Deep Dive in Machine Learning with Python

Part — XIII: Converse with the data

Welcome to another blog of Deep Dive in Machine Learning with Python, up to this point we have extensively worked to learn the concepts of python programming, data wrangling, and data visualization. And, in the last blog, we gained an understanding of Heatmaps and Pair Plots.

In today’s blog, we will explore the depths of various levels of data analysis. And, learn how to enable the raw data to discover the hidden patterns.

As a Data Scientist or ML Engineer, it is your primary task to listen to the data in many possible ways to manifest a credible story of the data.

Every stage of the ML problem requires a distinct type of Data Analysis to be performed.

As the above image represents, we have 4 levels of data analysis and each has its significance in a ML task.

IDA is the first activity that we perform while working on a Data Science problem and it solves below issues:

After performing IDA, we investigate in the dataset with a motive to discover patterns and this is known as Exploratory Data Analysis.

Congratulations, we come to the end of this blog. To summarize, we covered the different levels of data analysis. In the next blog, I’ll demonstrate each level of data analysis on real-life datasets.

Thank you and happy learning!!

Blog-14: Initial Data Analysis (IDA) with example

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