Working with Big Data LiveLessons (Video Training): Infrastructure, Algorithms, and Visualizations

Table of contents

1. Introduction
   1. Introduction to Working with Big Data LiveLessons 00:03:17
   2. What is Big Data? 00:05:26
2. Lesson 1: Unstructured Storage and Hadoop
   1. Learning objectives 00:00:49
   2. 1.1 Set up a basic Hadoop installation 00:16:14
   3. 1.2 Write data into the Hadoop file system 00:07:41
   4. 1.3 Write a Hadoop streaming job to process text files 00:17:55
3. Lesson 2: Structured Storage and Cassandra
   1. Learning objectives 00:01:00
   2. 2.1 Set up a basic Cassandra installation 00:10:16
   3. 2.2 Create a Cassandra schema for storing data 00:17:04
   4. 2.3 Store and retrieve data from Cassandra using the Ruby library 00:07:39
   5. 2.4 Write data into Cassandra from a Hadoop streaming job 00:20:14
   6. 2.5 Use the Hadoop reduce phase to parallelize writes 00:15:09
4. Lesson 3: Real Time Processing and Messaging
   1. Learning objectives 00:01:07
   2. 3.1 Set up the Kafka messaging system 00:08:02
   3. 3.2 Publish and consume data from Kafka in Ruby 00:11:05
   4. 3.3 Aggregate log files into Hadoop using Kafka and a Ruby consumer 00:13:55
   5. 3.4 Create horizontally scalable message consumers 00:11:35
   6. 3.5 Sample messages using Kafka’s partitioning 00:10:47
   7. 3.6 Create redundant message consumers for high availability 00:27:50
5. Lesson 4: Working with Machine Learning Algorithms
   1. Learning objectives 00:00:57
   2. 4.1 Grasp the concepts of machine learning and implement the k-nearest neighbors algorithm 00:25:47
   3. 4.2 Understand the basics of distance metrics and implement euclidean distance and cosine similarity 00:26:44
   4. 4.3 Transform raw data into a matrix and convert a text document into the vector space model 00:22:42
   5. 4.4 Use k-nearest neighbors to make predictions 00:18:41
   6. 4.5 Improve execution time by reducing the search space 00:11:08
6. Lesson 5: Experimentation and Running Algorithms in Production
   1. Learning objectives 00:00:58
   2. 5.1 Use cross validation to test a predictive model 00:17:37
   3. 5.2 Integrate a trained model into production 00:09:06
   4. 5.3 Version a model and track feedback data 00:03:35
   5. 5.4 Write a test harness to compare versioned models 00:09:22
   6. 5.5 Test new predicted models in production 00:02:41
7. Lesson 6: Basic Visualizations
   1. Learning objectives 00:00:53
   2. 6.1 Prepare raw data for use in visualizations 00:13:10
   3. 6.2 Use core functions of the D3 JavaScript visualizaiton toolkit 00:13:17
   4. 6.3 Use D3 to create a barchart 00:07:56
   5. 6.4 Use D3 to create a time series 00:15:29