In data analytics, data storytelling is communicating the meaning of a dataset with visuals and a narrative that is customized for a particular audience. In data journalism, journalists engage their audience of readers by combining visualizations, narrative, and context into data-driven articles. It turns out that data analysts and data journalists have a
lot in common! As a junior data analyst, you might learn a few things about effective storytelling from data journalism. Read further to explore the role and work of a data journalist in telling a good story. Note: This reading refers to an article published in The New Yorker. Non-subscribers may access several free articles each month. If you already reached your monthly limit on free articles, bookmark the article and come back to this reading later. Ben Wellington, a contributing writer for The New Yorker and a professor at the Pratt Institute, used New York City’s open data portal to track down noise complaints from logged service
requests. He analyzed the data to gain a more quantitative understanding of where the noise was coming from and which neighborhoods were the noisiest. Then, he presented his findings in the Mapping New York's Noisiest Neighborhoods article. First, click the link above to skim the article and familiarize yourself with the data visualizations. Then, join the
bus tour of the data! You will be directed to three visualizations (tour stops) to observe how each visualization helped strengthen the overall storytelling in the article. Earlier in the training, you learned how context is important to understand data. Context is the condition in which something exists or happens. Based on the categorization of noise complaints, the data journalist set the context in the article by
defining what people considered to be noise. In the article, review the combo table and bar chart that categorizes the noise complaints. Evaluate the visualization:
Tour stop 2: analyzing variablesAfter setting the context by identifying the noise categories, the data journalist describes his analysis of the noise data. One interesting analysis is the distribution of noise complaints versus the time of day. In the article, review the stacked area chart for the distribution of noise complaints by hour of the day. Evaluate the visualization:
Tour stop 3: drawing conclusionsAfter describing how the data was analyzed, the data journalist shares which neighborhoods are the noisiest using a variety of visualizations: combo table and bar chart, density map, and neighborhood map. In the article, review the neighborhood map for how close a noisy neighborhood is to a quiet neighborhood. Evaluate the visualization:
End of the tour: being inspiredWe hope you enjoyed your tour of a data journalist’s work! May this inspire your data storytelling to be as engaging as possible. For additional information about effective data storytelling, read these articles:
Speaking to your audience
Spotlighting
QuestionFill in the blank: A data analyst wants to pinpoint the most relevant data derived from their analysis and eliminate the less important details. They use _____ to scan the data and quickly identify the most important insights.
Correct. A data analyst uses spotlighting to pinpoint the most relevant data derived from their analysis and eliminate the less important details. Spotlighting involves scanning the data to quickly identify the most important insights. Understanding data storytellingIdentify the three steps of data storytelling
Data storytelling and visualizationData storytelling means communicating the meaning of a dataset with visuals and a narrative that are customized for a particular audience. For example, some music-streaming companies send their customers a “year in review” email. In these emails, they tell their customers which artists and songs they were a top fan of. This way, the companies use their customers’ data to tell a story. Data visualization is the representation and presentation of data to help with understanding. You can use graphs, charts, word clouds, and other visual depictions to help your audience see and clearly understand your data. The effects of data storytelling and data visualization can be powerful. Data storytelling and data visualization can captivate your audience, make stories memorable, touch people’s hearts, and inspire people to take action. Test your knowledge on data-driven storiesTOTAL POINTS 3 Question 1Data storytelling involves which of the following elements? Select all that apply.
Correct. Data storytelling involves communicating the meaning of a dataset with visuals and using a narrative that is customized to your audience. Question 2A data analyst presents their data story to an audience. They aim to capture and hold the audience members’ interest and attention. Which data storytelling concept does this describe?
Correct. Engagement involves capturing and holding the audience members’ interest and attention. Question 3Which of the following activities would a data analyst do while spotlighting? Select all that apply.
Correct. Spotlighting involves scanning through data to quickly identify the most important insights. This can be done with notes on a whiteboard, by searching for broad ideas, and by identifying concepts that arise repeatedly. 🗂️ Page Index for this GitHub WikiWhich of the following activities would a data analyst do while spotlighting select all that apply 1 point?Spotlighting enables data analysts to identify broad, universal ideas and messages. This may involve identifying connections or patterns, finding ideas or concepts that keep arising, or noticing repeated words or numbers.
What activities are involved in spotlighting?SpotLIGHTing Techniques. Use of a high powered flashlight; 250 lumens or greater or a 100 LED Flashlight works well (do NOT shine the light into student's eyes). When a toy, target or other interesting visual item is presented to the student, shine the light onto the object; reflective or mylar targets work well.. What is spotlighting in data analytics?Spotlighting is essentially a scanning technique that enables you to look through data quickly and screen them so that you can quickly develop a shortlist of ideas that trigger further thoughts, debate and discussion around a data 'hot-spot'.
Which of the following questions do data analyst ask to make sure they will engage their audience select all that apply?Solution. To engage their audience, data analysts ask about what roles the people in the audience play, their stake in the project, and what they hope to do with the data insights.
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