This Spark course aims to equip participants with the skills to analyze and process big data using Spark and PySpark, covering SQL data processing, machine learning with MLlib, and natural language processing (NLP).

Students will learn to build recommendation systems, work with streaming data, perform graph analysis, and deploy PySpark applications on the cloud.

Through practical exercises and projects, the course emphasizes hands-on experience in big data analytics, from basic data manipulation to advanced machine learning and cloud deployment.

1. Understanding Spark and PySpark Architecture: Overview of Spark and PySpark, including RDD and DataFrame operations.
2. SQL Spark and Data Processing: SQL querying in Spark, handling various data formats, and using PySpark API for data interaction.
3. Introduction to MLlib with Supervised Algorithms: Machine learning in PySpark using MLlib for algorithms like linear and logistic regression.
4. Practical Work 1: Unsupervised Algorithms with MLlib: Creating pipelines for k-means and PCA using advanced MLlib algorithms.
5. NLP with PySpark: Text classification using NLP techniques like Tokenization, Stemming, and Lemmatization with the NLTK package.
6. Recommendation System with PySpark: Understanding and implementing content-based and collaborative filtering recommendation engines.
7. PySpark Streaming: Working with Spark Streaming for building scalable streaming applications.
8. GraphX and GraphFrames: Learning Spark GraphX programming, operations, and comparing GraphX with GraphFrames.
9. Practical Work: Deploy PySpark Application on Cloud: Deploying a PySpark application on a cloud platform, including AWS cluster setup.