Spark and Python for Big Data with PySpark

Course Name : Spark and Python for Big Data with PySpark
Students : 139,165
Duration : 10.5 hrs & 4 downloadable resources
Avg Rating : 4.5 (24,833 ratings)
Original Price : $119.99
Discount Price : $15
Instructor / Provider : Jose Portilla, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

What You’ll Learn

• Analyze Big Data with Python and Spark: Harness the power of Python and Apache Spark for effective big data analysis.
• Master Spark 2.0 DataFrame Syntax: Learn the latest techniques with Spark’s modern DataFrame syntax.
• Real-World Consulting Projects: Work on practical projects that simulate real-world scenarios, sharpening your problem-solving skills.
• Machine Learning with Spark:
  • Classify customer churn using Logistic Regression.
  • Build classification models with Random Forests and Gradient Boosted Trees.
  • Leverage Spark’s MLlib to create advanced machine learning models.
• Explore DataBricks: Learn to use the DataBricks platform for streamlined big data workflows.
• Cloud Computing with AWS:
  • Set up Spark environments on Amazon EC2 for big data analysis.
  • Use AWS Elastic MapReduce (EMR) for scalable data processing.
• Linux for Big Data: Gain essential Linux skills to optimize Spark environments.
• Natural Language Processing (NLP): Create a spam filter with Spark and NLP techniques.
• Real-Time Data Analysis: Use Spark Streaming to analyze tweets in real-time.

Course Overview

1. Crash Course in Python:
   • Start with a quick refresher on Python basics, setting the stage for Spark integration.
2. Spark DataFrames:
   • Master the latest Spark 2.0 DataFrame syntax and its applications.
3. Machine Learning with Spark MLlib:
   • Build powerful machine learning models using Spark’s built-in MLlib library with DataFrames.
4. Hands-On Projects:
   • Apply your knowledge with exercises and mock consulting projects designed to mimic real-world scenarios.
5. Big Data on the Cloud:
   • Set up and run Spark environments on AWS EC2 and EMR for large-scale data processing.
6. Advanced Applications:
   • Build a spam filter using Spark and Natural Language Processing.
   • Analyze social media data in real-time with Spark Streaming.

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