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Default Sathyabama Institute of Science and Technology BE CSE SCSA1603 Big Data Analytics Syllabus

Sathyabama Institute of Science and Technology BE CSE SCSA1603 Big Data Analytics Syllabus

SATHYABAMA INSTITUTE OF SCIENCE AND TECHNOLOGY SCHOOL OF COMPUTING

SCSA1603 BIG DATA ANALYTICS
L T P Credits Total Marks
3 0 0 3 100

UNIT 1 INTRODUCTION 9 Hrs.
Introduction to Big Data – Issues and Challenges in the traditional systems - Evolution of Big Data – Four V’s of Big Data –
Big Data Use Cases and characteristics – Intelligent Data Analysis – Data Analytic Tools – Big Data StorageStatistical
Concepts: Sampling Distributions - Re-Sampling - Statistical Inference - Prediction Error – Random Sampling.

UNIT 2 BIG DATA TOOLS I 9 Hrs.
Big Data Applications using Pig and Hive – Fundamentals of HBase and ZooKeeper – IBM Infosphere Big Insights –
Introduction to FLUME – KAFKA.

UNIT 3 BIG DATA TOOLS II 9 Hrs.
Introduction to NoSQL - MongoDB – Spark – Cassandra - Cassandra Data Model – Data Design – Cassandra Architecture –
Read and Write Data – Clients – Integrate with Hadoop. Introduction - Importance of Effective Data Visualization -
Introduction to Tableau - Choosing the Right Chart Type - Using the Color Effectively Reducing Clutter - Dashboard Creation
and Formatting.

UNIT 4 HADOOP 9 Hrs.
Introduction to Hadoop – Hadoop Distributed File System – Analysing data with Hadoop – Scaling – Streaming – Clustering:
Single Node and Multi Node – Working with Hadoop Commands – Working with Apache Oozie.

UNIT 5 MAP REDUCE 9 Hrs.
Algorithms using map reduce - Matrix-Vector – Multiplication – Word Count - Understanding inputs and outputs of
MapReduce, Data Serialization – Introduction to YARN – MapReduce Vs YARN – YARN Architecture – Scheduling in YARN
– Fair Scheduler – Capacity Scheduler.
Max. 45 Hrs.

COURSE OUTCOMES
On completion of the course, student will be able to
CO1 - Configure the tools required for setting up Big Data Ecosystem.
CO2 - Understand conceptually how Big Data is stored and organized.
CO3 - Use appropriate models of analysis, assess the quality of input, derive insight from results, and investigate potential
issues.
CO4 - Interpret data findings effectively in visual formats.
CO5 - Explore the fundamentals of various big data applications.
CO6 - Implement the Algorithms for data analytics.

TEXT / REFERENCE BOOKS
1. Joshua N. Milligan, “Learning Tableau”, Packt Publishing, 2015.
2. Chuck Lam, “Hadoop in Action”, Manning Publications Co., 2018.
3. Tom White, “Hadoop the Definitive Guide”, Oreilly, 4th Edition, 2015.
4. Eben Hewitt, “Cassandra: The Definitive Guide”, O’Reilly, 2010.
5. Anand Rajaraman and Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge University Press, Edition I,
ISBN-10: 1107015359 | ISBN-13: 978-1107015357, 2011.
6. Jimmy Lin and Chris Dyer, “Data-Intensive Text Processing with MapReduce”, Morgan and Claypool Publishers, 2010.
7. Jonathan R. Owens, Brian Femiano, and Jon Lentz, “Hadoop Real World Solutions Cookbook”, Packt Publishing,
ISBN-10: 1849519129 | ISBN-13: 978-1849519120, 2013

END SEMESTER EXAMINATION QUESTION PAPER PATTERN
Max. Marks : 100 Exam Duration : 3 Hrs.
PART A: 10 Questions of 2 marks each-No choice 20 Marks
PART B: 2 Questions from each unit with internal choice, each carrying 16 marks 80 Marks
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