Data science courses

Learn to use SQL Server to perform common data manipulation tasks and master common data manipulation tasks using this database system.

Learn how to work with dates and times in Python.

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

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

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

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

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). Learn what EDA is for Power BI and how it can help you extract insights from your data.

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

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

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

Learn to retrieve and parse information from the internet using the Python library scrapy.

This course introduces dbt for data modeling, transformations, testing, and building documentation.

Master data modeling in Power BI.

Learn how to manipulate and visualize categorical data using pandas and seaborn.

R Markdown is an easy-to-use formatting language for authoring dynamic reports from R code.

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

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

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

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

This introductory course will help you hone the skills to build effective, performant, and reliable data pipelines.

Gain a clear understanding of data privacy principles and how to implement privacy and security processes.

Understand the fundamentals of Machine Learning and how it's applied in the business world.

Apply your skills to import, analyze and visualize Human Resources (HR) data using Power BI.

Learn essential data structures such as lists and data frames and apply that knowledge directly to financial examples.

Master your skills in NumPy by learning how to create, sort, filter, and update arrays using NYC’s tree census.

Transform almost any dataset into a tidy format to make analysis easier.

Learn about the challenges of monitoring machine learning models in production, including data and concept drift, and methods to address model degradation.

Explore AI ethics focusing on principles, fairness, bias reduction, and trust in AI design.

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

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