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749 students
Exploratory Data Analysis using Python
Master the Art of Data Exploration and Visualization with Python Libraries
Created byPralhad Teggi
Last updated 2/2025
English
English [Auto],
What you'll learn
- Data Cleaning and Preprocessing – Handling missing values, outliers, and data inconsistencies using Pandas and NumPy.
- Data Visualization – Creating insightful visualizations using Matplotlib, Seaborn, and Plotly to understand data distributions and relationships.
- Feature Engineering – Extracting meaningful features and transforming raw data for better analysis and model performance.
- Statistical Analysis – Understanding descriptive statistics, correlation, and hypothesis testing to draw meaningful insights.
- Hands-on EDA with Real-World Datasets – Applying EDA techniques to real-world datasets from domains like finance, healthcare, and environment
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Course content
3 sections • 23 lectures • 1h 48m total length
Requirements
- Basic Python Knowledge – Familiarity with Python syntax, variables, loops, and functions.
- Understanding of Pandas and NumPy – Some experience with data manipulation using Pandas and NumPy is helpful but not mandatory.
- Basic Statistics Concepts – Awareness of mean, median, mode, standard deviation, and correlation will be beneficial.
- Jupyter Notebook or Google Colab – Students should know how to set up and use Jupyter Notebook or Google Colab for coding.
- Curiosity and Enthusiasm for Data Analysis – No prior data science experience is required, but a willingness to explore data is essential.
Description
Data is everywhere, but without proper analysis, it’s just numbers. Exploratory Data Analysis (EDA) using Python helps you uncover patterns, detect anomalies, and extract meaningful insights to make informed decisions.
In this course, you’ll learn to clean, analyze, and visualize data using powerful Python libraries like Pandas, NumPy, Matplotlib, and Seaborn. You’ll explore real-world datasets, handle missing values, identify outliers, and perform feature engineering to prepare data for machine learning. You’ll also understand statistical techniques such as correlation, hypothesis testing, and distributions to interpret data effectively.
By the end of this course, you will:
Master data preprocessing and cleaning techniques
Create compelling visualizations to explore trends
Use statistical methods to gain deep insights
Perform feature engineering for machine learning
Work on hands-on projects with real-world datasets
Develop the ability to summarize large datasets efficiently
Gain confidence in applying EDA for data-driven decision-making
Learn best practices for handling and transforming structured and unstructured data
Whether you’re a beginner, student, data analyst, or developer, this course provides a solid foundation in EDA to advance your data science journey. No prior experience in data science is required—just basic Python knowledge and a curiosity to explore data!
Enroll now and start your journey into the world of Exploratory Data Analysis with Python!
Who this course is for:
- Beginners in Data Science – Anyone looking to start their journey in data analysis and machine learning.
- Students & Researchers – Those working on academic projects and needing strong data exploration skills.
- Data Analysts & Business Professionals – Individuals who want to improve their data interpretation and decision-making abilities.
- Software Developers – Programmers interested in expanding their skill set to data analysis and visualization.
- Anyone Curious About Data – No prior experience in data science is required, just a willingness to learn and explore!