AI Workflow: Data Analysis and Hypothesis Testing

IBM via Coursera

Coursera based on 123 ratings

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Overview

"AI Workflow: Data Analysis and Hypothesis Testing" is an essential course designed for professionals and data enthusiasts who want to learn how artificial intelligence can streamline data analysis and support hypothesis testing in scientific and business applications. This course offers a comprehensive guide to the entire AI-driven workflow, from data collection to analyzing complex datasets and drawing meaningful conclusions.

Syllabus

Data Analysis

Exploratory data analysis is mostly about gaining insight through visualization and hypothesis testing. This unit looks at EDA, data visualization, and missing values. One missing value strategy may be better for some models, but for others another strategy may show better predictive performance.

Data Investigation

Data scientists employ a broad range of statistical tools to analyze data and reach conclusions from data. This unit focuses on the foundational techniques of estimation with probability distributions and extending these estimates to apply null hypothesis significance tests.