Big Data Analytics with Apache Spark and Python

2016
9:30 hours

Section 1: Introduction
Lecture 1
About the course
08:42
Lecture 2
About V2 Maestros
01:39
Lecture 3
Resource Bundle
Article
Section 2: Overview
Lecture 4
Hadoop Overview
10:06
Lecture 5
HDFS Architecture
14:46
Lecture 6
Map Reduce – How it works
17:24
Lecture 7
Map Reduce – Example
16:46
Lecture 8
Hadoop Stack
06:27
Lecture 9
What is Spark?
14:03
Lecture 10
Spark Architecture – Part 1
13:23
Lecture 11
Spark Architecture – Part 2
13:25
Lecture 12
Installing Spark and Setting up for Python
12:05
Quiz 1
Hadoop and Spark Architecture
5 questions
Section 3: Programming with Spark
Lecture 13
Spark Transformations
11:33
Lecture 14
Spark Actions
15:04
Lecture 15
Advanced Spark Programming
10:10
Lecture 16
Python – Spark Programming examples 1
16:11
Lecture 17
Python – Spark Programming Examples 2
17:18
Quiz 2
Data Engineering with Spark
5 questions
Lecture 18
PRACTICE Exercise : Spark Operations
Article
Section 4: Spark SQL
Lecture 19
Spark SQL Overview
10:03
Lecture 20
Python – Spark SQL Examples
16:16
Quiz 3
Spark SQL
2 questions
Lecture 21
PRACTICE Exercise : Spark SQL
Article
Section 5: Spark Streaming
Lecture 22
Streaming with Apache Spark
15:53
Lecture 23
Python – Spark Streaming examples
17:47
Quiz 4
Spark Streaming
3 questions
Section 6: Real time Data Science
Lecture 24
Basic Elements of Data Science
11:51
Lecture 25
The Dataset
10:44
Lecture 26
Learning from relationships
12:55
Lecture 27
Modeling and Prediction
09:31
Lecture 28
Data Science Use Cases
07:47
Lecture 29
Types of Analytics
12:08
Lecture 30
Types of Learning
17:16
Lecture 31
Doing Data Science in real time with Spark
07:39
Quiz 5
Spark Data Science
5 questions
Section 7: Machine Learning with Spark
Lecture 32
Spark Machine Learning
12:18
Lecture 33
Analyzing Results and Errors
13:46
Lecture 34
Linear Regression
19:00
Lecture 35
Spark Use Case : Linear Regression
18:33
Lecture 36
Decision Trees
10:42
Lecture 37
Spark Use Case : Decision Trees Classification
14:58
Lecture 38
Principal Component Analysis
07:28
Lecture 39
Random Forests Classification
10:31
Lecture 40
Python Use Case : Random Forests & PCA
13:16
Lecture 41
Text Preprocessing with TF-IDF
14:53
Lecture 42
Naive Bayes Classification
19:21
Lecture 43
Spark Use Case : Naive Bayes & TF-IDF
07:26
Lecture 44
K-Means Clustering
11:53
Lecture 45
Spark Use Case : K-Means
14:26
Lecture 46
Recommendation Engines
11:55
Lecture 47
Spark Use Case : Collaborative Filtering
06:34
Lecture 48
Real Time Twitter Data Sentiment Analysis
10:11
Quiz 6
Spark Machine Learning Algorithms
4 questions
Lecture 49
PRACTICE Exercise : Spark Clustering
Article
Lecture 50
PRACTICE Exercise : Spark Classification
Article
Section 8: Conclusion
Lecture 51
Closing Remarks
01:56
Lecture 52
BONUS Lecture : Other courses you should check out
Article