You can crunch numbers all day, but your amazing insights will be lost unless you can communicate them effectively. Whether you are generating charts for your boss, or creating sophisticated dashboards for a large organization, this course will give you the practical information you need to achieve success.
What am I going to get from this course?
Create and effectively utilize data visualizations, know what to expect from data visualizations, and work with data science teams to get the visualizations they need. After this course, managers will be better equipped to plan and execute visualization projects and use data visualizations to improve their work.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
There are no prerequisites for this course, although a basic knowledge of Microsoft Excel is helpful, and a basic background in statistics can also be applied.
Who should take this course? Who should not?
This course is designed for anyone who needs to read and understand data visualizations, as well as anyone who is tasked with presenting data visualizations, either on a large or small scale. While the course covers many of the practical nuts-and-bolts aspects of visualizing data, it is not for software engineers who develop visualizations.
Module 1: Course Overview and Objectives
Course Overview and Objectives
This brief introduction gives and overview of the course, the intended audience, and the learning objectives. We will review the course modules and the theme or thesis of the course. There are no prerequisites for this course.
Learn how to gather data from disparate sources, clean and normalize data, and deal with data from legacy systems.
Review the concepts discussed in the lecture about gathering data.
Module 2: Turning Data into Information
Turning Data into Information
Learn how to get insights from your data (and how to be sure that insights are actually insights), and visualize them clearly. You will also learn how to determine the potential of your dataset and identify its weaknesses, and how to identify the key questions you want to answer (KPIs).
Module 3: Getting the Big Picture
Getting the Big Picture
Learn about the different data relationships you will be visualizing and the best choice of chart/graph options for each. You will also learn about the limitations of visualizations (sometimes tables are a better choice), and design tips for making your visualizations successful.
Reflection: Consider a recent visualization
Consider a recent data visualization you created, or perhaps on that was presented to you. What would you do to improve it? Print it out if possible, and mark it with pen or pencil to indicate the changes you would make. Also indicate where it was successful. If possible, re-create the visualization with your changes and compare them.
Make specific reference to the points covered in the lecture:
1. Does the visualization have a clear focus?
2. Does it direct the viewer's attention to one particular point?
3. Is the point of the visualization clear?
4. Is the visualization free of clutter?
5. Is the right amount of information presented?
6. Is the design simple, not distracting, and free of clutter?
Module 4: Getting the Truth out of Your Data
Getting the Truth out of Your Data
Use data science techniques to ensure that your data and your visualizations are not misleading or communicating the wrong information.
Module 5: Pitfalls in Data Visualization
Pitfalls in Data Visualization
Learn about how data visualizations can lie, or mislead unintentionally. Learn best practices for avoiding common pitfalls and problems in the visual presentation of you data.
Module 6: Challenges in Data Visualization
Challenges in Data Visualization
Learn how to work with big data, how to communicate your insights effectively, how to plan and manage data visualization projects, and how to make sure your visualizations get out to your audience so they can be effectively utilized.
Challenges in Data Visualization
Review of the key points in the lecture on the challenges of data visualization
Module 7: What to Look for in a Dashboard
What to Look for in a Dashboard
Learn about the dashboard and its potential for communication, both to small teams and large organizations. Learn what makes a dashboard successful, and how to measure the success of your dashboard development project.
Reflection: What are your KPIs?
It's very important at the outset of your data visualization project, and throughout its life, to define the metrics that are most important to your audience. In this reflection, define your KPI's. You should have no more than six. They should be specific to an audience, for example, your team, or the entire company. (If you have more than one audience in your project, pick one).
Make sure that the KPIs you choose meet these criteria:
1. It's quantifiable
2. You can measure it (you have the technical means and the business processes in place)
3. It's key (not just cool, or something that you have easily accessible)
4. It's actionable for your audience
5. It directs individuals in your audience to an action or a goal
Module 8: Data Visualization Tools
Data Visualization Tools
We will review a number of different data visualization and business intelligence (BI) software packages and platforms. You will also learn how to define your requirements for choosing the right package for you and your organization, considering the features, budget, licensing structure, and security requirements.