62 ratings
4,711 already enrolled
Learn how to clean your big dataset in PySpark
Learn how to explore big dataset in PySpark
Learn how to create visualizations from big dataset loaded in PySpark
By the end of this project, you will learn how to clean, explore and visualize big data using PySpark. You will be using an open source dataset containing information on all the water wells in Tanzania. I will teach you various ways to clean and explore your big data in PySpark such as changing column’s data type, renaming categories with low frequency in character columns and imputing missing values in numerical columns. I will also teach you ways to visualize your data by intelligently converting Spark dataframe to Pandas dataframe. Cleaning and exploring big data in PySpark is quite different from Python due to the distributed nature of Spark dataframes. This guided project will dive deep into various ways to clean and explore your data loaded in PySpark. Data preprocessing in big data analysis is a crucial step and one should learn about it before building any big data machine learning model. Note: You should have a Gmail account which you will use to sign into Google Colab. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Cleaning
Python Programming
Data Visualization (DataViz)
Apache Spark
Exploratory Data Analysis
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Install Spark on Google Colab and load datasets in PySpark
Change column datatype, remove whitespaces and drop duplicates
Remove columns with Null values higher than a threshold
Group, aggregate and create pivot tables
Rename categories and impute missing numeric values
Create visualizations to gather insights
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by JA
Mar 23, 2022fast and simple explanation about ow to start to work with Spak on Colab
by NN
Apr 22, 2022use case could be explained a little better, before actually going to the code
by AA
Aug 21, 2021Practical walk through of basic PySpark operations. Great quick-start to using Pyspark for data analysis
by SR
Dec 14, 2020More theory behind the functions used and concepts behind spark and how it works in a distributed way would've been more benefitting. Overall it was a worthy course.
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
Because your workspace contains a cloud desktop that is sized for a laptop or desktop computer, Guided Projects are not available on your mobile device.
Guided Project instructors are subject matter experts who have experience in the skill, tool or domain of their project and are passionate about sharing their knowledge to impact millions of learners around the world.
You can download and keep any of your created files from the Guided Project. To do so, you can use the “File Browser” feature while you are accessing your cloud desktop.
Guided Projects are not eligible for refunds. See our full refund policy.
Financial aid is not available for Guided Projects.
Auditing is not available for Guided Projects.
At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
More questions? Visit the Learner Help Center.
