Data Wrangling using Python
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Learn how to wrangle and clean data using Python in this guided project. Discover techniques for handling missing values, correcting data formats, and standardizing data. Prepare your data for analysis with binning and creating indicator variables. Start your data wrangling journey now.
At a Glance
Data wrangling is essential for ensuring your data is usable for analysis and modeling. In this guided project, you will learn how to wrangle data using Python.
Data wrangling, also known as data cleaning, is a crucial step in the data science process where raw data is cleaned, transformed, and prepared for analysis and modeling. You will explore how data is wrangled, including how to identify and handle missing values, correct data formats, and standardize data.
In this guided project, you will learn about data wrangling and how to apply various techniques to clean and prepare your data for analysis. You will start by identifying and handling missing values, correcting data format, and cleaning and normalizing the data. Additionally, you will learn about binning and creating indicator variables to further prepare your data for analysis.
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