From 0 to 1 : Spark for Data Science with Python

From 0 to 1 : Spark for Data Science with Python
From 0 to 1 : Spark for Data Science with Python

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 8h 19m | 2.37 GB
eLearning | Skill level: All Levels

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

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.

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

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
+ Table of Contents

01 You, This Course and Us
02 What does Donald Rumsfeld have to do with data analysis
03 Why is Spark so cool
04 An introduction to RDDs – Resilient Distributed Datasets
05 Built-in libraries for Spark
06 Installing Spark
07 The PySpark Shell
08 Transformations and Actions
09 See it in Action – Munging Airlines Data with PySpark – I
10 [For Linux_Mac OS Shell Newbies] Path and other Environment Variables
11 RDD Characteristics – Partitions and Immutability
12 RDD Characteristics – Lineage, RDDs know where they came from
13 What can you do with RDDs
14 Create your first RDD from a file
15 Average distance travelled by a flight using map() and reduce() operations
16 Get delayed flights using filter(), cache data using persist()
17 Average flight delay in one-step using aggregate()
18 Frequency histogram of delays using countByValue()
19 See it in Action – Analyzing Airlines Data with PySpark – II
20 Special Transformations and Actions
21 Average delay per airport, use reduceByKey(), mapValues() and join()
22 Average delay per airport in one step using combineByKey()
23 Get the top airports by delay using sortBy()
24 Lookup airport descriptions using lookup(), collectAsMap(), broadcast()
25 See it in Action – Analyzing Airlines Data with PySpark – III
26 Get information from individual processing nodes using accumulators
27 See it in Action – Using an Accumulator variable
28 Long running programs using spark-submit
29 See it in Action – Running a Python script with Spark-Submit
30 Behind the scenes – What happens when a Spark script runs
31 Running MapReduce operations
32 See it in Action – MapReduce with Spark
33 The Java API and Function objects
34 Pair RDDs in Java
35 Running Java code
36 Installing Maven
37 See it in Action – Running a Spark Job with Java
38 What is PageRank
39 The PageRank algorithm
40 Implement PageRank in Spark
41 Join optimization in PageRank using Custom Partitioning
42 See it Action – The PageRank algorithm using Spark
43 Dataframes – RDDs + Tables
44 See it in Action – Dataframes and Spark SQL
45 Collaborative filtering algorithms
46 Latent Factor Analysis with the Alternating Least Squares method
47 Music recommendations using the Audioscrobbler dataset
48 Implement code in Spark using MLlib
49 Introduction to streaming
50 mplement stream processing in Spark using Dstreams
51 Stateful transformations using sliding windows
52 See it in Action – Spark Streaming
53 The Marvel social network using Graphs