Elegant way to make data talk stories: Exploratory data analysis

Elegant way to make data talk stories: Exploratory data analysis

Elegant way to make data talk stories: Exploratory data analysis. Data can tell great stories and making it to convey the right story is an art. The means to acquire this art is Exploratory data analysis (EDA).

Introduction

Data can tell great stories and making it to convey the right story is an art. The means to acquire this art is Exploratory data analysis (EDA). Exploratory data analysis is nothing but using the statistical and probability approaches to understand what the data is trying to convey to us.

As a data scientist, a major share of the work will mostly be focused on understanding the data and trying to get only the necessary characteristics to be sent to the Machine Learning model. Only when the input data makes sense, the model will be able to leverage its maximum power.

One of the really tough things is figuring out what questions to ask. Once you figure out the question, then the answer is relatively easy — Elon Musk

It’s often a challenging task to find the right question from a clean slate. But, by constantly asking _Why, _we’ll be able to understand the behaviour of the data and derive the insights

Now, we can dive into some common starting points that can be used while performing EDA. Having always been a fan of Pokemon right from my childhood, I will be using the _Pokemon 🌟 dataset _from Kaggle for step by step process to go ahead with EDA

Come let’s catch ’em all :)

Super tool: Pandas

Before describing the general steps to perform the EDA, let’s take a look at an important tool.

Pandas *library is a fast, powerful and easy tool which was built on the top of *python. From my personal experience, as a data scientist, my everyday bread and butter solely rely on pandas. All the programming logic can be easily implemented with just one or two lines of code, which makes this library so popular. It can handle thousands of data without many computational requirements. Moreover, the functionalities provided by this library is simple and are quite effective.

Even for our Pokemon dataset EDA, we will be using pandas for understanding the data and also for visualisation

artificial-intelligence data-science exploratory-data-analysis machine-learning data

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