Data, AI, and Cloud Courses

Learn how to build interactive and insight-rich dashboards with Dash and Plotly.

Explore GDPR through real-world cases on data rights, breaches, and compliance challenges.

Learn to tame your raw, messy data stored in a PostgreSQL database to extract accurate insights.

Ensure high data quality in data science and data engineering workflows with Pythons Great Expectations library.

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

Using Python and NumPy, learn the most fundamental financial concepts.

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

Discover what all of the DeepSeek hype was really about! Build applications using DeepSeeks R1 and V3 models.

Learn how to structure your PostgreSQL queries to run in a fraction of the time.

Master time series data manipulation in R, including importing, summarizing and subsetting, with zoo, lubridate and xts.

In this course you will learn how to predict future events using linear regression, generalized additive models, random forests, and xgboost.

Learn about the difference between batching and streaming, scaling streaming systems, and real-world applications.

Learn how to identify, analyze, remove and impute missing data in Python.

In this course, youll learn the basics of relational databases and how to interact with them.

This course provides an intro to clustering and dimensionality reduction in R from a machine learning perspective.

Learn to bring data into Microsoft Fabric, covering Pipelines, Dataflows, Shortcuts, Semantic Models, security, and model refresh.

Create visualizations and dynamic dashboards with Databricks, turning raw data into clear and actionable insights.

You will use Net Revenue Management techniques in Excel for a Fast Moving Consumer Goods company.

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Master SQL Server programming by learning to create, update, and execute functions and stored procedures.

Learn to work with Plain Old Java Objects, master the Collections Framework, and handle exceptions like a pro, with logging to back it all up!

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Learn how to manipulate data and create machine learning feature sets in Spark using SQL in Python.

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

Learn about AWS Boto and harnessing cloud technology to optimize your data workflow.

Learn the fundamentals of using DataLab, an AI-powered data notebook for data analysis and exploration.

Use RNA-Seq differential expression analysis to identify genes likely to be important for different diseases or conditions.

Sharpen your knowledge and prepare for your next interview by practicing Python machine learning interview questions.

Learn key financial concepts such as capital investment, WACC, and shareholder value.