Data Visualization

Asynchronous

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

Checking your registration status...

To access the program content, you must first create an account and member profile and be logged in.

You are registered for this program.

Next Event

NEXT EVENT

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

Click here to be invited to the slack organization: The Erdős Institute

Click here to access the slack cohort channel: #slack-cohort-channel

Click here to access the slack program channel: #slack-program-channel

Click here to download the Events & Deadlines .ics calendar file

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

I'm a paragraph. Click here to add your own text and edit me. It's easy.

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

Making pairplots and jointplots in seaborn.

Slides

Transcript

Code

Project/Homework Instructions

I'm a paragraph. Click here to add your own text and edit me. It's easy.

Project/Team Formation

Project Submission

Projects README

Schedule

Click on any date for more details

Orientation & Setup

Phase 1: Instruction and Project Completion

Project Review & Judging

Phase 2: Intense Interview Prep & Career Connections

Project/Homework Deadlines

THE ERDŐS INSTITUTE

Helping PhDs get and create jobs they love at every stage of their career.

Data Visualization

Textbook/Notes

Patches

Python: matplotlib

Saving a Figure to File

Python: matplotlib

Data for seaborn

Python: seaborn

displots

Python: seaborn

plt.subplots

Python: matplotlib

matplotlib Next Steps

Python: matplotlib

Figure- vs. Axes-level Functions

Python: seaborn

catplots

Python: seaborn

Adjusting Non-Graphical Elements

Python: matplotlib

seaborn Introduction

Python: seaborn

relplots

Python: seaborn

pairplot and jointplot

Python: seaborn