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CRISP-DM and Data Wrangling

CRISP-DM and data wrangling

Once the data has been loaded and extracted, it then needs to be cleaned, transformed, and rearranged. This process is known as data wrangling. Let’s watch a video to learn more about data wrangling.


Do you remember learning about the CRISP-DM process earlier in Course 1?

In the previous course, we unpacked data ingestion as a part of the data understanding step of the CRISP-DM process. This week and next, we will have similar practical and hands-on tasks for the next step in the process of data preparation that includes data wrangling and transformation.

Sometimes, the way data is stored in the data sources (files, databases) is not in the format you need for a data processing application, and therefore substantial time is spent on data preparation.

Pandas, along with the various libraries and modules of Python, provide a flexible, high-level, and high-performing set of core manipulations and algorithms that enable you to perform the data wrangling into the required form.

This week, we will spend a lot of time on building the foundations of data wrangling activities that can be performed in Python by way of examples and programming.

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Data Wrangling and Ingestion using Python

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