Spark and Python for Big Data with PySpark
4.0 /5- Offered byUDEMY
Spark and Python for Big Data with PySpark at UDEMY Overview
Duration | 11 hours |
Total fee | ₹599 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Spark and Python for Big Data with PySpark at UDEMY Highlights
- Compatible on Mobile and TV
- Earn a Cerificate on successful completion
- Get Full Lifetime Access
- Course Instructor
- Jose Portilla
Spark and Python for Big Data with PySpark at UDEMY Course details
- 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
- 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!
- 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!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! This course also has a full 30 day money back guarantee and comes with a LinkedIn Certificate of Completion! If you're ready to jump into the world of Python, Spark, and Big Data, this is the course for you!
Spark and Python for Big Data with PySpark at UDEMY Curriculum
Introduction to Course
Introduction
Course Overview
Frequently Asked Questions
What is Spark? Why Python?
Setting up Python with Spark
Set-up Overview
Note on Installation Sections
Local VirtualBox Set-up
Local Installation VirtualBox Part 1
Local Installation VirtualBox Part 2
Setting up PySpark
AWS EC2 PySpark Set-up
AWS EC2 Set-up Guide
Creating the EC2 Instance
SSH with Mac or Linux
Installations on EC2
Databricks Setup
Databricks Setup
AWS EMR Cluster Setup
AWS EMR Setup
Python Crash Course
Introduction to Python Crash Course
Jupyter Notebook Overview
Python Crash Course Part One
Python Crash Course Part Two
Python Crash Course Part Three
Python Crash Course Exercises
Python Crash Course Exercise Solutions
Spark DataFrame Basics
Introduction to Spark DataFrames
Spark DataFrame Basics
Spark DataFrame Basics Part Two
Spark DataFrame Basic Operations
Groupby and Aggregate Operations
Missing Data
Dates and Timestamps
Spark DataFrame Project Exercise
DataFrame Project Exercise
DataFrame Project Exercise Solutions
Introduction to Machine Learning with MLlib
Introduction to Machine Learning and ISLR
Machine Learning with Spark and Python with MLlib
Linear Regression
Linear Regression Theory and Reading
Linear Regression Documentation Example
Regression Evaluation
Linear Regression Example Code Along
Linear Regression Consulting Project
Linear Regression Consulting Project Solutions
Logistic Regression
Logistic Regression Theory and Reading
Logistic Regression Example Code Along
Logistic Regression Code Along
Logistic Regression Consulting Project
Logistic Regression Consulting Project Solutions
Decision Trees and Random Forests
Tree Methods Theory and Reading
Tree Methods Documentation Examples
Decision Tress and Random Forest Code Along Examples
Random Forest - Classification Consulting Project
Random Forest Classification Consulting Project Solutions
K-means Clustering
K-means Clustering Theory and Reading
KMeans Clustering Documentation Example
Clustering Example Code Along
Clustering Consulting Project
Clustering Consulting Project Solutions
Collaborative Filtering for Recommender Systems
Introduction to Recommender Systems
Recommender System - Code Along Project
Natural Language Processing
Introduction to Natural Language Processing
NLP Tools Part One
NLP Tools Part Two
Natural Language Processing Code Along Project
Spark Streaming with Python
Introduction to Streaming with Spark!
Spark Streaming Documentation Example
Spark Streaming Twitter Project - Part
Spark Streaming Twitter Project - Part Two
Spark Streaming Twitter Project - Part Three
Bonus
Bonus Lecture:
Other courses offered by UDEMY
Spark and Python for Big Data with PySpark at UDEMY Students Ratings & Reviews
3 Ratings- 3-43
Student Forum
Anything you would want to ask experts?