The Complete 18ec72 | Big Data Analytics Notes

Description

VTU 18CS72 - BIG DATA ANALYTICS: Course Summary

Course Code: 18CS72

University: Visvesvaraya Technological University (VTU)

Summary:

What is it?

A specialized subject focusing on extracting insights and value from massive datasets. Combines computer science fundamentals with statistics, mathematics, and domain knowledge.

Why is it important?

High demand in diverse fields like healthcare, finance, retail, and more. Equips students with skills to manage, analyze, and interpret massive data volumes. Enables data-driven decision making for businesses and organizations.

Typical Course Coverage:

Foundations: Probability, statistics, data structures, algorithms, database systems.

Big Data Technologies: Hadoop, Spark, NoSQL databases, cloud computing platforms.

Data Analysis Techniques: Machine learning, data mining, predictive analytics, data visualization.

Domain Applications: Case studies and projects applying big data in specific fields.

Benefits for Students:

Develops valuable analytical and problem-solving skills.

Provides hands-on experience with cutting-edge tools and technologies.

Increases job marketability and career prospects in data-driven industries.

Overall:

Big data analytics is a rapidly evolving field offering exciting opportunities for computer science engineers. University courses equip students with the theoretical and practical knowledge to become data-driven problem solvers in a diverse range of sectors.

Additional points to consider:

The specific course content and structure may vary depending on the university.

Some programs offer specializations or concentration options within big data analytics.

Strong computational and critical thinking skills are essential for success in this field.

Course Learning Objectives: This course (18CS72) will enable students to:

• Understand fundamentals of Big Data analytics
• Explore the Hadoop framework and Hadoop Distributed File system
• Illustrate the concepts of NoSQL using MongoDB and Cassandra for Big Data
• Employ MapReduce programming model to process the big data
• Understand various machine learning algorithms for Big Data Analytics, Web Mining and Social Network Analysis.

Module 1

Introduction to Big Data Analytics: Big Data, Scalability and Parallel Processing, Designing Data Architecture, Data Sources, Quality, Pre-Processing and Storing, Data Storage and Analysis, Big Data Analytics Applications and Case Studies.

Module 2

Introduction to Hadoop (T1): Introduction, Hadoop and its Ecosystem, Hadoop Distributed File System, MapReduce Framework and Programming Model, Hadoop Yarn, Hadoop Ecosystem Tools.

Hadoop Distributed File System Basics (T2): HDFS Design Features, Components, HDFS User Commands. Essential Hadoop Tools (T2): Using Apache Pig, Hive, Sqoop, Flume, Oozie, HBase.

Module 3

NoSQL Big Data Management, MongoDB and Cassandra: Introduction, NoSQL Data Store, NoSQL Data Architecture Patterns, NoSQL to Manage Big Data, Shared-Nothing Architecture for Big Data Tasks, MongoDB, Databases, Cassandra Databases.

Module 4

MapReduce, Hive and Pig: Introduction, MapReduce Map Tasks, Reduce Tasks and MapReduce Execution, Composing MapReduce for Calculations and Algorithms, Hive, HiveQL, Pig.

Module 5

Machine Learning Algorithms for Big Data Analytics: Introduction, Estimating the relationships, Outliers, Variances, Probability Distributions, and Correlations,

Regression analysis, Finding Similar Items, Similarity of Sets and Collaborative Filtering, Frequent Itemsets and Association Rule Mining.

Text, Web Content, Link, and Social Network Analytics: Introduction, Text mining, Web Mining, Web Content and Web Usage Analytics, Page Rank, Structure of Web and analyzing a Web Graph, Social Network as Graphs and Social Network Analytics