Learn Exploratory Data Analysis (EDA) from Scratch | EDA in 5 hours | Satyajit Pattnaik

Introduction

This tutorial aims to provide a comprehensive guide to Exploratory Data Analysis (EDA) based on the teachings from Satyajit Pattnaik's video. EDA is crucial for understanding your data, identifying patterns, and revealing hidden trends. You'll learn practical techniques to clean, preprocess, and analyze data effectively, whether you are a beginner or an experienced data professional.

Step 1: Understand the Data Analysis Process

• Familiarize yourself with the overall data analysis and data science processes.
• Identify key stages:
  • Data collection
  • Data cleaning
  • Data exploration (EDA)
  • Data modeling
  • Data interpretation and presentation

Step 2: Learn What EDA Is

• EDA is an approach to analyzing data sets to summarize their main characteristics.
• It employs visual methods to uncover patterns and anomalies.
• Understand the importance of asking the right questions to guide your analysis.

Step 3: Explore Data Visualization Techniques

• Visualization helps to interpret data effectively.
• Common visualization methods include:
  • Histograms
  • Box plots
  • Scatter plots
  • Heatmaps
• Use visualization tools like Matplotlib and Seaborn in Python.

Step 4: Data Sourcing

• Identify and gather the data you need for analysis.
• Sources can include:
  • Public datasets
  • Company databases
  • APIs
• Ensure data is relevant to your analysis goals.

Step 5: Clean Your Data

• Data cleaning is essential for accuracy.
• Steps include:
  • Removing duplicates
  • Handling missing values
  • Correcting inconsistencies
• Use libraries like Pandas in Python to streamline this process.

Step 6: Feature Scaling

• Understand the importance of scaling features to ensure equal weighting in analysis.
• Common methods include:
  • Min-Max scaling
  • Standardization (Z-score)
• Apply scaling techniques using libraries like Scikit-learn.

Step 7: Manage Outliers

• Identify outliers that can skew analysis results.
• Techniques to treat outliers:
  • Removing them
  • Transforming data
• Use box plots or Z-scores to detect outliers.

Step 8: Handle Invalid Data

• Identify and rectify any invalid data points.
• This may involve:
  • Checking for negative values in a context that requires only positive numbers
  • Ensuring data type consistency
• Document your cleaning process for transparency.

Step 9: Understand Types of Data

• Differentiate between:
  • Categorical vs. numerical data
  • Continuous vs. discrete data
• Understanding data types is crucial for selecting appropriate analysis methods.

Step 10: Perform Types of Analysis

• Conduct various analyses to gain insights:
  • Univariate Analysis: Analyze one variable at a time.
  • Bivariate Analysis: Explore the relationship between two variables.
  • Multivariate Analysis: Investigate interactions among three or more variables.
• Use statistical tests to support your findings.

Step 11: Create Derived Metrics

• Develop new metrics from existing data to provide deeper insights.
• This may involve calculations like ratios or averages.

Step 12: Implement Feature Binning

• Binning transforms continuous variables into categorical ones.
• This can simplify analysis and improve model performance.
• Use techniques like equal-width or equal-frequency binning.

Step 13: Use Feature Encoding

• Convert categorical variables into numerical format for analysis.
• Common encoding methods include:
  • One-Hot Encoding
  • Label Encoding
• Libraries like Pandas can help with encoding.

Step 14: Apply a Case Study

• Engage in a practical case study to apply your EDA skills.
• Analyze a dataset from start to finish, documenting your process and findings.

Conclusion

In this tutorial, you learned the foundational steps of Exploratory Data Analysis, from understanding the data analysis process to applying various techniques like data cleaning, feature scaling, and visualization. As a next step, practice these concepts on real datasets to solidify your skills and enhance your data analysis capabilities. For further learning, refer to the resources and code provided in the video description.