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 diabetes prediction data into a PySpark DataFrame

To commence, create a `SparkSession` as previously learned. Utilize it to load the data into a PySpark DataFrame and display the initial rows.


# Import libraries
from pyspark.sql import SparkSession
# Create a SparkSession
spark = 

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

# Write code to display the initial rows of the dataframe


# Write code to check the column types of diabetes_df 


- To create a `SparkSession`, we'll need to use the `builder` and `getOrCreate()` attributes of the `SparkSession`.
- We can use the `read.csv()` function to read the CSV file into the 'diabetes_df' DataFrame.
- Do you remember what method is used to show the first five rows of the DataFrame?
- To examine the column data types, use the `dtypes` attribute of the DataFrame.

# Task 2: Data preprocessing and EDA

In the data preprocessing task, we’ll apply essential data preparation techniques to ensure the dataset is in a suitable format for the model training. 

## 2.1 Convert string variables into integer representations
Convert all the columns that contain string variables (for example, `age`) into integers so that we can use them in modeling. 

## 2.2 Relationship between diabetes and blood glucose level

Group the data based on the diabetes column and calculate the average blood glucose level for each group. 

## 2.3 Biological sex distribution

Calculate the count or percentage of males and females in the diabetic and non-diabetic groups separately to identify any disparities or patterns related to biological sex and diabetes.


Learn how to build a predictive model to detect diabetes using PySpark MLlib.

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: Predicting Diabetes Using PySpark MLlib

Task 1: Load the diabetes prediction data into a PySpark DataFrame

Task 2: Data preprocessing and EDA