Semester 3: Advanced Topics in Big Data Analytics

Semester 3: Advanced Topics in Big Data Analytics-

8. Machine Learning for Big Data
   • Introduction to machine learning
   • Supervised and unsupervised learning algorithms
   • Applying machine learning to Big Data
9. Data Visualization and Reporting
   • Data visualization techniques (e.g., Tableau, Power BI)
   • Design principles for effective data visualization
   • Reporting tools and dashboards
10. Big Data Analytics in Industry
    • Case studies and real-world applications of Big Data analytics
    • Ethical and legal considerations in Big Data

Reference Books:

1. “Advanced Analytics with Spark: Patterns for Learning from Data at Scale” by Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills
2. “Machine Learning Yearning” by Andrew Ng
3. “Big Data Analytics with Python” by Armando Fandango
4. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
5. “Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples” by Sayan Mukhopadhyay
6. “Graph Algorithms: Practical Examples in Apache Spark and Neo4j” by Mark Needham and Amy E. Hodler
7. “Data Science for Business and Decision Making” by Colleen McCue and James D. Savage