Analytics Tips #1 - 7 Steps to Mastering Exploratory Data Analysis
Analytics Tips #1 - 7 Steps to Mastering Exploratory Data Analysis
A Step-by-Step Approach to Unearthing Trends, Outliers, and Insights in your Data.
Hey everyone! This is Josep, one more week 👋🏻
This week, we're kicking off the Analytics Tips series, where we'll explore different aspects of data science in an easy-to-understand way.
Today we'll explore the 7 steps to mastering Exploratory Data Analysis (EDA), a fundamental concept that helps you understand your data and unlock its potential.
For those who are curious where these 7 steps come from, I always follow the most basic steps according to Ayodele Oluleye in his book Exploratory Data Analysis with Python Cookbook.
By mastering EDA, you'll be equipped to extract valuable insights and make informed decisions based on your data.
Step 1: Ask Questions and Define Goals
Before diving into the data, it's crucial to define your goals and the questions you want to answer through EDA.
🎯 This step helps you stay focused and ensures your analysis is relevant to your project objectives.
Step 2: Collect and Understand Data
Gather the data you'll be working with and get familiar with its format, structure, and any potential issues. This might involve checking for missing values, inconsistencies, and outliers.
Step 3: Describe the Data
Analyze the basic characteristics of your data using descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation).
Step 4: Visualize the Data
Create visualizations like histograms, scatter plots, and boxplots to explore the distribution of your data, identify patterns and relationships between variables, and detect potential anomalies.
Step 5: Clean the Data
Address any data quality issues you identified in step 2. This might involve handling missing values, correcting inconsistencies, and transforming variables as needed.
🎯 This step helps you make sure your data is ready to be used.
Step 6: Feature Engineering
Create new features from existing ones to improve the effectiveness of your machine learning models. This step involves techniques like scaling, encoding categorical variables, and dimensionality reduction.
Step 7: Model Selection and Evaluation
Based on your EDA findings, choose appropriate machine learning models and evaluate their performance on a held-out test set. This step helps you select the best model for your specific task.
By following these steps, you'll gain a deep understanding of your data, identify patterns and trends, and prepare it for further analysis and modeling.
To learn more details and explore different techniques for each step you can check the following article.
And this is all for now!
If you have any suggestions or preferences, please comment below or message me through my social media!
Remember you can also find me in X, Threads, Medium and LinkedIn 🤓