In this section, five students who took CMS.631 Data Storytelling Studio in either Spring 2017 or Spring 2016 offer advice to students taking the course in the future.
Almaha Almalki, Graduate Student, Collective Learning Group | MIT Media Lab
I would advise students taking this course in the future to be as creative as possible, because by doing the unthinkable, your way of thinking will grow.
Anonymous, Graduate Student, MIT Graduate Program in Science Writing
My first piece of advice would be to make sure you ALWAYS have someone in your group who is good with computers and, ideally, can do some coding. This can make data analysis a whole lot easier because they can just write a script to make the computer do something rather than your group having to do it by hand. And if you are a techy/computer person, seek out others in fields that are more humanities-oriented. In a few cases, I found the computer-savvy students overlooked things that were really obvious to me as a student studying journalism/science communication. So, basically, make sure your groups are somewhat diverse. Finally, don’t be afraid to ask for advice from Rahul Bhargava—he’s super friendly, approachable, and helpful.
Katherine Marlowe, Undergraduate, Computer Science | MIT Department of Electrical Engineering and Computer Science
My biggest takeaway from the class was the importance of having a “call of action” in each of my projects. I learned that while it was important for viewers to engage with the project or campaign, their post-engagement could be even more important. I think that is an important focus area for future students taking the course and for educators facilitating a similar course.
Kendra Pierre-Louis, Graduate Student, MIT Graduate Program in Science Writing
Make sure your groups are diverse. Don’t just pair with people you know. Experiment.
Felipe Lozano-Landinez, Undergraduate, Technology for Social Systems | MIT Department of Mechanical Engineering
Invest in knowing the people in the class; good groups lead to good projects. Don’t be afraid to take chances and fail; there is no right answer, experiment! But whatever you do, do think critically and rigorously about it; any truly good data story has 10 layers underneath it.