Data Analysis with Python

IBM follows a dataset from import through preparation, exploration and a predictive model. The roughly sixteen-hour outline emphasizes both transformations and evaluation, ending with an assignment that combines the workflow. It is a continuation for someone who already uses Python notebooks.

What you will learn

• Clean missing values, formats and other data inconsistencies.
• Explore distributions and relationships with Python analysis libraries.
• Evaluate and refine a regression model.

Who this suits

Learners with working knowledge of Python and Jupyter Notebooks.

Before you enroll

Python and notebooks are explicit requirements. Keep evaluation data separate from model fitting, and distinguish correlation from an explanation of cause.

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Put it into practice

Analyze a public tabular dataset with one documented cleaning pipeline. Compare a simple prediction baseline with a regression model using the same held-out records.

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.

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