Data Science

Employ a multiparadigm approach for your data science projects. Develop modular, flexible and scalable workflows to import, process, analyze and visualize data. Learn about easy-to-use functions, repositories, frameworks and interfaces while applying algorithms and techniques from across multiple disciplines.

These courses showcase some of the computational processes driving data analysis and visualization, the application of automated machine learning tools and the use of natural language queries within a symbolic framework. Topics covered also include the use of interactive Wolfram Notebooks and the cloud for generating reports and deploying data products. Earn course completion certificates and work toward Level 1 and Level 2 data science certifications.

Upcoming Events

  • Sep 24 | Online

    Data Visualization with Wolfram Language

    With the use of a curated dataset from the Wolfram Data Repository, this course shows how to quickly visualize different data structures and how to make your graphics ready to publish and share. Domain-specific functions as well as general techniques are shared for getting the most out of your graphics. The course is intended for anyone who wants to learn more about data visualization using Wolfram Language.

  • OCT 8 | Online

    Function and Data Visualization: A Showcase of Plots, Charts and Graphics

    Expand your skills for visualizing mathematical functions and improve your ability to represent different types of data with plots, charts and graphics of all kinds. You'll learn how functions and data can be plotted in one, two or three dimensions with Wolfram Language.

  • OCT 2 & 9 | Online

    Friday Study Group: Exploring Creative Data Analytics

    This Study Group meets over two consecutive Fridays and explores case studies applying modern computation to business analytics. See examples that show how to work with text, image, video and web data in Wolfram Language. No prior programming experience is required, making this an ideal starting point for newcomers to computational analytics.