BIG DATA ANALYTICS

Pre-requisite: Should have knowledge of one Programming Language (Java

preferably), Practice of SQL (queries and sub queries), exposure to Linux Environment.

Course Educational Objective: The Objective of the course is to provide practical,

foundation level training that enables immediate and effective participation in Big Data

and other Analytics projects using Hadoop and Data Visualization using Tableau.

Course Outcomes(CO): At the end of this course, the student will be able to:

CO1: Demonstrate the installation of Big data analytic tools.(Understand–L2)

CO2: Apply data modeling techniques to large datasets. (Apply–L3)

CO3: Conduct exploratory data analysis using visualization.(Understand–L2)

CO4: Improve individual / teamwork skills, communication & report writing skills with

ethical values.

List of Experiments

1. Refreshing Linux Commands and Installation of Hadoop

2. Implementation of Run a basic Word Count Map Reduce program

3. Implementation of Matrix Multiplication with Hadoop Map Reduce.

4. Implementation of Weather mining by taking weather dataset using Map Reduce.

5. Installation of Hive along with practice examples.

6. Installation of Sqoop along with Practice examples.

7. Downloading and installing Tableau Understanding about importing data,

saving,opening, and sharing work books.

8. Data Preparation with Tableau

9. Charts: Bar Charts, Legends, Filters ,and Hierarchies ,Step Charts, Line Charts.

10. Maps: Symbol Maps, Filled Maps, Density Maps, Maps with Pie Charts

11. Interactive Dash boards

TEXTBOOKS

1. Seema Acharya, Subhasini Chellappan,"BigDataAnalytics"Wiley2015

2. Alexander Loth,“Visual Analytics withTableau”,ISBN:978-1-119-56020-3, Wiley2019

REFERENCES

1. Tom White, “Hadoop: The Definitive Guide”,ThirdEditon,O‘reilyMedia,2012

2. Michael Berthold, David J. Hand, "Intelligent Data Analysis”Springer,2007.

3. JayLiebowitz,“BigDataandBusinessAnalytics”AuerbachPublications,CRC press(2013).

4. Anand Rajaraman and Jefrey David Ulman, “Mining of Massive Datasets”,