An Extensive Step By Step Guide for Data Preparation

A go-to resource for preparing your data for data science. Before we get into this, I want to make it clear that there is no rigid process when it comes to data preparation.

Introduction

Before we get into this, I want to make it clear that there is no rigid process when it comes to data preparation. How you prepare one set of data will most likely be different from how you prepare another set of data. Therefore this guide aims to provide an overarching guide that you can refer to when preparing any particular set of data.

Before we get into the guide, I should probably go over what Data Preparation is…

What is Data Preparation?

Data preparation is the step after data collection in the machine learning life cycle and it’s the process of cleaning and transforming the raw data you collected. By doing so, you’ll have a much easier time when it comes to analyzing and modeling your data.

There are three main parts to data preparation that I’ll go over in this article:

1. Exploratory Data Analysis (EDA)
2. Data preprocessing
3. Data splitting

1. Exploratory Data Analysis (EDA)

Exploratory data analysis, or EDA for short, is exactly what it sounds like, exploring your data. In this step, you’re simply getting an understanding of the data that you’re working with. In the real world, datasets are not as clean or intuitive as Kaggle datasets.

The more you explore and understand the data you’re working with, the easier it’ll be when it comes to data preprocessing.

Below is a list of things that you should consider in this step:

Feature and Target Variables

Determine what the feature (input) variables are and what the target variable is. Don’t worry about determining what the final input variables are, but make sure you can identify both types of variables.

artificial-intelligence technology data-science education data analysis

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Data science is omnipresent to advanced statistical and machine learning methods. For whatever length of time that there is data to analyse, the need to investigate is obvious.

Business Intelligence and Data Science terms become very popular these days: It is undeniable that information is the foundation of any successful company and business entrepreneurs.

The dataset also includes information on time and distance of flights which might also have an effect on delays. These columns can be analyzed with similar methods.

This is because AI and analytics tools are very picky: The data has to be in just the right format, and anything unexpected throws a wrench into the system.