From 0 to 1 : Spark for Data Science with Python [Video]

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From 0 to 1 : Spark for Data Science with Python [Video]

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Get your data to fly using Spark for analytics, machine learning and data science​
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Video Details

ISBN 139781788625708
Course Length8 hours 19 minutes

Video Description

Get your data to fly using Spark for analytics, machine learning and data science Let’s parse that. What's Spark? If you are an analyst or a data scientist, you're used to having multiple systems for working with data. SQL, Python, R, Java, etc. With Spark, you have a single engine where you can explore and play with large amounts of data, run machine learning algorithms and then use the same system to productionize your code.Analytics: Using Spark and Python you can analyze and explore your data in an interactive environment with fast feedback. The course will show how to leverage the power of RDDs and Dataframes to manipulate data with ease. Machine Learning and Data Science : Spark's core functionality and built-in libraries make it easy to implement complex algorithms like Recommendations with very few lines of code. We'll cover a variety of datasets and algorithms including PageRank, MapReduce and Graph datasets.

Style and Approach

A 8 hour high-quality courses available at super low prices to cover Spark, Machine Learning and Data Science

Table of Contents

You, This Course and Us
You, This Course and Us
Introduction to Spark
What does Donald Rumsfeld have to do with data analysis?
Why is Spark so cool?
An introduction to RDDs - Resilient Distributed Datasets
Built-in libraries for Spark
Installing Spark
The PySpark Shell
Transformations and Actions
See it in Action: Munging Airlines Data with PySpark – I
[For Linux/Mac OS Shell Newbies] Path and other Environment Variables
Resilient Distributed Datasets
RDD Characteristics: Partitions and Immutability
RDD Characteristics: Lineage, RDDs know where they came from
What can you do with RDDs?
Create your first RDD from a file
Average distance travelled by a flight using map() and reduce() operations
Get delayed flights using filter(), cache data using persist()
Average flight delay in one-step using aggregate()
Frequency histogram of delays using countByValue()
See it in Action: Analyzing Airlines Data with PySpark – II
Advanced RDDs: Pair Resilient Distributed Datasets
Special Transformations and Actions
Average delay per airport, use reduceByKey(), mapValues() and join()
Average delay per airport in one step using combineByKey()
Get the top airports by delay using sortBy()
Lookup airport descriptions using lookup(), collectAsMap(), broadcast()
See it in Action: Analyzing Airlines Data with PySpark – III
Advanced Spark: Accumulators, Spark Submit, MapReduce, Behind The Scenes
Get information from individual processing nodes using accumulators
See it in Action: Using an Accumulator variable
Long running programs using spark-submit
See it in Action: Running a Python script with Spark-Submit
Behind the scenes: What happens when a Spark script runs?
Running MapReduce operations
See it in Action: MapReduce with Spark
Java and Spark
The Java API and Function objects
Pair RDDs in Java
Running Java code
Installing Maven
See it in Action: Running a Spark Job with Java
PageRank: Ranking Search Results
What is PageRank?
The PageRank algorithm
Implement PageRank in Spark
Join optimization in PageRank using Custom Partitioning
See it Action: The PageRank algorithm using Spark
Spark SQL
Dataframes: RDDs + Tables
See it in Action: Dataframes and Spark SQL
MLlib in Spark: Build a recommendations engine
Collaborative filtering algorithms
Latent Factor Analysis with the Alternating Least Squares method
Music recommendations using the Audioscrobbler dataset
Implement code in Spark using MLlib
Spark Streaming
Introduction to streaming
Implement stream processing in Spark using Dstreams
Stateful transformations using sliding windows
See it in Action: Spark Streaming
Graph Libraries
The Marvel social network using Graphs

What You Will Learn

  • Use Spark for a variety of analytics and Machine Learning tasks
  • Implement complex algorithms like PageRank or Music Recommendations
  • Work with a variety of datasets from Airline delays to Twitter, Web graphs, Social networks and Product Ratings
  • Use all the different features and libraries of Spark : RDDs, Dataframes, Spark SQL, MLlib, Spark Streaming and GraphX

Authors

Table of Contents

You, This Course and Us
You, This Course and Us
Introduction to Spark
What does Donald Rumsfeld have to do with data analysis?
Why is Spark so cool?
An introduction to RDDs - Resilient Distributed Datasets
Built-in libraries for Spark
Installing Spark
The PySpark Shell
Transformations and Actions
See it in Action: Munging Airlines Data with PySpark – I
[For Linux/Mac OS Shell Newbies] Path and other Environment Variables
Resilient Distributed Datasets
RDD Characteristics: Partitions and Immutability
RDD Characteristics: Lineage, RDDs know where they came from
What can you do with RDDs?
Create your first RDD from a file
Average distance travelled by a flight using map() and reduce() operations
Get delayed flights using filter(), cache data using persist()
Average flight delay in one-step using aggregate()
Frequency histogram of delays using countByValue()
See it in Action: Analyzing Airlines Data with PySpark – II
Advanced RDDs: Pair Resilient Distributed Datasets
Special Transformations and Actions
Average delay per airport, use reduceByKey(), mapValues() and join()
Average delay per airport in one step using combineByKey()
Get the top airports by delay using sortBy()
Lookup airport descriptions using lookup(), collectAsMap(), broadcast()
See it in Action: Analyzing Airlines Data with PySpark – III
Advanced Spark: Accumulators, Spark Submit, MapReduce, Behind The Scenes
Get information from individual processing nodes using accumulators
See it in Action: Using an Accumulator variable
Long running programs using spark-submit
See it in Action: Running a Python script with Spark-Submit
Behind the scenes: What happens when a Spark script runs?
Running MapReduce operations
See it in Action: MapReduce with Spark
Java and Spark
The Java API and Function objects
Pair RDDs in Java
Running Java code
Installing Maven
See it in Action: Running a Spark Job with Java
PageRank: Ranking Search Results
What is PageRank?
The PageRank algorithm
Implement PageRank in Spark
Join optimization in PageRank using Custom Partitioning
See it Action: The PageRank algorithm using Spark
Spark SQL
Dataframes: RDDs + Tables
See it in Action: Dataframes and Spark SQL
MLlib in Spark: Build a recommendations engine
Collaborative filtering algorithms
Latent Factor Analysis with the Alternating Least Squares method
Music recommendations using the Audioscrobbler dataset
Implement code in Spark using MLlib
Spark Streaming
Introduction to streaming
Implement stream processing in Spark using Dstreams
Stateful transformations using sliding windows
See it in Action: Spark Streaming
Graph Libraries
The Marvel social network using Graphs

Video Details

ISBN 139781788625708
Course Length8 hours 19 minutes
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