The MET CS 777 Big Data Analytics Code Repository is a collection of code examples and notebooks designed to aid students in learning big data analytics. The repository contains several directories, each with its own focus.

Notebooks: This directory contains Jupyter notebooks that illustrate the basic usage of Spark and examples presented during lectures. The notebooks can be used as a guide to learn how to use Spark and its different components.

Spark-RDD-and-DataFrame-Examples: This directory contains several examples of data analytics on well-known datasets using Spark RDDs and DataFrames. The following datasets are included in this directory:

• FlightsData dataset: This dataset contains flight details such as the departure and arrival time, flight duration, and flight distance. The examples in this directory show how to analyze this data using Spark RDDs and DataFrames.

• SocialMedia dataset: This dataset contains social media data, such as Facebook posts and tweets. The examples in this directory show how to analyze this data using Spark RDDs and DataFrames.

• TPCH dataset: This is a benchmark dataset that is used to test the performance of database systems. The examples in this directory show how to analyze this data using Spark RDDs and DataFrames.

• Word-Count examples: This is a classic example used to demonstrate the capabilities of Spark. The examples in this directory show how to count the number of occurrences of words in a given text file using Spark RDDs and DataFrames.

Python: This directory contains machine learning algorithms implemented in Python. Students can use these algorithms as a starting point to learn about different machine learning models and techniques.

Installations-HowTos: This directory contains installation how-tos for various OS and Jupyter notebooks, demonstrating how to use Spark on Google Colab.

The MET CS 777 Big Data Analytics Code Repository is a valuable resource for anyone interested in learning about big data analytics using Spark and Python.