Gain insights into PySpark within big data. Learn about data ingestion, distributed computing, data processing, and performance optimization to solve real-world problems and apply machine learning.

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This course explores the big data ecosystem, focusing on hands-on utilization of PySpark—the Python API for Apache Spark. 

In this course, you’ll experience a balanced blend of theory and practice. You’ll learn about data ingestion, storage, distributed computing, PySpark’s intricacies, data processing, data analysis, performance optimization, tool integration, and practical applications like machine learning. 

This course, suited for beginners to intermediate learners, will give you an understanding of big data tools and techniques. After completing this course, you’ll be fully equipped with effective problem-solving capabilities in real-world scenarios.

Mastering Big Data with PySpark

# Task 1: Load the Telco churn data into a PySpark DataFrame

To begin our analysis, we’ll load the Telco churn data into a PySpark DataFrame. This task consists of two subtasks:

## 1.1 Load the customer data into a PySpark DataFrame

We’ll first load the customer data from a suitable data source into a PySpark DataFrame using PySpark's built-in capabilities.

## 1.2 Ensure the data is properly formatted and structured for analysis

Once we have loaded the data, it’s essential to ensure that the data is properly formatted and structured for analysis by printing the first five rows and the schema of the DataFrame.



# Import the necessary libraries
from pyspark.sql import SparkSession
# Create a SparkSession object
spark = SparkSession.builder.getOrCreate()

## Write code to read "churn.csv" that is available in your path in a variable called 'spark_df'. Make sure you include options 'header=T' and 'inferSchema=True'
telco_df = 

## Write code to look at the first five rows of the "churn_df" DataFrame


## Write code to check the schema of the "churn_df" DataFrame



from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('churn').getOrCreate()

## Write code to read "churn.csv" that is available in your path in a variable called 'spark_df'. Make sure you include options 'header=T' and 'inferSchema=True'
telco_df = spark.read.csv("churn.csv", header=True, inferSchema=True)

## Write code to look at the first 5 rows of the "churn_df" DataFrame
telco_df.show(5)

## Write code to check the schema of the "churn_df" DataFrame
telco_df.printSchema()

- To load the data into a PySpark DataFrame, we can use the read.csv method and provide the appropriate file path. Make sure to include `header=True` and `inferSchema=True` as parameters to correctly interpret the header row and infer the data types.
- What PySpark `DataFrame` action method is used to print the `n` number of rows of a DataFrame?
- What method can be used to show the schema of the DataFrame?

# Task 2: Data preprocessing and transformation

In this task, we’ll preprocess and transform the data to gain insights into potential predictors of customer churn. These operations will help prepare the data for further analysis or modeling. By preprocessing and transforming the data, we’ll gain insights into potential predictors of churn and prepare the data for further analysis and modeling.

## 2.1 Filter churned customers and count the number of customers

Filter the data to select only the customers who have churned and count the number of such customers. By doing so, we can focus specifically on this group and study their characteristics.

## 2.2 Group data by sex and compute average monthly charges

Group the data by sex and calculate the average monthly charges for each sex. This analysis will help us determine if there are any sex-based differences in charges and their potential impact on churn.

## 2.3 Compute total charges for each customer

Calculate the total charges for each customer by multiplying the monthly charges by the tenure and add a new column, "Total Charges," to the DataFrame. This information will provide insights into the revenue generated from each customer and its relationship with churn.

## 2.4 Compute the correlation between monthly charges and total charges

Compute the correlation between monthly charges and total charges to determine if there is a strong relationship and how it may impact customer churn.



Test your knowledge of customer churn analysis and predictions using PySpark.

Introduction to the Course

Introduction to Big Data

Exploring PySpark Core and RDDs

PySpark DataFrames and SQL

Customer Churn Analysis Using PySpark

Machine Learning with PySpark

Modeling with PySpark MLlib

Predicting Diabetes in Patients Using PySpark MLlib

Performance Optimization in PySpark

PySpark Optimization: Analyzing NYC Restaurants Data

Integrating PySpark with Other Big Data Tools

Wrap Up

Apriori Algorithm for Finding Frequent Itemsets with PySpark

Quiz: Customer Churn Analysis Using PySpark

Task 1: Load the Telco churn data into a PySpark DataFrame

1.1 Load the customer data into a PySpark DataFrame

1.2 Ensure the data is properly formatted and