EDA and EDA Life Cycle

• What Is Exploratory Data analysis?

EDA, or exploratory data analysis, is like detective work for your data! It's about sniffing around, digging deep, and uncovering hidden clues. Imagine having a treasure chest full of information, but it's messy and confusing. EDA is like organizing, looking at patterns, and figuring out what the heck all that stuff means. You might see pictures, numbers, and weird connections and suddenly understand what kind of story your data is telling. Basically, EDA helps you make sense of your data before diving into fancy models or predictions. It's like building a strong foundation before constructing a house!

• Why is exploratory data analysis important?Imagine trying to cook a delicious meal without tasting or checking the ingredients first. That's kind of like analyzing data without EDA! Here's why it's super important:

1. Cleanliness:?EDA helps find dirty data like typos,?missing values,?or even weird stuff that doesn't belong.?No one wants a crunchy rock in their soup!
2. Understanding: You get to know your data's strengths and weaknesses,?like its shape,?patterns,?and how different parts connect.?It's like knowing your fridge before shopping—you get the right ingredients!
3. Smart Choices:?Based on what you find,?you can choose the best tools and methods for analyzing your data.?No more using a whisk for chopping carrots!
4. Fewer Mistakes: By spotting problems early,?you avoid basing your conclusions on wrong or missing information.?No burned dinners because of bad ingredients!
5. Hidden Gems:?Sometimes,?EDA reveals surprising patterns and connections you wouldn't see otherwise.?It's like finding a secret spice that makes your dish amazing!

In short, EDA makes your data analysis cleaner, faster, and more reliable. It's like having a helpful kitchen assistant who sets you up for success—delicious results guaranteed!

The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors as well as better understand patterns within the data, detect outliers or anomalous events, and find interesting relations among the variables. EDA is the process of examining the available dataset to understand its structure, patterns, and characteristics. EDA helps identify and address data quality issues such as missing values and outliers. Cleaning and preprocessing the data during the exploratory phase improves the quality and reliability of subsequent analyses. The visual representations generated during EDA, such as charts and graphs, make it easier to communicate complex patterns and insights to stakeholders who may not have a technical background. Visualization enhances the interpretability of results.

• EDA Life Cycle

Having grasped the significance of Exploratory Data Analysis (EDA), let's delve into the procedural steps that guide this insightful process.

1. Problem Definition: If you are trying to extract useful insight from the data, First, you need to define your business problem to be solved. The problem definition works as the driving force for your exploratory data analysis. The main tasks involved in problem definition are defining the main objective of the analysis,defining the main deliverables, outlining the main roles and responsibilities, and obtaining the current status of the data. Based on this point, you can define your problem.
2. Data Preparation: This step involves methods for preparing the dataset before actual analysis. In this step, you can define your source of data, like Extracting the data from your database or online web scraping define your data schemas,understand the main characteristics of the data,clean the dataset,delete non-relevant datasets, and transform the data into meaningful datasets.
3. Data Analysis: This is one of the most crucial steps that deals with descriptive statistics and analysis of the data. The main tasks involve summarizing the data, finding the hidden correlations and relationships among the data, developing predictive models, evaluating the models, and calculating their accuracy. You can use some of the techniques, like data summarization, are summary tables, graphs,descriptive statistics, inferential statistics, correlation statistics, searching, grouping, and mathematical models.
4. Development and Results: This step involves presenting the dataset to the target audience in the form of graphs, summary tables, maps,and diagrams. This is also an essential step, as the results analyzed from the dataset should be interpretable by the business stakeholders, which is one of the major goals of EDA.

Thank you !

Blog by :- Thomas Patole .