Cleaning and Exploring Big Data using PySpark

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在此指导项目中,您将:

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

Clock2 hours
Intermediate中级
Cloud无需下载
Video分屏视频
Comment Dots英语(English)
Laptop仅限桌面

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.

您要培养的技能

  • Cleaning
  • Python Programming
  • Data Visualization (DataViz)
  • Apache Spark
  • Exploratory Data Analysis

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Install Spark on Google Colab and load datasets in PySpark

  2. Change column datatype, remove whitespaces and drop duplicates

  3. Remove columns with Null values higher than a threshold

  4. Group, aggregate and create pivot tables

  5. Rename categories and impute missing numeric values

  6. Create visualizations to gather insights

指导项目工作原理

您的工作空间就是浏览器中的云桌面,无需下载

在分屏视频中,您的授课教师会为您提供分步指导

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