Python for Data Analysis: Preparing Numeric Data

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United States DataDaft
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Published on ● Video Link: https://www.youtube.com/watch?v=Y1mXf0rXm04


Duration: 23:44
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This video examines a variety of techniques for preprocessing and preparing numeric data for analysis in Python. It covers centering and scaling data, dealing with skewed data, identifying and dealing with highly correlated features and imputing missing data with the sklearn package.

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This is lesson 16 of a 30-part introduction to the Python programming language for data analysis and predictive modeling. Link to the code notebook below:

Python for Data Analysis: Preparing Numeric Data
https://www.kaggle.com/hamelg/python-for-data-16-preparing-numeric-data

This guide does not assume any prior exposure to Python, programming or data science. It is intended for beginners with an interest in data science and those who might know other programming languages and would like to learn Python.

I will create the videos for this guide such that you should be able to learn a lot just watching on YouTube, but to get the most out of the guide, it is recommended that you create a Kaggle account so that you can copy and edit each lesson so that you can follow along and run code yourself.

Introduction to Python Playlist:
https://www.youtube.com/playlist?list=PLiC1doDIe9rCYWmH9wIEYEXXaJ4KAi3jc

Link to the Python for Data Analysis written guide index page:
https://www.kaggle.com/hamelg/python-for-data-analysis-index .


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Tags:
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