Top 10 EDA Techniques Every Data Scientist Should Know

Exploratory Data Analysis (EDA) is one of the most crucial steps in any data science project. It allows you to understand your data, spot errors, and uncover patterns before building models. For beginners and professionals alike, mastering a few key EDA techniques can make analysis faster and more accurate.

Here’s a simple guide to the top 10 EDA techniques every data scientist should know.

1. Understand Your Dataset

Before diving into analysis, get to know your dataset:

• Check the number of rows and columns

• Understand the data types of each column

• Look at the first few rows to get an overview

Python commands like data.info(), data.head(), and data.shape are perfect for this step.

2. Summary Statistics

Use statistical summaries to understand your data better:

• Mean, median, and mode

• Minimum and maximum values

• Standard deviation

In Python, data.describe() provides a quick overview of numerical columns.

3. Check for Missing Values

Missing data can affect your analysis:

• Identify missing values using data.isnull().sum()

• Decide whether to remove or fill missing values

Handling them early ensures more reliable results.

4. Handle Duplicates

Duplicate rows can skew your insights:

• Remove duplicates with data.drop_duplicates(inplace=True)

• Check for redundant columns or entries

5. Univariate Analysis

Analyze each variable individually:

• Use histograms to see distribution

• Boxplots help detect outliers

• Count plots for categorical variables

This step gives a clear picture of individual features.

6. Bivariate Analysis

Study relationships between two variables:

• Scatter plots to see correlations

• Correlation matrices to measure linear relationships

• Crosstabs for categorical variables

Understanding relationships helps in feature selection.

7. Outlier Detection

Outliers can distort results:

• Use boxplots or scatter plots to identify outliers

• Decide whether to keep or remove them based on context

8. Feature Engineering

Create new features or modify existing ones:

• Transform variables (like log or square root transformations)

• Combine or split features to capture more information

Feature engineering can improve model performance significantly.

9. Data Visualization

Visualizations make patterns easy to spot:

• Histograms, bar charts, and boxplots for distribution

• Scatter plots and line charts for trends

• Heatmaps for correlation visualization

Tools like Matplotlib, Seaborn, or Plotly make this simple and effective.

10. Document Your Insights

After exploration:

• Summarize key findings

• Note patterns, trends, and anomalies

• Plan next steps based on insights

This ensures that your analysis is actionable and reproducible.

Conclusion

Mastering these top 10 EDA techniques helps data scientists turn messy data into meaningful insights. By combining statistics, visualization, and careful observation, you can make better decisions and build more accurate models.

For anyone looking to gain hands-on experience and become a professional data scientist, joining a top 10 data science institute in Pune can provide the guidance, projects, and mentorship needed to excel in EDA and beyond.