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Spark Development & Data Analyst

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

Module 9 – Anomaly Detection with K-means Clustering Anomaly Detection
K-means clustering
A First Take on Clustering
Choosing k
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
Determine the factor Weights
Visualizing the results
Evaluating results

The Trainers
Ms. Jun Guan
Senior Data Analyst
Senior Biostatistian
Mater Degree in Statistics, U of T

Ms. Joan Lin
Ph.D. in Computer Application
Senior Scientisit
Senior Statistian
The Achievements

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The Resources
Data Mining

OCOT Advantages

100 %Instructor-Led Class
State-of-the-Art Facilities
Unlimited Lab Time
Labs Open 7-days a Week
Free Repeat
Free Job Placement
Financial Aid Possible
Resume Writing
Interview Skills

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