Course Overview

Big Data Analytics with R Programming

Master Data-Driven Decision Making with Industry-Ready Big Data Skills

In today’s digital economy, data is the new currency. This comprehensive course on Big Data Analytics using R equips learners with the skills to process massive datasets, uncover insights, build predictive models, and work with real-time analytics platforms used across industries.

Whether you’re an aspiring data analyst, data engineer, business intelligence professional, or someone looking to break into the big data domain, this course provides hands-on, practical, and job-oriented expertise.

What You Will Learn

1. Concept of Big Data

Understand the world of large-scale data

Learn what Big Data means, the 5Vs (Volume, Velocity, Variety, Veracity, and Value), and how organisations leverage data-driven strategies to improve business outcomes.

2. Challenges with Conventional Systems

Why traditional tools fail in a Big Data environment

Explore performance limitations, scalability issues, storage constraints, and processing delays faced by conventional systems—and why modern businesses need distributed architectures.

3. Structured & Unstructured Data

Master the foundation of enterprise data

Understand key differences between structured, semi-structured, and unstructured data. Work with text, logs, images, social data, and relational datasets using appropriate R tools and packages.

4. The Hadoop Framework

Your entry point to scalable Big Data processing

Learn Hadoop’s core ecosystem: HDFS, MapReduce, YARN, Hive, Pig, and HBase. Understand how big data is stored, processed, and distributed across clusters.

5. Data Analysis with R

Hands-on analytics to extract meaningful insights

Perform data cleaning, transformation, exploratory analysis, data visualisation, and statistical modelling using R. Learn to work with large datasets using optimised R functions.

6. Regression and Classification Models

Build predictive models that solve real business problems

Develop machine learning models, including:

• Linear & Multiple Regression
• Logistic Regression
• Decision Trees
• Naive Bayes
• SVM

• Random Forest

Learn how to evaluate models, measure accuracy, and improve performance.

7. Real-Time Analytics Platforms

Work with fast, live-streaming data environments

Understand platforms like Apache Kafka, Spark Streaming, and Flink. Learn how companies process data in milliseconds for fraud detection, monitoring, and customer analytics.

8. Stream Data Mining

Analyze data that never stops flowing

Explore techniques for mining continuous data streams, anomaly detection, pattern identification, and real-time decision-making using R and big data tools.

9. Analytics Tools and Packages

Master the most powerful R libraries

Get hands-on experience with:

• dplyr
• ggplot2
• tidyr
• data.table
• caret
• mlr
• sparklier
• Radoop

Learn how to integrate R with Hadoop, Spark, and other enterprise analytics ecosystems.

Course Outcomes

By the end of this course, you will be able to:

• Process, analyze, and visualize massive datasets
• Build predictive and classification models using R
• Work with Hadoop ecosystem tools and distributed systems
• Implement real-time data streaming and Big Data pipelines
• Apply analytics skills to real business use cases
• Become job-ready for roles such as Big Data Analyst, Data Scientist, or Machine Learning Engineer

This course includes