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.

Cleaning the data is important for data scientists, data analysts, and business intelligence professionals, as well as for anyone who needs to work with large, complex datasets to uncover meaningful insights and make data-driven decisions.

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.

By completing this project, you will be equipped with the skills necessary to effectively standardize your data and ensure its quality for analysis.

A Look at the Project Ahead

After completing this project, you'll be able to:

Handle missing data values
Correct data format
Standardize and normalize data

What You'll Need

For this project, you will need:

Familiarity with Python
Familiarity with principles of data science
A basic understanding of data standardization
A web browser

Your online lab environment has everything you need to get started. Also, note that this platform works best with current versions of modern browsers.

Data Wrangling using Python

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