| Course Name | Mining of Massive Datasets |
| Course Code | 24ASD638 |
| Program | M.Sc. in Applied Statistics and Data Analytics |
| Credits | 3 |
| Campus | Coimbatore , Kochi |
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.
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 Books
2. Tom White, Hadoop: The Definitive Guide: Storage and Analysis at Internet Scale O’Reilly Media; 4th edition, 2015.
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