DATA VISUALIZATION

Get introduced to the major pillars of successful data visualization.

• Introducing data visualization
• Why this skill?
• Major frameworks
• Major tools

Assignment #01: Explore the provided dataset for Divvy. What stories do you anticipate unlocking here?

Learn how to create a planning guide for yourself and other analysts before starting a visualization.

• Building a successful data story
• Minto Pyramid Principle
• In-class exercise: Planning a data story for Divvy

Project #01: Use the provided dataset to help Divvy rebalance their bike system, or tackle your own business challenge. Your deliverable will be a Minto-style plan for your data story.

Get familiar with software tools used to analyze data for patterns indicating the presence of a story.

• Preparing data for analysis with Excel, SQL, and R/Python
• Finding patterns in data
• Converting plans to story outlines
• In-class exercise: Interpreting patterns in Divvy visuals

Kevin explains how software tools R and Python are used to reveal and visualize sophisticated patterns in complex data.

• Understanding data visualization in R
• Understanding data visualization in Python
• Advanced data visualization techniques
• In-class exercise: Interpreting patterns in advanced Divvy visuals

Assignment #03: Produce a data visualization based on a provided Divvy dataset (or, use data from your own company) using R or Python.

Use software tools (including SQL, Google Sheets, Tableau, and R) to gather and analyze data from the Divvy database. You will first perform exploratory data analysis to look for patterns in Divvy data that indicate the presence of a story. Then, you will use visuals to inform their interpretation of the Key Questions in a plan. Convert your plan into a solid outline for a data story.

Project #02: Refer to the Divvy case study and database to analyze data for patterns indicating the presence of a story. Use the tools demoed in class to create exploratory analysis charts.

Understand a framework for evaluating dataviz and the first pillar of that framework: the sophisticated use of contrast.

• Brain science & visual interpretation basics
• The components of good visual form
• Understanding contrast in dataviz
• In-class exercise: Improving the use of contrast in data visualizations

Understand the second pillar of the framework for evaluating dataviz, clear meaning, and learn how to spot and counter bad practices that lead to the manipulation of a chart’s meaning.

• What makes clear meaning in dataviz
• Spotting bad dataviz: Biases and manipulations
• In-class exercise: Improving unclear data visualizations

Understand the third and final pillar of the framework for evaluating dataviz, refined execution.

• Refined execution in dataviz
• Why prioritize?
• In-class exercise: Improving refined execution in data visualizations
• In-class exercise: Improving all elements of good dataviz

Assignment #04: Evaluate a provided Divvy dataviz (or, alternatively, a dataviz from work at your own company) using the framework introduced during lecture. Offer recommendations for how the dataviz can be improved.

This lab focuses on converting the efficiently generated, yet visually lacking, visuals produced during the Exploratory Data Analysis lab into “client-ready” visualizations that effortlessly communicate insight.

You will be challenged to demonstrate a sophisticated use of contrast to attract the audience’s attention to important patterns in the data. Use this technique to create a clear expression of meaning through the effective use of common chart elements, and a level of refined execution that minimizes distractions.

Then, generate visuals using tools including Excel, R, Tableau, and/or Power BI. A presentation software, such as Google Slides, PowerPoint, or Keynote, will serve as the canvas for your output.

Project #03: Client-ready Dataviz
Start working on your presentation for the Divvy executive team. Use the provided data set to create a series of client-ready charts from provided Divvy data (or, alternatively, data from your own company).

Learn the skills needed to effectively present dataviz and persuade stakeholders at every level of the organization.

• The ‘McCandless Method’ of data storytelling
• Stakeholders needs & methods of persuasion
• Ensuring a successful presentation
• In-class exercise: Writing a ‘McCandless Method’-style narrative for a dataviz

Project #04: Write a McCandless Method-style narrative for one of the dataviz you've created for the Divvy executive team (or, alternatively, a dataviz you created from your own company’s data). Record yourself presenting the narrative and submit to Kevin for feedback.

Project #05: The Divvy executive team believes it's important to stay on top of the patterns and trends you've found in the data. Bring together all the work you've done so far to create a dashboard for Divvy built in Tableau, Power BI, or another dashboarding tool of your choice.

It's your time to shine! This is part one of presenting your data analysis work and receiving invaluable feedback from our expert instructor. Plus, engage in a Q&A session to deepen your insights.

• Project showcase
• Instructor feedback
• Q&A