Page 46 - REC :: M.E. CSE Curriculum and Syllabus - R2019

CP19213 BIG DATA ANALYTICS LABORATORY Category L T P C
PE 0 0 4 2

Objectives:
⚫ To implement Map Reduce concept to process big data.
⚫ To apply linear models to analyze big data.
⚫ To analyze big data using machine learning techniques.
⚫ To realize storage of big data using Hbase, MongoDB.
⚫ To develop big data applications for streaming data using Apache Spark.

List of Experiments
1 Configure and run Hadoop and HDFS.
2 Implement word count/frequency programs using MapReduce.
3 Implement a MapReduce program to process a weather dataset.
4 Implement Linear and Logistic Regression.
5 Implement SVM/Decision tree classification techniques.
6 Implement clustering techniques – Hierarchical and K-Means.
7 Implement an application that stores big data in Hbase/MongoDB/Pig using Hadoop/R.
8 Install, Deploy & Configure Apache Spark Cluster.
9 Run Apache Spark applications using Scala.
10 Perform Data Visualization using any Online tools like Plot.ly, advanced Excel etc.,
11 Install and execute Rapid Miner/Tableau and perform data analysis. ( use www.data.gov.in for real-time data)
Total Contact Hours : 60

PLATFORM NEEDED
Hardware Systems with Virtualization Enabled, dual core processor with 8 GB RAM
Software Hadoop, R package, Spark, Rapid Miner, Tableau.

Course Outcomes:
Upon completion of the course, students will be able to
⚫ Process big data using Hadoop framework.
⚫ Build and apply linear and logistic regression models.
⚫ Perform data analysis with machine learning methods.
⚫ Perform graphical data analysis.
⚫ Create applications for big data analytics.