Parallel Processing for Data Analytics

Topic

1. Course Outline

2. Python

3-1. Introduction to Spark

3-2. Map and Reduce Operations in Spark

3-3. Key-Value Pairs and Partitioning

3-4. Lazy Evaluation, Resilience & Persistence

3-5. Launching a Standalone Spark Cluster

4. Math Background Review

5-1. Convex Sets & Functions

5-2. Unconstrained Minimization & Gradient Descent

6-1. Regression and Statistical Learning

6-2. Feature Selection

6-3. Sparsity and Parallelism

6-4. Classification

6-5. SGD and Deep Learning