Spark and Python for Big Data with PySpark
Learn the latest Big Data Technology - Spark! And learn to use it with one of the most popular programming languages, Python! One of the most valuable technology skills is the ability to analyze huge data sets, and this course is specifically designed to bring you up to speed on one of the best technologies for this task, Apache Spark! The top technology companies like Google, Facebook, Netflix, Airbnb, Amazon, NASA, and more are all using Spark to solve their big data problems!
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Spark can perform up to 100x faster than Hadoop MapReduce, which has caused an explosion in demand for this skill! Because the Spark 2.0 DataFrame framework is so new, you now have the ability to quickly become one of the most knowledgeable people in the job market!
This course will teach the basics with a crash course in Python, continuing on to learning how to use Spark DataFrames with the latest Spark 2.0 syntax! Once we've done that we'll go through how to use the MLlib Machine Library with the DataFrame syntax and Spark. All along the way you'll have exercises and Mock Consulting Projects that put you right into a real world situation where you need to use your new skills to solve a real problem!
We also cover the latest Spark Technologies, like Spark SQL, Spark Streaming, and advanced models like Gradient Boosted Trees! After you complete this course you will feel comfortable putting Spark and PySpark on your resume!
If you're ready to jump into the world of Python, Spark, and Big Data, this is the course for you!
- General Programming Skills in any Language (Preferrably Python)
- 20 GB of free space on your local computer (or alternatively a strong internet connection for AWS)
- Someone who knows Python and would like to learn how to use it for Big Data
- Someone who is very familiar with another programming language and needs to learn Spark
What you'll learn:
- Use Python and Spark together to analyze Big Data
- Learn how to use the new Spark 2.0 DataFrame Syntax
- Work on Consulting Projects that mimic real world situations!
- Classify Customer Churn with Logisitic Regression
- Use Spark with Random Forests for Classification
- Learn how to use Spark's Gradient Boosted Trees
- Use Spark's MLlib to create Powerful Machine Learning Models
- Learn about the DataBricks Platform!
- Get set up on Amazon Web Services EC2 for Big Data Analysis
- Learn how to use AWS Elastic MapReduce Service!
- Learn how to leverage the power of Linux with a Spark Environment!
- Create a Spam filter using Spark and Natural Language Processing!
- Use Spark Streaming to Analyze Tweets in Real Time!
Watch Online Spark and Python for Big Data with PySpark
| # | Title | Duration |
|---|---|---|
| 1 | Introduction | 03:10 |
| 2 | Course Overview | 07:56 |
| 3 | What is Spark? Why Python? | 18:58 |
| 4 | Set-up Overview | 05:59 |
| 5 | Local Installation VirtualBox Part 1 | 11:26 |
| 6 | Local Installation VirtualBox Part 2 | 14:00 |
| 7 | Setting up PySpark | 05:46 |
| 8 | AWS EC2 Set-up Guide | 02:47 |
| 9 | Creating the EC2 Instance | 16:19 |
| 10 | SSH with Mac or Linux | 04:50 |
| 11 | Installations on EC2 | 15:06 |
| 12 | Databricks Setup | 11:42 |
| 13 | AWS EMR Setup | 17:17 |
| 14 | Introduction to Python Crash Course | 01:34 |
| 15 | Jupyter Notebook Overview | 06:50 |
| 16 | Python Crash Course Part One | 16:09 |
| 17 | Python Crash Course Part Two | 12:08 |
| 18 | Python Crash Course Part Three | 11:20 |
| 19 | Python Crash Course Exercises | 01:30 |
| 20 | Python Crash Course Exercise Solutions | 09:27 |
| 21 | Introduction to Spark DataFrames | 02:27 |
| 22 | Spark DataFrame Basics | 10:52 |
| 23 | Spark DataFrame Basics Part Two | 09:56 |
| 24 | Spark DataFrame Basic Operations | 10:16 |
| 25 | Groupby and Aggregate Operations | 12:28 |
| 26 | Missing Data | 08:57 |
| 27 | Dates and Timestamps | 10:05 |
| 28 | DataFrame Project Exercise | 03:14 |
| 29 | DataFrame Project Exercise Solutions | 16:54 |
| 30 | Introduction to Machine Learning and ISLR | 10:22 |
| 31 | Machine Learning with Spark and Python with MLlib | 09:05 |
| 32 | Linear Regression Theory and Reading | 05:04 |
| 33 | Linear Regression Documentation Example | 14:20 |
| 34 | Regression Evaluation | 06:47 |
| 35 | Linear Regression Example Code Along | 15:14 |
| 36 | Linear Regression Consulting Project | 03:12 |
| 37 | Linear Regression Consulting Project Solutions | 15:33 |
| 38 | Logistic Regression Theory and Reading | 11:23 |
| 39 | Logistic Regression Example Code Along | 15:40 |
| 40 | Logistic Regression Code Along | 18:37 |
| 41 | Logistic Regression Consulting Project | 03:14 |
| 42 | Logistic Regression Consulting Project Solutions | 11:14 |
| 43 | Tree Methods Theory and Reading | 08:01 |
| 44 | Tree Methods Documentation Examples | 13:19 |
| 45 | Decision Tress and Random Forest Code Along Examples | 20:38 |
| 46 | Random Forest - Classification Consulting Project | 02:34 |
| 47 | Random Forest Classification Consulting Project Solutions | 08:01 |
| 48 | K-means Clustering Theory and Reading | 06:55 |
| 49 | KMeans Clustering Documentation Example | 09:52 |
| 50 | Clustering Example Code Along | 12:46 |
| 51 | Clustering Consulting Project | 03:10 |
| 52 | Clustering Consulting Project Solutions | 08:43 |
| 53 | Introduction to Recommender Systems | 06:33 |
| 54 | Recommender System - Code Along Project | 12:09 |
| 55 | Introduction to Natural Language Processing | 08:03 |
| 56 | NLP Tools Part One | 16:13 |
| 57 | NLP Tools Part Two | 08:06 |
| 58 | Natural Language Processing Code Along Project | 14:09 |
| 59 | Introduction to Streaming with Spark! | 10:20 |
| 60 | Spark Streaming Documentation Example | 11:48 |
| 61 | Spark Streaming Twitter Project - Part | 04:30 |
| 62 | Spark Streaming Twitter Project - Part Two | 13:09 |
| 63 | Spark Streaming Twitter Project - Part Three | 17:36 |
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