Unit 600: Big Data Analytics

1. Introduction to Big Data Analytics

• Definition of Big Data
• Characteristics of Big Data (Volume, Velocity, Variety, Veracity, Value)
• Importance of Analyzing Big Data
• Business Benefits and Use Cases of Big Data Analytics

2. Data Collection and Preprocessing

• Data Sources: Structured vs Unstructured Data
• Data Collection Methods and Tools
• Data Cleaning: Handling Missing Data, Noise, and Outliers
• Data Normalization Techniques
• Data Transformation and Feature Engineering

3. Data Storage and Management

• Overview of Big Data Storage Needs
• Hadoop Distributed File System (HDFS): Architecture and Features
• NoSQL Databases: Types (Document, Key-Value, Columnar, Graph), Use Cases
• Data Lakes vs Data Warehouses
• Scalability and Performance Considerations

4. Data Mining and Machine Learning

• Introduction to Data Mining in Big Data Context
• Clustering Algorithms (e.g., K-Means, Hierarchical Clustering)
• Classification Algorithms (e.g., Decision Trees, Random Forest, SVM)
• Regression Analysis
• Association Rule Mining
• Challenges of Machine Learning on Big Data

5. Data Visualization

• Importance of Visualization in Big Data Analytics
• Visualization Techniques: Charts, Graphs, Heatmaps, Dashboards
• Tools for Big Data Visualization (e.g., Tableau, Power BI, D3.js)
• Best Practices for Effective Data Communication

6. Sentiment Analysis and Text Mining

• Introduction to Unstructured Data in Big Data
• Natural Language Processing (NLP) Basics
• Techniques for Sentiment Analysis
• Text Mining Methods: Tokenization, Stemming, Lemmatization
• Applications: Social Media, Customer Feedback, Document Analysis

7. Real-time Analytics

• Concept of Real-time Data Processing
• Stream Processing Frameworks: Apache Kafka, Apache Storm
• Use Cases and Benefits of Real-time Analytics
• Challenges: Latency, Scalability, Fault Tolerance

8. Ethical and Privacy Issues in Big Data Analytics

• Data Security Concerns
• Privacy Regulations: GDPR, CCPA, and Others
• Bias and Fairness in Algorithms
• Responsible and Ethical Use of Big Data

9. Case Studies in Big Data Analytics

• Healthcare: Predictive Analytics for Patient Care
• Finance: Fraud Detection and Risk Management
• Marketing: Customer Segmentation and Personalization
• E-commerce: Recommendation Systems
• Lessons Learned and Best Practices