- List the five threads your team should focus on when spinning a yarn.
- Explain the benefits of using scenarios as story helpers.
- Explore how using conflict in a story can captivate your audience.
- Determine how details will enhance a story and make it memorable.
- Recall the downfalls of using too many visualizations.
- Define vision in the context of data-science storytelling.
- Recognize the features of good storytelling.
Skill Level Beginner
- Author Terry Tempest Williams once said that storytelling is the oldest form of education. That's why so much of the success of your data science team is tied into how well you tell good stories. It's not enough for your team to understand the data. You'll need to be able to communicate your key insights to everyone else in your organization. If you can't tell a good story about your findings, then you won't have much of an impact on anyone outside of your team. Storytelling is still the best way to educate, motivate, and engage others.
It helps you talk to a broader audience and get much better feedback. I'm Doug Rose and in this course, you'll see how define a story and break down some of the key elements. Then you'll see how to set up your story's context while weaving in a plot and conflict. Often, a good story is about the details that make it memorable. You'll see how to add memory cues and metaphors so that everyone can visualize what you're saying and better connect with your insights.
Finally, a good story will help you reverse engineer mountains of data into the motivations, thoughts, and ideas of thousands or even millions of people. This course is for anyone who's interested in being part of a data science team but isn't necessarily interested in becoming a full time data scientist. Whether you're a long time project manager or a first year developer, this course will help your team better communicate and connect with your audience.
So let's see how we can start telling better data science stories.