Cleaning and Exploring Big Data using PySpark
4,089명이 이미 등록했습니다.
4,089명이 이미 등록했습니다.
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.
Data Visualization (DataViz)
Exploratory Data Analysis
작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.
작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.
분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.
NN 제공2022년 4월 22일
use case could be explained a little better, before actually going to the code
AA 제공2021년 8월 21일
Practical walk through of basic PySpark operations. Great quick-start to using Pyspark for data analysis
SR 제공2020년 12월 14일
More 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.
JA 제공2022년 3월 23일
fast and simple explanation about ow to start to work with Spak on Colab