1. Introduction to R/Python programming Language. Write program to read the data from any online website, excel file and CSV file.

2.Learning limitation of data analytics by applying Machine Learning Techniques on large amount of data. Write R/Python program to Read data set from any online website, excel file and CSV file and to perform

a) Linear regression and logistic regression on iris dataset.

b) K-means clustering.

3. Setup single node Hadoop cluster and apply HDFS commands on single node Hadoop Cluster.

4.Design MapReduce algorithms to take a very large file of integers and produce as output:

a) The largest integer

b) The average of all the integers.

c) The same set of integers, but with each integer appearing only once. *

d) The count of the number of distinct integers in the input.*

5. Apply MapReduce algorithms to perform analytics on single node cluster:

a) Analyse phrase frequency from given dataset

b) Search Records with matching criteria

c) Aggregate inputs and search records based on aggregation output

Prepare a report to guide design of mapper and reducer

6. Analyse impact of different number of mapper and reducer on same definition as practical 3 Prepare a conclusive report on analysis.

7. Implement PCY/Multi-Hash/SON algorithm for identification of frequent item set by handling larger datasets in main memory

8. Setup MongoDB environment in your system. Import Restaurant Dataset and perform CRUD operation

9. Extend MongoDB functionality for MapReduce on document collection

10.Case study: Use following platforms for solving any big data analytic problem of your choice. (1) Amazon web services,(2) Micosoft Azure, (3)Google App engine