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Course Detail

Course Name Mining of Massive Datasets
Course Code 24ASD638
Program M.Sc. in Applied Statistics and Data Analytics
Credits 3
Campus Coimbatore , Kochi

Syllabus

Basics of Data Mining – computational approaches – statistical limits on data mining – MapReduce – Distributed File Systems . MapReduce. Algorithms using MapReduce. Extensions to MapReduce. Mining Data Streams: The Stream Data Model – Sampling Data in a Stream – Filtering Streams. Link analysis, Frequent itemsets, Clustering, Advertising on web, Recommendation system, Mining Social-Network Graphs, Dimensionality Reduction, Large-Scale Machine Learning.

Objectives and Outcomes

Course Outcomes:

CO1: Understand the basics of data mining and its limitations. 

CO2: Gain knowledge about data mining streams.

CO3: Understand the clustering techniques for data mining.

CO4: Apply the dimensionality reduction algorithm for social network analysis.

CO-PO Mapping:

PO1

PO2

PO3

PO4

PO5

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

2

2

2

2

2

2

–

–

–

–

1

1

CO2

3

3

2

2

2

2

–

–

–

–

1

1

CO3

2

2

3

2

2

2

–

–

–

–

1

1

CO4

3

3

3

2

2

2

–

–

–

–

1

1

Text Books / References

Text Books / References Books

  1. Jure Leskovec , Anand Rajaraman, Jeffrey David Ullman, Mining of Massive Datasets, Cambridge University Press, 2014.

2. Tom White, Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale O’Reilly Media; 4th edition, 2015.

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