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Machine Learning/AI Series: Getting Started with Data Visualization

New session times will be displayed below upon confirmation.

Most Technology Training classes will be delivered online until further notice.

Before each sesson, Tech Training will provide a Zoom link for live online classes, along with any required class materials.

 

This session is intended to get you started with using tools to visualize data for data exploration prior to performing machine learning on the data.

Prerequisite:
Have a basic understanding of the Python language, Pandas library, and be familiar with how to use Jupyter Notebook.

Audience:
This session is designed for anyone who wants to start exploring machine learning, and understand the tools and techniques involved in visualizing data before doing machine learning.

Objectives
Use Matplotlib and Seaborn libraries to explore data.

Learn how to do the following:

  • Plot different types of charts -- pie, bar, histograms to explore the data.
  • Visualize correlations in the dataset.

Setup
Attendees must install Anaconda software (https://www.anaconda.com/) prior to the class and have a basic understanding of using Jupyter Notebook.

Antony Ross

Antony originally attained a degree in psychology with an emphasis in sport psychology. He began working with athletes and eventually chose to pursue a graduate degree in exercise physiology. He conducted research in muscle physiology while teaching at USC and, subsequently, UCLA.

Custom training workshops are available for this program

Technology training sessions structured around individual or group learning objectives. Learn more about custom training

University IT Technology Training sessions are available to a wide range of participants, including Stanford University staff, faculty, students, and employees of Stanford Hospitals & Clinics, such as Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health.

Additionally, some of these programs are open to interested individuals not affiliated with Stanford, allowing for broader community engagement and learning opportunities.