Cleaning and Exploring Big Data using PySpark
3818 ya inscrito
3818 ya inscrito
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
En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:
Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.
En un video de pantalla dividida, tu instructor te guía paso a paso
por NN22 de abr. de 2022
use case could be explained a little better, before actually going to the code
por SR14 de dic. de 2020
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.
por JA23 de mar. de 2022
fast and simple explanation about ow to start to work with Spak on Colab
por AA21 de ago. de 2021
Practical walk through of basic PySpark operations. Great quick-start to using Pyspark for data analysis