Data, AI, and Cloud Courses

Grow your machine learning skills with scikit-learn in Python. Use real-world datasets in this interactive course and learn how to make powerful predictions!

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Learn to combine data from multiple tables by joining data together using pandas.

Learn how to explore, visualize, and extract insights from data using exploratory data analysis (EDA) in Python.

Learn how to build your first neural network, adjust hyperparameters, and tackle classification and regression problems in PyTorch.

Learn how to explore whats available in a database: the tables, relationships between them, and data stored in them.

Learn how to create queries for analytics and data engineering with window functions, the SQL secret weapon!

Learn to diagnose and treat dirty data and develop the skills needed to transform your raw data into accurate insights!

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Learn how to cluster, transform, visualize, and extract insights from unlabeled datasets using scikit-learn and scipy.

Learn to implement distributed data management and machine learning in Spark using the PySpark package.

This course will take you from Snowflakes foundational architecture to mastering advanced SnowSQL techniques.

Learn the most important PostgreSQL functions for manipulating, processing, and transforming data.

Improve your Python data importing skills and learn to work with web and API data.

In this course, youll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

Learn Java from the ground up with this beginner-friendly course, mastering essential programming concepts and skills.

You’ll learn how to (un)pivot, transpose, append and join tables. Gain power with custom columns, M language, and the Advanced Editor.

This introductory and conceptual course will help you understand the fundamentals of data warehousing.

Learn to write efficient code that executes quickly and allocates resources skillfully to avoid unnecessary overhead.

Learn fundamental natural language processing techniques using Python and how to apply them to extract insights from real-world text data.

Master data modeling in Power BI.

Gain an introduction to Docker and discover its importance in the data professional’s toolkit. Learn about Docker containers, images, and more.

Discover a wide range of DAX calculations and learn how to use them in Microsoft Power BI.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Learn to use best practices to write maintainable, reusable, complex functions with good documentation.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Learn how to analyze a SQL table and report insights to management.

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Take your Power BI visualizations up a level with the skills you already have. Learn alternative data storytelling techniques to simply building dashboards.

Discover how MLOps can take machine learning models from local notebooks to functioning models in production that generate real business value.