Exploratory Data Analysis (EDA) Learning Path

An interactive course that teaches the fundamentals of EDA in Python, covering summary statistics, data visualization, and preparing data for machine learning models.

Part of the IBM Data Analyst Professional Certificate, this course covers the fundamentals of data analysis using Python, including working with data, exploratory data analysis, and an introduction to machine learning models.

This course covers the essential exploratory techniques for summarizing data. It is part of the Data Science Specialization from Johns Hopkins University and focuses on applying these techniques before formal modeling.

Learn to think like a data scientist by using interactive features in MATLAB to explore, analyze, and visualize data. The course focuses on extracting subsets of data, computing statistics, and creating customized visualizations.

A project-based course that teaches practical techniques for cleaning messy data in Microsoft Excel, including data manipulation and transformation.

A Wesleyan University specialization that teaches how to analyze and interpret data, with a focus on statistical methods and their application in various fields.

This course provides a practical guide to data cleaning in R, covering everything from common data problems to techniques for tidying data.

This course teaches how to use graphical and numerical techniques in R to uncover the structure of your data and identify interesting relationships and unusual observations.

This course focuses on the pandas library, a powerful tool for data manipulation and analysis in Python. It is a great precursor to learning about regression and other machine learning techniques.

A career track focused on using R for data analysis, covering data manipulation, visualization, and case studies to build practical EDA skills.

A hands-on learning path that teaches data cleaning and preprocessing in Python, covering topics from basic data cleaning tasks to more advanced techniques for handling messy data.

A free, self-paced course covering the concepts of anomaly and outlier detection, including handling missing values and data visualization.

This course from Intel provides practical knowledge on the theory and methods used for anomaly detection, from beginning to advanced levels, with implementation in Python.

This course provides a step-by-step guide to using Python for data analysis, including data cleaning, manipulation, and visualization for EDA.