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

Learn how to describe relationships between two numerical quantities and characterize these relationships graphically.

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

Learn to design databases in SQL.

Learn to create your own Python packages to make your code easier to use and share with others.

The xts and zoo packages make the task of managing and manipulating ordered observations fast and mistake free.

In this course you'll learn how to perform inference using linear models.

Master core concepts about data manipulation such as filtering, selecting and calculating groupwise statistics using data.table.

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

Learn the core techniques necessary to extract meaningful insights from time series data.

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

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

Learn the fundamentals of AI. No programming experience required!

Take your R skills up a notch by learning to write efficient, reusable functions.

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