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Exploratory Data Analysis: A Guide with Examples

Mlearning.ai

Before conducting any formal statistical analysis, it’s important to conduct exploratory data analysis (EDA) to better understand the data and identify any patterns or relationships. EDA is an approach that involves using graphical and numerical methods to summarize and visualize the data. Thank you for reading!

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Roadmap to Learn Data Science for Beginners and Freshers in 2023

Becoming Human

In Inferential Statistics, you can learn P-Value , T-Value , Hypothesis Testing , and A/B Testing , which will help you to understand your data in the form of mathematics. For Data Analysis you can focus on such topics as Feature Engineering , Data Wrangling , and EDA which is also known as Exploratory Data Analysis.

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Building ML Platform in Retail and eCommerce

The MLOps Blog

The objective of an ML Platform is to automate repetitive tasks and streamline the processes starting from data preparation to model deployment and monitoring. So, we need to build a verification layer that runs based on a set of rules to verify and validate data before preparing it for model training.

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