Module 1 - Spark Introduction
Introduction Spark
What is Spark?
A brief History of Spark
Programming with RDDs
Module 2 - Advanced Spark Programming
Spark Storage - Loading and saving data
Advanced Spark Programming
Standalone applications
Module 3 - Spark SQL
Linking with Spark SQL
Using Spark SQL in Applications
JDBC/ODBC server
User-Defined Functions
Spark SQL Performance
Module 4 - Spark Streaming
Architecture and abstraction
Input/output operations
Streaming UI
Performance Considerations
Module 5 - Tuning and Debug Spark
Configuration Spark
Key Performance considerations
Module 6 - Running on Cluster
Runtime Architecture
Cluster Manage
rModule 7 - Machine Learning
Designing a Machine learning system
Building a Recommendation Engine with Spark
MLlib Decision Trees
Module 8 – Prediction with Decision tree
Decision tree
Training Examples
Preparing the data
A First Decision tree
Tuning Decision Trees
Making Predictions
Conclusions
Module 9 – Anomaly Detection with K-means Clustering Anomaly Detection
K-means clustering
A First Take on Clustering
Choosing k
Visualization
Feature Normalisation
Clustering in action
Module 10 – Exploring Property Location data
Loading data
Variables to explore
Exploring property value
Exploring lot size
Exploring costs
Exploring the year a property has been built
Exploring rent and income
Module 11 - Estimating Financial Risk through Mote Carlo Simulation
Build model
Getting the data
Preprocessing
Determine the factor Weights
Visualizing the results
Evaluating results
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