Learn how to work with environments for continuous deployment of machine learning models.

This course will introduce basic methods to convert categorical variables into quantitative variables for analysis using Python. Without properly prepared data, your model is likely to be inaccurate or incomplete.

Copilots are quickly becoming a popular way to use with AI models, automate tasks, and improve productivity. In this learning path, you'll explore options for adopting, using, and creating copilots.

Learn about the most essential steps of data preparation: Missing value imputation, outlier detection, and duplicate removal.

Introduction to statistics and probability

Data science, Machine learning, statistical test, hypothesis, statistics

Learn all about how DataCamp builds the best platform to learn and teach data skills.

Join Luke Dion, vice president of engineering operations at Cox Automotive, as he shares his team’s data-driven approach to developing skills and driving engineering effectiveness amidst the sudden shift and uncertainty due to COVID-19.

Use JavaScript, Azure, GitHub, and Visual Studio Code to write a web app that helps the Tune Squad coach make data-based decisions on the basketball court, inspired by SPACE JAM: A NEW LEGACY.

In this course, you'll dive into DataFrame indexing with pandas. Follow Abby, a marketing analyst, as she navigates her marketing dataset. You'll learn reindexing, .loc, .iloc, data filtering techniques, and more through interactive demos.

Explore model explanations, error analysis, counterfactuals, and causal analysis by creating a Responsible AI dashboard. You'll create and run the pipeline in Azure Machine Learning using the Python SDK v2 to generate the dashboard.

Creating shared data assets for your analytics environment provides structure and consistency. Maintaining those assets is as important, and XMLA endpoint provides additional administrative capabilities.