Big Data Analysis — Complete Notes & Syllabus

Updated: • Author: Tauqueer Alam

A complete, exam-oriented guide covering the full Big Data Analysis syllabus: Hadoop, HDFS, MapReduce, Data Mining streams, Visualization, and advanced modeling techniques. Ideal for semester exam preparation.

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UNIT I — Introduction to Big Data & Modelling Techniques

Topics covered: Introduction to Big Data, challenges of conventional systems, Evolution of analytic scalability, Modern data analytic tools.

  • Modelling techniques: Mining frequent itemsets, Apriori algorithm.
  • Handling Data: Handling large data sets in main memory.
  • Clustering & Prediction: Clustering techniques, clustering for parallelism, Classification and Prediction, Decision Tree induction, Developing models using Decision Tree Algorithms.

UNIT II — Big Data Frameworks

Overview of massive data processing frameworks ecosystems, essential for analyzing terabytes of unstructured data.

  • Hadoop & HDFS: Overview of Hadoop, Hadoop Distributed File System (HDFS) design and architecture.
  • Map Reduce: Hadoop Map reduce Framework.
  • Hecosystem: HBASE, Interacting HDFS using HIVE, sample programs in HIVE-PIG.

UNIT III — Data Analysis and Mining Data Streams

Focuses on analyzing and drawing insights from high-velocity data streams in real-time platforms.

  • Advanced Modelling: Regression modelling, Rule Induction Fuzzy decision trees and neural networks.
  • Stream Concepts: Introduction to streams concepts, Real-time analytics platform, case studies.

UNIT IV — Visualization & Analytics

Detailed discussion on visualizing big data and drawing association intelligence.

  • Visualization: Visual data analysis techniques, Interaction techniques.
  • Analytics: Analytics using statistical packages, association intelligence from unstructured information.
  • Industry Application: Text analytics, industry challenges and real-world application of analytics.

Why is Big Data Analysis Important?

As the volume of data generation grows exponentially globally across all domains, understanding how to clean, store, and process massive datasets using distributed systems like Hadoop and modeling with Decision Trees or Neural Networks ensures computational systems are efficient and insightful. This serves as a vital foundation for any future AI and Machine Learning endeavors.

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