Scala and Spark for Big Data and Machine Learning

Course Curriculum

Chapter 1: Course Introduction

Lecture 1: Introduction

Lecture 2: Course FAQs

Lecture 3: Scala and Spark Overview

Chapter 2: Scala IDE Options and Overview

Lecture 1: ScalaIDE Overview

Lecture 2: Computer Set-up Time!

Chapter 3: Windows Scala and Spark Set-up and Installation

Lecture 1: Windows Introduction

Lecture 2: Quick note about Windows Installation.

Lecture 3: Windows Scala and Spark Installation

Lecture 4: Atom Windows Installation

Lecture 5: Terminal Exericse

Chapter 4: Mac OS Setup and Installation

Lecture 1: Mac OS Installation and Setup

Chapter 5: Linux (Ubuntu) Setup and Installation

Lecture 1: Installing Scala and Spark on Linux (Ubuntu)

Chapter 6: Scala Programming: Level One

Lecture 1: Arithmetic and Numbers

Lecture 2: Values and Variables

Lecture 3: Booleans and Comparison Operators

Lecture 4: Strings and Basic Regex

Lecture 5: Tuples

Lecture 6: Scala Basics – Assessment Test Exercises

Lecture 7: Scala Basics Assessment Test Questions

Lecture 8: Scala Basics – Assessment Test Solutions

Chapter 7: Collections

Lecture 1: Intro to Collections

Lecture 2: Lists

Lecture 3: Arrays

Lecture 4: Sets

Lecture 5: Maps

Lecture 6: Collections – Assessment Test Exercise

Lecture 7: Scala Collections Assessment Test

Lecture 8: Collections Assessment Test – Solutions

Chapter 8: Scala Programming: Level Two

Lecture 1: Flow Control

Lecture 2: For Loops

Lecture 3: While Loops

Lecture 4: Functions

Lecture 5: Scala Programming Exercises

Lecture 6: Scala Programming Exercises – Solutions

Chapter 9: Spark DataFrames with Scala

Lecture 1: Quick Note for Windows Users!

Lecture 2: Introduction to Spark DataFrames

Lecture 3: DataFrames Overview

Lecture 4: Spark DataFrame Operations

Lecture 5: GroupBy and Aggregate Functions

Lecture 6: Missing data

Lecture 7: Date and Timestamps

Lecture 8: Quick Note on DataFrame Project

Lecture 9: DataFrame Project Exercises

Lecture 10: DataFrame Project – Solutions

Chapter 10: Introduction to Machine Learning

Lecture 1: Introduction to Machine Learning

Lecture 2: Machine Learning with Spark

Lecture 3: IntelliJ IDEA Installation Overview

Chapter 11: Regression with Spark

Lecture 1: Introduction to Linear Regression

Lecture 2: Introduction to Regression Section

Lecture 3: Linear Regression Documentation Example

Lecture 4: Alternate Linear Regression Data CSV File

Lecture 5: Linear Regression Walkthrough Part 1

Lecture 6: Linear Regression Walkthrough Part 2

Lecture 7: Linear Regression Exercise Project

Lecture 8: Linear Regression Project Solutions

Chapter 12: Classification with Spark

Lecture 1: Introduction to Classification

Lecture 2: Classification Documentation Example

Lecture 3: Spark Classification – Logistic Regression Example – Part 1

Lecture 4: Spark Classification – Logistic Regression Example – Part 2

Lecture 5: Logistic Regression Project Exercise

Lecture 6: Classification Project Solutions

Chapter 13: Model Evaluation

Lecture 1: Model Evaluation Overview

Lecture 2: Spark Model Evaluation – Documentation Example

Lecture 3: Spark – Model Evaluation – Regression Example

Chapter 14: Clustering with Spark

Lecture 1: Introduction to Clustering with Spark

Lecture 2: KMeans Theory Lecture

Lecture 3: Note on Kmeans

Lecture 4: Example of KMeans with Spark

Lecture 5: Clustering Project Exercise Overview

Lecture 6: Clustering Project Exercises – Solutions

Chapter 15: PCA with Spark

Lecture 1: PCA Theory Overview

Lecture 2: PCA with Spark – Documentation Example

Lecture 3: PCA with Spark – Project Exercise

Lecture 4: PCA Spark Exercise – Solutions

Chapter 16: DataBricks and Spark

Lecture 1: Databricks Overview

Lecture 2: Introduction to Spark Recommendation Systems

Lecture 3: Spark Recommender System Implementation

Lecture 4: Zeppelin Notebooks on AWS Elastic MapReduce

Lecture 5: So what's next?

Chapter 17: BONUS SECTION: THANK YOU!

Lecture 1: Bonus Lecture: