Learning Objectives
5 objectives- Understand the fundamental concepts and significance of big data analytics.
- Gain proficiency in data collection, preprocessing, storage, and management techniques for big data.
- Explore data mining, machine learning algorithms, and data visualization methods applied to big data.
- Analyze unstructured data using sentiment analysis and text mining techniques.
- Evaluate ethical, privacy, and real-time challenges in big data analytics and apply knowledge to real-world case studies.
Content Outline
PreviewUnit 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
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