Exploratory Data Analysis Courses

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Join our free Exploratory Data Analysis courses and learn key statistical and visualization techniques. Boost your data science skills and make informed decisions. Enroll now and earn certificates upon course completion.

4.6

4.89

4.94

4.7

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4.7

UNIVERSITY

Great Learning

Generative AI for Practitioners

12 weeks • Online

UNIVERSITY

Walsh College

MS in Artificial Intelligence & Machine Learning

2 Years • Online

PRO

2 projects

Master Data Analytics in Excel

View Course

UNIVERSITY

MIT IDSS

AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact

12 weeks • Online

UNIVERSITY

MIT Professional Education

No Code and Agentic AI

14 Weeks • Online

Learn from MIT Faculty

UNIVERSITY

Johns Hopkins University

Applied Generative AI and Agentic AI

16 weeks • Online

BASICS

4.51 103.7K+ Learners 1.5 hrs

Exploratory Data Analysis Essentials

Skills: Exploratory data analysis, summary statistics, data cleaning, visualization (histograms, boxplots, scatter), handling missing values

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BASICS

Intro to Exploratory Data Analysis with Excel

4.59 16.7K+ Learners 1.5 hrs

Intro to Exploratory Data Analysis with Excel

Skills: EDA Basics ,Data Analysis ,Data Cleaning,Data Manipulation,Univariate Analysis

View Course

PRO

2 projects

Master Data Analytics in Excel

View Course

BASICS

4.55 12.8K+ Learners 2 hrs

Basics of EDA with Python

Skills: Practical visualization walkthrough, IPL data analysis with Python

View Course

BASICS

Exploratory Data Analysis Essentials

4.51 103.7K+ Learners 1.5 hrs

Skills: Exploratory data analysis, summary statistics, data cleaning, visualization (histograms, boxplots, scatter), handling missing values

BASICS

Intro to Exploratory Data Analysis with Excel

4.59 16.7K+ Learners 1.5 hrs

Skills: EDA Basics ,Data Analysis ,Data Cleaning,Data Manipulation,Univariate Analysis

PRO

Master Data Analytics in Excel

4.5 64.6K+ Learners 5.5 hrs

BASICS

Basics of EDA with Python

4.55 12.8K+ Learners 2 hrs

Skills: Practical visualization walkthrough, IPL data analysis with Python

Explore Courses

Learner reviews of the Free Exploratory Data Analysis Courses

Our learners share their experiences of our courses

4.53

★★★★

★ ☆

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72%

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19%

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Sakeena Fatima

5.0

★★★★ ★

India

“Informative and Well-Structured Course with Helpful Hands-On Exercises”

The course provided a solid foundation in data manipulation techniques. The hands-on exercises were particularly helpful in reinforcing the concepts. The instructor's explanations were clear and concise, making it easy to follow along. While the course covered a wide range of topics, a deeper dive into advanced data cleaning and visualization techniques would have been beneficial. Overall, the course was a valuable learning experience.

ANGEL MERRIN JOHN URK22CS2017

5.0

★★★★ ★

India

“Introduction to Exploratory Data Analysis: Unlocking Insights with Great Learning”

This course provides a comprehensive introduction to Exploratory Data Analysis (EDA), focusing on fundamental techniques and tools to analyze and visualize data. You'll learn how to uncover patterns, identify anomalies, and derive actionable insights, using real-world datasets and practical examples. Perfect for beginners looking to build a solid foundation in data exploration with Great Learning's expert guidance.

Bhehul Shirish Rajderkar

4.0

★★★ ★ ☆

India

“Learning to effectively analyze and visualize data through practical exercises was the highlight of the course.”

The course provided a comprehensive overview of exploratory data analysis techniques, from basic statistics to advanced visualizations. I particularly enjoyed the hands-on approach and real-world examples, which made the learning process both engaging and applicable to real-life data challenges.

rachana acharya | AP22110011632

4.0

★★★ ★ ☆

India

“As a helpful assistant, I've had the opportunity to work with various datasets and perform Exploratory Data Analysis (EDA) to gain insights and understand the data”

Exploratory Data Analysis (EDA) is a crucial step in the data science process that involves using various techniques and tools to understand and summarize the main characteristics of a dataset. The goal of EDA is to identify patterns, trends, and correlations, and to detect anomalies and outliers. Some common EDA techniques include summary statistics, data visualization, correlation analysis, regression analysis, cluster analysis, dimensionality reduction, and anomaly detection. Popular EDA tools and libraries include Pandas.

Sanjivani Chavan

4.0

★★★ ★ ☆

India

“The well-structured and understandable nature of the material is particularly beneficial for new learners.”

Clear progression of concepts: The curriculum effectively builds upon prior knowledge, ensuring a smooth transition from simpler to more complex topics. Consistent terminology and notation: The use of consistent terminology and notation throughout the material helps to avoid confusion and promotes a deeper understanding of the concepts. Relevant examples and applications: Real-world examples and applications are used to illustrate theoretical concepts, making them more relatable and easier to grasp. Well-organized content: The material is divided into logical sections or chapters, with clear headings and subheadings to guide learners through the content. Effective use of visuals: Diagrams, charts, and other visual aids are used to enhance understanding and clarify complex ideas. Sufficient practice opportunities: The curriculum provides ample opportunities for practice through exercises, assignments, and quizzes, reinforcing learning and allowing learners to apply their knowledge.

Sanjeevni

5.0

★★★★ ★

India

“It made the learning super easy and fun.”

Fantastic experience. This course helped me to gain in-depth knowledge of the topic and made my concepts clear in the easiest way.

PAAL NESAN J URK22CS1004

5.0

★★★★ ★

India

“Basics of Exploratory Data Analysis”

Overall, I found the course to be informative and well-structured. The content was presented in a clear and concise manner, making it easy to follow along. The exercises provided ample opportunity to practice the concepts learned. Specific Feedback: Content: The coverage of essential EDA techniques was comprehensive. I particularly appreciated the focus on data visualization, as it's a crucial tool for understanding and communicating insights.

Shashwata Nandi

4.0

★★★ ★ ☆

India

“Explored data patterns and insights through visualizations.”

Exploratory Data Analysis (EDA) is a crucial step in the data analysis process that involves investigating and visualizing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. My experience with EDA has been both enriching and enlightening, allowing me to uncover patterns and insights that are critical for informed decision-making.

Pradeepta Bhanja

5.0

★★★★ ★

India

“Amazing and easy to understand”

It's easy to understand and concept clarity is on top. Solving examples along with theory is great.

Shravani

5.0

★★★★ ★

India

“Basics of Exploratory Data Analysis is a very good course”

This course is very useful and makes it easy for us to learn in detail. Thank you for teaching.

Meet your faculty

Meet industry experts who will teach you relevant skills in Exploratory Data Analysis

Bharani has been working in the field of data science for the last 2 years. He has expertise in languages such as Python, R and Java. He also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow. He has been in the technical content side from last 2 years and has taught numerous classes with respect to data science.