Applied Statistics for Data Analytics

DeepLearning.AI develops the statistical reasoning behind an analysis. Sampling and descriptive measures lead into probability, simulation and inference. At roughly thirty-five hours, the course gives more attention to uncertainty than a short tool-specific analytics introduction.

What you will learn

• Summarize distributions while examining sampling bias.
• Use probability and simulation to represent uncertainty.
• Interpret confidence intervals and hypothesis tests.

Who this suits

New analytics learners who want to understand the reasoning behind numerical claims.

Before you enroll

The provider lists no prior experience requirement. Spreadsheet exercises and statistical interpretation need active practice even when a formula is already supplied.

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

Simulate repeated samples from a known distribution. Compare their estimates and explain why a confidence interval is more informative than a single reported number.

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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