer Application and M.Sc (NT & IT ), St.John’s college , Palayamkottai affiliated to M.S university Since 22nd August 2014. He has conducted a several seminar in college. He has invited from various college to deliver a guest talk. He has published a journal and 2 national conference in research articles. He has attended many seminars and workshops. He presented a E-content for EMRC Furthermore, he is involved in developing Massive Open Online Course (MOOC) in SWAYAM Platform.

Course certificate

Internal Assessment - Weekly assessments released in the course shall be considered for Internal Marks and will carry 30 percent for the Overall Result. Out of all weekly assignments, the best/top five scores will be considered for the final Internal Assessment marks.

End-term Assessment - The final exam shall be conducted by NTA, and will carry 70 percent for the overall Result.

All students who obtain 40% marks in the internal assessment and 40% marks in the end-term proctored exam separately will be eligible for the SWAYAM Credit Certificate.

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