Intro to EDA

Exploratory Data Analysis (EDA) is like a detective's investigation when it comes to understanding a dataset. Just like how a detective looks for clues to solve a mystery, EDA helps data scientists explore and understand their data to uncover valuable insights and patterns. This detective work helps us understand the story behind the data, find any irregularities or outliers, and decide how to best approach analyzing the data for valuable insights.

What is EDA?

Back to our detective analogy, EDA carefully examine the data to understand its story by summarizing the data using descriptive statistics, such as averages, ranges, and distributions, which give us a general overview. Then, we move on to visualizing the data using graphs and charts, which make it easier to spot trends, relationships, or anomalies. To understand this further, let's look at some examples...

Example 1

Imagine we have a dataset containing information about house prices. Through EDA, we can calculate the average price, explore the distribution of prices, and visualize the relationships between price and factors like the number of bedrooms or location. By doing this, we might discover that houses with more bedrooms tend to have higher prices, or that houses in certain neighborhoods are more expensive than others.

Example 2

Imagine you're given a large dataset, like a collection of puzzle pieces. EDA helps you make sense of these pieces and understand what story they're trying to tell. We can start by examining the individual pieces, such as looking at the values, checking for missing or unusual data, and understanding what each variable represents. This is like inspecting each puzzle piece to see its color, shape, or pattern.

Next, we start putting the puzzle pieces together and look for connections. You analyze how variables relate to each other, finding correlations, trends, and patterns. This is similar to connecting puzzle pieces based on their edges or colors to create meaningful parts of the picture.

Example 3

Imagine you have a dataset that contains information about literacy rates in different African countries.

First, EDA can help us detect any outliers or inconsistencies in the data, such as countries with unusually high or low literacy rates compared to others. Next, we can create visualizations like bar charts or maps to show the literacy rates of different countries by identifying which countries have high literacy rates and vice versa.

This analysis helps us to compare and understand the variations in literacy across different African countries.

👩🏾‍🎨 Practice: Understand the EDA 🎯

➡️ Next, you'll be introduced to fundamentals of statistics 🎯.