Understanding Your Data: The Essentials of Exploratory Data Analysis

What is Exploratory Data Analysis

Exploratory Data Analysis, also orefered to as EDA, is a process of analysing data through different steps, methods and using different analysis tools and visuals, to better understand and summarise the data's main characteristics, identify patterns, spot anomalies, test a hypothesis, or check assumptions. It helps summarise the data and discover insights before applying more advanced analysis techniques.

The Importance of Exploratory Data Analysis in Data Science

EDA is a crucial step before you run your data through any algorithm. It helps you detrmine the important variables and those with insignificant impact to the output.

EDA helps data scientists ensure the results produced are valid and applicablel to the desired organisations goals and confirms that the right questions are being asked by stakeholders. It can help anwer questions like standard deviations, categorical variables etc.

Once EDA is performed and insights gained, its features can be used for more complex or sophisticated data analysis, modelling and even machine learning.

Goals of EDA

Put simply, EDA aims to achieve the below:

• Understand how data is distributed across different variables in your dataset. This helps identify patterns and potential outliers.
• Remove irregularities and unnecessary values from the dataset.
• EDA prepares the dataset for further analysis.
• Draw meaningful conclusions from the data using statistical techniques.
• EDA helps choose the most suitable machine-learning model. It ensures that your model doesn’t suffer from data quality issues due to outliers or anomalies.
• EDA contributes to better predictions by machine learning models.

Types of EDA

1. Univariate Analysis:
   Focuses on analysing a single variable at a time, with the main purpose being understanding the variable's distribution, central tendency, and spread.
   It uses techniques like:

   • Descriptive statistics (Non Graphical) (mean, median, mode, variance, standard deviation).
   • Visualizations (Graphical analysis) using histograms, box plots, bar charts, pie charts to visualise the data.

2. Bivariate Analysis: looks at the relationship between two variables, to understand how one variable is affected or associated to another variable.
   It uses mainly graphical techniques like:

   • Scatter plots, correlations matrices, line plots and pair plots.

3. Multivariate Analysis: looks at the relationship between two or more variables in a data set in order to understand the more complex relationships between the variables and interactions within the data.
   It uses graphical techniques like:

   • grouped bar plots, Multivariate plots (pair plots, parallel coordinates plots), Cluster analysis, heatmaps and correlation matrices.

Tools of Exploratory Data Analysis

The most commonly used tools by data scientists are:

1. Python: An interpreted, object-oriented programming language with high-level, built-in data structures. It uses various libraries such as:

   • Pandas: Provides data structures and functions needed to manipulate structured data seamlessly. Used for data cleaning, manipulation, and summary statistics. Supports large, multi-dimensional arrays and matrices and a collection of mathematical functions.
   • Matplotlib: A plotting library that produces static, animated, and interactive visualizations. Used for basic plots like line charts, scatter plots, and bar charts.
   • SciPy: Builds on NumPy and provides many higher-level scientific algorithms, used in statistical analysis and additional mathematical functions.

2. R: An open-source programming language and free software environment for statistical computing and graphics. It has useful libraries like:

   • ggplot2: A framework for creating graphics using the principles of the Grammar of Graphics. It is used for Complex and multi-layered visualizations.
   • dplyr: A set of tools for data manipulation, offering consistent verbs to address common data manipulation tasks, for use in data wrangling and manipulation.
   • tidyr: Provides functions to help you organize your data in a tidy way; for data cleaning and tidying.

Conclusion

Exploratory Data Analysis is a cornerstone for successful data scientists, acting as a guide through the data wilderness, helping them understand the landscape, uncover patterns and hidden gems within data, and pave the way to successful modeling and actionable insights.

EDA is not a one time journey, and you will continuously revisit for new insights. EDA is your compass that you will continuously refer to in your data journey.

Happy Analysing!

References

1. What Is Exploratory Data Analysis?
2. Exploratory Data Analysis Basics – The Essential Guide
3. What is exploratory data analysis (EDA)?