Understanding Your Data | Day 19 | 100 Days of Machine Learning
Channel: CampusX
Date: 2021-04-03

Quality data is fundamental to any data science engagement. To gain actionable insights, the appropriate data must be sourced and cleansed. Understanding Your Data is the foundational step in any data analysis, involving exploring data characteristics, patterns, and relationships to gain insights.

It is important at the beginning of a project to consider potential harms from your tool. These harms can be caused by designing for only a narrow group of users, having insufficient representation of sub-populations, or human labelers favoring a privileged group.

Machine learning discovers and generalizes patterns in the data and could, therefore, replicate bias. If a group is under-represented, the machine learning model has fewer examples to learn from, resulting in reduced accuracy for those individuals in this group.

When implementing these models at scale, it can result in a large number of biased decisions, harming a large number of people. Ensure you have evaluated risks and have techniques in place to mitigate them.

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⌚Time Stamps⌚

00:00 - Intro
00:27 - Understanding your data
00:53 - Asking Basic Questions to your Data
03:00 - How big is the data?
03:35 - How does the data looks like?
05:02 - What is the data type of columns?
06:50 - Are there any missing values?
08:39 - How does the data looks mathematically?
10:25 - Are there any duplicate values?
11:27 - How is the Corelation between the columns?