Data Visualization

Asynchronous

This program is included with Career Launch Cohort Enrollment and Erdős Institute Alumni Club Membership at no additional cost.

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Registration Deadlines

Category

Launch, Supplemental, Self-Directed, Mini-Course

Overview

Our materials touch on the following content to varying degrees:
• Plotting in Python: matplotlib, seaborn, plotly, and bokeh
• Web browser visualizations: HTML, CSS, SVG, and d3.js
• Basic Tableau
• Basic design principles

Note: our asynchronous mini-courses do not have a project component and do not offer certification. Any references to either projects or certificates you find in these course materials are remnants of earlier iterations of the course where these components were present.

Syllabus

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Organizers, Instructors, and Advisors

Steven Gubkin

Head of Training and Assessment

Office Hours:

By appointment only

Email:

steven@erdosinstitute.org

Preferred Contact:

Slack

Message me on Slack if you have questions about the course! I am also your primary contact for GitHub access.

Matthew Osborne, PhD

Lead Instructor, Senior Operations Analyst

Office Hours:

None

Email:

matthewosborne71@erdosinstitute.org

Preferred Contact:

Slack

Please direct all course questions to Steven Gubkin.

Objectives

The aim of this mini course is to teach you how to produce data visualizations in a variety of programming languages/softwares while also touching on fundamental design principles.

Project Examples

View the full project database >>>

First Steps/Prerequisites

Participants should have a base-level familiarity with Python. If you are new to Python, but would still like to participate you can review our existing Python Prep materials to get up to speed. It will also be helpful, but not necessary to have a basic understanding of probability and statistics. If you would like to review some statistics or probability should check out the slides at these links:

Probability Theory: https://docs.google.com/presentation/d/1BPdJqdOrtxoBkBnP1iqzZtw0tPRkmRZdcgZm0oiTc9s/edit?usp=sharing

Statistics: https://docs.google.com/presentation/d/1lOOEeCWw3c19dHhGAxHH0FqEajxId_hK3Qb9e6rQ1Hs/edit?usp=sharing

First Steps

Program Content

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Course materials are available on github through the following link:

https://github.com/TheErdosInstitute/data-viz

Add your full github URL (e.g. https://github.com/{your_username})

Request Access to GitHub

We introduce our final Python visualization package, plotly.

Slides

Transcript

Code

Project/Homework Instructions

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Project/Team Formation

Project Submission

Projects README

Schedule

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Orientation & Setup

Phase 1: Instruction and Project Completion

Project Review & Judging

Phase 2: Intense Interview Prep & Career Connections

Project/Homework Deadlines

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Data Visualization

Textbook/Notes

Error Bars and Confidence Intervals

Python: seaborn

bokeh Introduction

Python: bokeh

Adjusting Non-Graphical Elements

Python: bokeh

Saving Figures

Python: bokeh

Adjusting Non-Graphical Elements

Python: seaborn

Static Plots

Python: bokeh

Adding Some Interactivity

Python: bokeh

bokeh Next Steps

Python: bokeh

seaborn Next Steps

Python: seaborn

Data for bokeh

Python: bokeh

Using JavaScript in bokeh

Python: bokeh

plotly Introduction

Python: plotly