Exploratory Data Analysis (EDA) is like exploring a new place. Just as you walk around, notice things, and try to understand your surroundings, EDA helps you explore a dataset. You look at the data, check its different parts, and try to
understand what it tells you. This involves using simple math and charts to summarize the data and see it from different perspectives, without assuming anything about it beforehand.
Typical Process
- Look at the Data: Start by understanding the dataset. Check how many rows and columns it has and what type of data is in each column (like numbers, text, or dates). Look at each variable to see its range and distribution.)
- Clean the Data: Fix any problems in the data, such as missing or incorrect values. Cleaning the data is important so you an trust it for analysis or making predictions
- Summarize the Data: Get a quick overview of the data by calculating things like averages, most common values, or how values are spread out. Look at metrics like quantiles (e.g., median, percentiles) to understand how the data is distributed.
- Visualize the Data: Use charts and graphs to make the data easier to understand. For example, bar charts, scatter plots, or line graphs can show patterns, trends, or unusual data points. Tools like Python libraries (pandas, Matplotlib, Seaborn, etc.) help create these visuals.
- Find Answers: Investigate further to answer these questions. This might involve deeper analysis or building models like regression to understand relationships or predict outcomes.
Types of EDA Techniques
Before working with a dataset, it’s helpful to know the main types of Exploratory Data Analysis (EDA) techniques. Here are six key types:
- Univariate Analysis: Look at one variable at a time to understand its basic features, like the average (mean), middle value (median), most common value (mode), or how spread out the values are (standard deviation). Use simple charts like histograms, bar charts, and box plots to visualize it
- Bivariate Analysis: Compare two variables to see how they relate to each other. Use tools like scatter plots or heatmaps to spot patterns, trends, or connections between them.
- Multivariate Analysis: Look at more than two variables together to understand how they interact. This helps when studying complex relationships. Techniques like contour plots or PCA (Principal Component Analysis) can simplify this.
- Visualization Techniques: Charts and graphs make data easier to understand. Use bar charts, line charts, scatter plots, or heatmaps to show trends, relationships, or data distributions in a clear way.
- Outlier Detection: Find unusual values that stand out from the rest of the data. Outliers can be identified using tools like box plots or scatter plots. They might show errors in data or reveal interesting insights
- Statistical Tests: Use math-based tests to check if patterns or differences in the data are meaningful. For example, t-tests or ANOVA can help confirm whether the differences between groups are significant
By combining these techniques, you can better understand your data, spot patterns, clean it up, and prepare it for deeper analysis