Exploratory Data Analysis for Machine Learning

IBM places exploratory analysis before machine-learning model selection. Data retrieval and cleaning lead into transformations, feature work and statistical questions, then a final analysis project. The provider estimates about fourteen hours.

What you will learn

• Inspect and clean records before modeling.
• Explore variables and prepare candidate features.
• Use statistical questions to guide an analysis.

Who this suits

Intermediate learners who want a stronger preparation and exploration workflow for machine learning.

Before you enroll

The prerequisite field is unspecified. Preview the Python and statistics expectations, and consider how transformations could leak information from evaluation data.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Tool subscriptions, model API usage and cloud services may cost extra.

Put it into practice

Choose a public dataset and write three questions before plotting. Record one data-quality issue and one apparent pattern that disappears after an appropriate grouping.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

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