Coding has become a necessary skill for today’s researchers to collect, organize, analyze, visualize, and communicate their data. Yet most of us are never trained in these vital skills. At Code Horizons, we help you learn the skills you need to take your work to the next level.
November 11-13, 2021
This seminar provides a comprehensive introduction to Python, a a free, open-source language for modern data science and data analysis.
December 2-4, 2021
This seminar provides a comprehensive introduction working with agent-based models of social systems, a class of formal models in which individual actors are instantiated as computational objects.
December 9-11, 2021
This seminar is designed to teach all of the steps involved with data cleaning, from merging your data to documenting and presenting your data to trickier issues like identifying missing data.
Designing Effective Online Surveys
January 27-29, 2022
This course provides a practical introduction to designing effective web surveys and working with online respondents.
More information to follow
Passion. Experience. Knowledge.
Learn From The Best
Code Horizons instructors are recognized experts in their fields of study. They have each published extensively, and have many years of experience in research and coding. Most importantly, they are demonstrably excellent instructors with the skills to present coding methods in an efficient, stimulating, and easy-to-follow manner to participants from all disciplines and backgrounds.
Our Latest News
New Blog Post
Aaron Gullickson, instructor of GitHub for Data Analysis, gives a brief introduction to the benefits of using git for R projects and how to create your first repository using GitHub.
We’re excited to welcome Paul Smaldino, who will teach the basics for understanding, building, and analyzing agent-based models, a class of formal models in which individual actors are instantiated as computational objects, using NetLogo.
Introducing Data Cleaning
Learn all the steps involved with cleaning your data, plus a framework for approaching data cleaning that allows you to represent data both accurately and fully.