Exploratory Data Analysis (EDA)

Issue #23 | Aug 21

Hello!!

We hope you enjoyed our previous newsletter on Data Wrangling! It was all about taming messy, raw data and transforming it into something clean and ready for analysis.

In this edition, we’re diving into the world of Exploratory Data Analysis (EDA). EDA is a critical process in data science where you summarize the main characteristics of a dataset, often with visual methods. This step allows you to discover patterns, spot anomalies, test hypotheses, and check assumptions with the help of summary statistics and graphical representations.

Source: MarkovML

Trending

1. How LLMs Will Democratize Exploratory Data Analysis
   The rise of Large Language Models (LLMs) is set to transform EDA by making it accessible to non-technical users, enabling them to extract insights effortlessly. Read more.

2. 4 Ways to Automate Exploratory Data Analysis (EDA) in Python
   Automating EDA processes can significantly speed up data insights. Explore four powerful ways to automate your EDA in Python. Read more.

3. How to Do an EDA for Time Series Data
   Conducting EDA on time series data involves unique challenges. Learn how to approach it with these insightful tips. Read more.

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Python code for heat map visualization

# Import the necessary library
import matplotlib.pyplot as plt
import numpy as np

# Create sample data
data = np.random.rand(10, 10)

# Create a heatmap
plt.imshow(data, cmap='hot', interpolation='nearest')
plt.colorbar()

# Set plot title and labels
plt.title("Heatmap")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")

# Display the plot
plt.show()

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In our last email we talked about Data Wrangling Techniques. Please read here
Or search ‘businessanalytics@substack.com’ in your mailbox.

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Recommended Reads

1. Introduction to Exploratory Data Analysis by IBM
   EDA involves summarizing a dataset's main characteristics, and this introduction by IBM gives you a solid overview of the process. Read more.

2. Exploratory Data Analysis in R for Data Science
   Dive into Chapter 7 of "R for Data Science" to understand how to conduct effective EDA in R. Read more.

3. The NIST Handbook: EDA Techniques
   The NIST Handbook offers detailed guidance on various EDA techniques, helping you make the most of your data exploration. Read more.

Tool of the Day: Seaborn (Python Library)

Seaborn is a Python visualization library based on Matplotlib that makes it easy to create informative and attractive statistical graphics. It’s particularly well-suited for EDA, offering tools to make complex plots like heatmaps and violin plots with just a few lines of code. Seaborn simplifies the process of visualizing your data to identify trends and patterns at a glance.

Learn more

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Stay tuned for our next edition on Data Cleaning!

STUDY DATA VISUSUALIZATION.
105 Python codes on Data Visualization. Learn More