Data science courses

Data Analysis Expressions (DAX) allow you to take your Power BI skills to the next level by writing custom functions.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Data-driven organizations consistently rely on insights to inspire action and drive change.

In ecommerce, increasing sales and reducing expenses are top priorities. In this case study, you'll investigate data from an online pet supply company.

Find tables, store and manage new tables and views, and write maintainable SQL code to answer business questions.

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

Learn to clean data as quickly and accurately as possible to help your business move from raw data to awesome insights.

Learn the fundamentals of working with big data with PySpark.

Consolidate and extend your knowledge of Python data types such as lists, dictionaries, and tuples, leveraging them to solve Data Science problems.

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

In this course you'll learn the basics of working with time series data.

Learn about string manipulation and become a master at using regular expressions.

Learn to use facets, coordinate systems and statistics in ggplot2 to create meaningful explanatory plots.

Learn to connect Tableau to different data sources and prepare the data for a smooth analysis.

Master data modeling in Power BI.

In this course you'll learn how to get your cleaned data ready for modeling.

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

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.

Learn to write SQL queries to calculate key metrics that businesses use to measure performance.

In this course you will learn the details of linear classifiers like logistic regression and SVM.

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

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

In this course, you will use T-SQL, the flavor of SQL used in Microsoft's SQL Server for data analysis.

Analyze text data in R using the tidy framework.

Gain an introduction to data governance, exploring its meaning, purpose, and how to implement a data governance framework.

Expand your spreadsheets vocabulary by diving deeper into data types, including numeric data, logical data, and missing data.

Learn to process, transform, and manipulate images at your will.

Learn how to implement and schedule data engineering workflows.

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