5th Sem, RP

5302: Data Mining Syllabus for Robotics Process Automation 5th Sem 2021 Revision SITTTR

Data Mining detailed syllabus for Robotics Process Automation (RP) for 2021 revision curriculum has been taken from the SITTTRs official website and presented for the Robotics Process Automation students. For course code, course name, number of credits for a course and other scheme related information, do visit full semester subjects post given below.

For Robotics Process Automation 5th Sem scheme and its subjects, do visit Robotics Process Automation (RP) 5th Sem 2021 revision scheme. The detailed syllabus of data mining is as follows.

Course Objectives:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

Course Outcomes:

On completion of the course student will be able to:

  1. Understand the core concepts of Data Mining and Data Warehousing
  2. Apply appropriate techniques to convert raw data into suitable format for practical data mining tasks and understand different Classification Models.
  3. Analyze and compare various classification algorithms and apply in appropriate domain.
  4. Make use of the concept of association rule mining in real world scenario. Select appropriate clustering and algorithms for various applications

Module 1:

Introduction to Data Mining – Data Mining Concepts and Applications, Data Mining Stages, Data Mining Models, Applications of Data Mining, Data Warehousing (DWH) and On-Line Analytical Processing (OLAP), OLAP vs OLTP, OLAP operations, Schemas of multidimensional Data model-Star schema, Snowflake schema and Fast Constellation, Applications of DWH .

Module 2:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

Module 3:

Artificial Neural Network-concepts and Characteristics, Back propagation Algorithm. Support Vector Machines, Lazy Learners-K Nearest Neighbor Classifier. Accuracy and error Measures evaluation. Prediction:-Linear Regression

Module 4:

Association Rule Mining: Concepts- support and confidence, FP-Growth AlgorithmCluster Analysis-Introduction, Concepts, Types of Data in cluster analysis, Categorization and Clustering Method.
Partitioning Method: K-Means an K-Medoid Clustering.
Hierarchical Clustering method: Agglomerative hierarchical clustering, BIRCH.

Text Books:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

Online Resources

  1. https://www.investopedia.com/terms/d/datamining.asp
  2. https://www.techtarget.com/
  3. https://www.javatpoint.com/data-mining
  4. https://www.electronicshub.org/artificial-neural-networks-ann/
  5. https://www.tutorialspoint.com/data_mining/
  6. https://towardsdatascience.com/the-fp-growth-algorithm

For detailed syllabus of all other subjects of Robotics Process Automation (RP), 2021 revision curriculum do visit Robotics Process Automation 5th Sem subject syllabuses for 2021 revision.

To see the syllabus of all other branches of diploma 2021 revision curriculum do visit SITTTR diploma all branches syllabus..

To see the results of Robotics Process Automation (RP) of diploma 2021 revision curriculum do visit SITTTR diploma Robotics Process Automation (RP) results..

For all Robotics Process Automation academic calendars, visit Robotics Process Automation all semesters academic calendar direct link.

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