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Introduction to PySpark

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

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Course Description

In this course, you'll learn how to use Spark from Python! Spark is a tool for doing parallel computation with large datasets and it integrates well with Python. PySpark is the Python package that makes the magic happen. You'll use this package to work with data about flights from Portland and Seattle. You'll learn to wrangle this data and build a whole machine learning pipeline to predict whether or not flights will be delayed. Get ready to put some Spark in your Python code and dive into the world of high-performance machine learning!
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In the following Tracks

Big Data with PySpark

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Machine Learning Scientist in Python

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  1. 1

    Getting to know PySpark

    Free

    In this chapter, you'll learn how Spark manages data and how can you read and write tables from Python.

    Play Chapter Now
    What is Spark, anyway?
    50 xp
    Using Spark in Python
    50 xp
    Examining The SparkContext
    100 xp
    Using DataFrames
    50 xp
    Creating a SparkSession
    100 xp
    Viewing tables
    100 xp
    Are you query-ious?
    100 xp
    Pandafy a Spark DataFrame
    100 xp
    Put some Spark in your data
    100 xp
    Dropping the middle man
    100 xp
  2. 2

    Manipulating data

    In this chapter, you'll learn about the pyspark.sql module, which provides optimized data queries to your Spark session.

    Play Chapter Now
    Creating columns
    100 xp
    SQL in a nutshell
    50 xp
    SQL in a nutshell (2)
    50 xp
    Filtering Data
    100 xp
    Selecting
    100 xp
    Selecting II
    100 xp
    Aggregating
    100 xp
    Aggregating II
    100 xp
    Grouping and Aggregating I
    100 xp
    Grouping and Aggregating II
    100 xp
    Joining
    50 xp
    Joining II
    100 xp
  3. 3

    Getting started with machine learning pipelines

    PySpark has built-in, cutting-edge machine learning routines, along with utilities to create full machine learning pipelines. You'll learn about them in this chapter.

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    Machine Learning Pipelines
    50 xp
    Join the DataFrames
    100 xp
    Data types
    50 xp
    String to integer
    100 xp
    Create a new column
    100 xp
    Making a Boolean
    100 xp
    Strings and factors
    50 xp
    Carrier
    100 xp
    Destination
    100 xp
    Assemble a vector
    100 xp
    Create the pipeline
    100 xp
    Test vs. Train
    50 xp
    Transform the data
    100 xp
    Split the data
    100 xp
  4. 4

    Model tuning and selection

    In this last chapter, you'll apply what you've learned to create a model that predicts which flights will be delayed.

    Play Chapter Now
    What is logistic regression?
    50 xp
    Create the modeler
    100 xp
    Cross validation
    50 xp
    Create the evaluator
    100 xp
    Make a grid
    100 xp
    Make the validator
    100 xp
    Fit the model(s)
    100 xp
    Evaluating binary classifiers
    50 xp
    Evaluate the model
    100 xp
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Training 2 or more people?

Get your team access to the full DataCamp platform, including all the features.

In the following Tracks

Big Data with PySpark

Go To Track

Machine Learning Scientist in Python

Go To Track

datasets

AirportsFlightsPlanes

collaborators

Collaborator's avatar
Colin Ricardo

prerequisites

Introduction to Python
Lore Dirick HeadshotLore Dirick

Director of Data Science Education at Flatiron School

Lore is a data scientist with expertise in applied finance. She obtained her PhD in Business Economics and Statistics at KU Leuven, Belgium. During her PhD, she collaborated with several banks working on advanced methods for the analysis of credit risk data. Lore formerly worked as a Data Science Curriculum Lead at DataCamp, and is and is now Director of Data Science Education at Flatiron School, a coding school with branches in 8 cities and online programs.
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Nick Solomon HeadshotNick Solomon

Data Scientist

Nick has a degree in mathematics with a concentration in statistics from Reed College. He's worked on many data science projects in the past, doing everything from mapping crime data to developing new kinds of models for social networks. He's currently a data scientist in the New York City area.
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