Big Data Analytics

I no longer offer this course.  But I put the notes (CUHK internal access only) and lab material online for education purpose.

1. Introduction to Big Data Analytics
   • Lab: Setting up your Environment (Course VM can be downloaded here).
2. MapReduce
   • Lab: Hadoop MapReduce Programming
3. Spark
   • Lab: Scala
   • Lab: Spark
4. Linear Regression
   • Lab: MLlib basic
   • Lab: Linear Regression using MLlib and Spark
5. Logistic Regression
   • Lab: Logistic Regression and Model Building using MLlib and Spark
6. Regularization
7. Miscellaneous about Machine Learning
8. SVM
   • Lab: SVM and Model Selection using MLlib and Spark
9. Graph Analytics
   • Lab: Giraph
   • Lab: GraphX
10. NoSQL data collection
    • Lab: NoSQL HBase
11. SQL on Hadoop
    • Lab: Spark SQL
12. Recommender Systems
13. Dimension Reduction Overview
14. Neural Network and Deep Learning Overview
15. Real-time Analytics
    • Lab: Spark Streaming

*Acknowledgment: Notes related to machine learning are largely based on Prof. Andrew Ng’s Machine Learning course at Coursea.  Many images in the notes are obtained from Google Images.  You may re-use my materials for any non-commercial purpose provided that you have done acknowledgement.

Further Readings/Watchings:

• Cloudera
• HortonWork
• R
• MOCC: Calculus One by Jim Fowler (Ohio)
• MOCC: Probability by Joe Blitzstein (Harvard)
• MOCC: Linear Algebra by Gilbert Strang (MIT)
• MOCC: Big Data courses based on Spark
• Book: Linear Algebra
• Spark Submit East 2015
• DataBricks Tech Talk
• Google Data Center
• Michael Franklin: Making Sense of Big Data with the Berkeley Data Analytics Stack
• Sam Madden: Big Data Challenges and Opportunities
• Michael Stonebraker: Vision and Research Program in “Big Data”
• H.V. Jagadish: Ethics of Big Data
• How AlphaGo Works
• Realtime Event Processing in Hadoop with Storm and Kafka