Data Analytics

Course Syllabus

Name of the Course: Data Analytics
LTP structure of the course: 2-1-1
Objective of the course: The course discusses the methods & algorithms of data analysis and its related issues.
Outcome of the course: Students will get exposure of various algorithms to be used in different application domain for data analysis and its practical implementations.
Course Plan:

Component	Unit	Topics for Coverage
Component 1	Unit 1	Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data Mining systems, Major issues in Data Mining, Preprocessing the Data (Data Cleaning, Integration, Transformation & Reduction) Mining Association Rules: Association Rule Mining, Mining Single-Dimensional Boolean Association Rules from Transactional Databases, APRIORI, Variations of APRIORI (Sampling, Hash Based, Partitioning, Transaction Reduction), Frequent Pattern Growth, Mining Multilevel Association Rules from Transaction Databases, Mining Multidimensional Association Rules, Concept of LIFT, Clustering of Association rules.
Component 1	Unit 2	Classification and Prediction: Classification by Decision Tree Induction, Bayesian Classification.
Component 2	Unit 3	Classification continues: Classification by Back propagation, Classification Based on concepts from association Rule Mining, SVM, Prediction, and Classifier Accuracy.
Component 2	Unit 4	Clustering: Data types in cluster analysis, Categories of clustering methods, partitioning methods- K-Means, PAM, CLARA, CLARANS, KNN. Hierarchical Clustering- Agglomerative and Divisive Clustering, BIRCH and Chameleon, Density Based methods-DBSCAN, CURE, OPTICS, Grid Based Methods- STING, Wave Cluster, COBWEB, Outlier Analysis.

Text/ Reference Books:
References (papers from major conferences/journals):
- Jiawei Han MichelineKamberJian Pei “Data Mining: Concepts and Techniques” 3rd Edition, 2011
- Hadzic F., Tan H. & Dillon T. S. “Mining data with Complex Structures” Springer, 2011
- Yates R. B. and Neto B. R. “Modern Information Retrieval ” Pearson Education, 2005