Understanding Your Data: The Essentials of Exploratory Data Analysis

Exploratory Data Analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making.

Exploratory data analysis commonly known as (EDA) is the approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. We can therefore say that EDA supports the main function of data analysis.

Look at it as a professional athlete running track. Before they take to competition they must first ensure their spikes are well functioning ,scope out the track and partake in warmups. Simply, EDA is the warmup session to the Data Analysis' running.

Importance of EDA
The main purpose of EDA is to help look at data before making any assumptions. It helps identify obvious errors, as well as better understand patterns within the data, detect outliers and find interesting relations among variables. EDA also helps stakeholders by confirming they are asking the right questions

Essentials of EDA
EDA involves a range of activities, including data integration, analysis, cleaning, transformation, and dimension reduction. In this article we will highlight some key steps in EDA.

1. Data Cleaning
   Begin by checking for missing values, duplicates, and inconsistent data types. Clean the data to ensure it's ready for analysis. we usually import necessary python libraries such as pandas, NumPy and matplotlib.
   import pandas as pd
import numpy as np

2. Descriptive Statistics
   Here we will calculate basic statistics like mean, median, mode, standard deviation, and variance to get a sense of the data distribution. This is usually supported by the imported libraries that enable the aforementioned mathematical functions.

3. Data Visualization
   Once we have calculated the mean, median ,std etc. of the data we will use visual tools like histograms, box plots, and scatter plots to visualize data distributions, relationships, and patterns. These visuals reveal trends over time in a way which cannot be seen from raw data. This can be broken down to;

• Univariate Analysis which is the use of histograms, box plots, and density plots to examine the distribution of individual variables.

• Bivariate Analysis which is the creation of scatter plots, pair plots, and bar plots to explore relationships between two variables and;

• Multivariate Analysis which employs heatmaps, correlation matrices, and pair plots to investigate interactions among multiple variables.

1. Correlation Analysis
   In correlation analysis we compute correlation matrices and heatmaps to explore relationships between variables. This helps identify which variables are related to inform further modelling.

Handling Outliers
Detecting and analyzing outliers using methods like the Z-score or Interquartile Range (IQR) is usually done where decisions are made whether to keep ,transform, or remove outliers based on their impact on the analysis.

1. Understanding Distribution where we analyze the shape of data distributions to determine if they are skewed or normally distributed. This informs decisions about data transformation and the suitability of statistical tests

2. Dimension Reduction or Addition where we can either reduce or impute the number of variables while preserving essential information. EDA can inform this through the use of statistics methods in filling our data. This needs domain knowledge as it should and will add value to the data.

Conclusions
In conclusion, EDA is crucial for understanding datasets, identifying patterns, and informing subsequent analysis.