ITC602: Data Mining and Business Intelligence Syllabus for IT 6th Sem 2018 Pattern Mumbai University

Data Mining and Business Intelligence detailed syllabus scheme for Information Technology (IT), 2018 regulation has been taken from the University of Mumbai official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.

For all other Mumbai University Information Technology 6th Sem Syllabus 2018 Pattern, do visit IT 6th Sem 2018 Pattern Scheme. The detailed syllabus scheme for data mining and business intelligence is as follows.

Data Mining and Business Intelligence Syllabus for Information Technology TE 6th Sem 2018 Pattern Mumbai University

Course Objectives:

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Course Outcomes:

Student will be able to:

1. Demonstrate an understanding of the importance of data mining and the principles of business intelligence
2. Organize and Prepare the data needed for data mining using pre preprocessing techniques
3. Perform exploratory analysis of the data to be used for mining.
4. Implement the appropriate data mining methods like classification, clustering or Frequent Pattern mining on large data sets.
5. Define and apply metrics to measure the performance of various data mining algorithms.
6. Apply BI to solve practical problems : Analyze the problem domain, use the data collected in enterprise apply the appropriate data mining technique, interpret and visualize the results and provide decision support.

Prerequisites:

Database Management System, Advanced Data Management Technology.

Module I

Introduction to Data Mining What is Data Mining; Kind of patterns to be mined; Technologies used; Major issues in Data Mining 03 CO1

Module II

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Module III

Classification Basic Concepts; Classification methods:

1. Decision Tree Induction: Attribute Selection Measures, Tree pruning.
2. Bayesian Classification: Naive Bayes” Classifier. Prediction: Structure of regression models; Simple linear regression, Multiple linear regression. Accuracy and Error measures, Precision, Recall, Holdout, Random Sampling, Cross Validation. 09 CO4 CO5

Module IV

Clustering Cluster Analysis: Basic Concepts; Partitioning Methods: K-Means, K-Mediods; Hierarchical Methods: Agglomerative, Divisive, BIRCH; Density-Based Methods: DBSCAN What are outliers? Types, Challenges; Outlier Detection Methods: Supervised, Semi Supervised, Unsupervised, Proximity based, Clustering Based. 10 CO4 CO5

Module V

Frequent Pattern Market Basket Analysis, Frequent Itemsets, Closed Itemsets, and 10 CO4 Mining Association Rules; Frequent Pattern Mining, Efficient and Scalable Frequent Itemset Mining Methods, The Apriori Algorithm for finding Frequent Itemsets Using Candidate Generation, Generating Association Rules from Frequent Itemsets, Improving the Efficiency of Apriori, A pattern growth approach for mining Frequent Itemsets; Mining Frequent itemsets using vertical data formats; Introduction to Mining Multilevel Association Rules and Multidimensional Association Rules; From Association Mining to Correlation Analysis, lift, ; Introduction to Constraint-Based Association Mining. CO5

Module VI

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
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Text Books:

1. Han, Kamber, “Data Mining Concepts and Techniques”, Morgan Kaufmann 3nd Edition.
2. P. N. Tan, M. Steinbach, Vipin Kumar, Introduction to Data Mining, Pearson Education.
3. Business Intelligence: Data Mining and Optimization for Decision Making by Carlo Vercellis ,Wiley India Publications.
4. G. Shmueli, N.R. Patel, P.C. Bruce, Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2nd Edition, Wiley India.

Reference Books:

1. Michael Berry and Gordon Linoff Data Mining Techniques, 2nd Edition Wiley Publications.
2. Michael Berry and Gordon Linoff Mastering Data Mining- Art & science of CRM, Wiley Student Edition.
3. Vikram Pudi & Radha Krishna, Data Mining, Oxford Higher Education.

Assessment:

Internal Assessment for 20 marks: Consisting of Two Compulsory Class Tests Approximately 40% to 50% of syllabus content must be covered in First test and remaining 40% to 50% of syllabus contents must be covered in second test. End Semester Theory Examination: Some guidelines for setting the question papers are as:

• Weightage of each module in end semester examination is expected to be/will be proportional to number of respective lecture hours mentioned in the syllabus.
• Question paper will comprise of total six questions, each carrying 20 marks.
• Q.1 will be compulsory and should cover maximum contents of the syllabus.
• Remaining question will be mixed in nature (for example if Q.2 has part
1. from module 3 then part
2. will be from any other module. (Randomly selected from all the modules)
• Total four questions need to be solved.

For detail syllabus of all other subjects of Information Technology (IT) 6th Sem 2018 regulation, visit IT 6th Sem Subjects syllabus for 2018 regulation.