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A data science experience with a focus on narrative, in addition to fact. See pricing
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You've completed your data science project. What next?
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- A hands-on experience
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+ Backed by Experience
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- - Start with a topic of your choice. (If you can't decide, we'll help you choose)
- - Work with real-world data from publicly available sources
- - Write your own scripts and algorithms to analyse the data
- - Draw conclusions with practical implications
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With a combined 10+ years of experience in science communication, we have the skills to mentor you through the process of sharing your work in an articulate and accessible way.
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As editors of Snipette magazine and assistant physics professor at Georgia Gwinnett University, we’ve been involved in intensive editing projects with authors from both ends of the spectrum: professional researchers working to communicate their technical work, and high school and college students eager to further their learning process through writing.
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- The larger picture
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+ Stories before Statistics
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- - Learn cutting-edge data science in real-world contexts
- - Develop your data communication skills and become a content creation expert
- - Get your work published on multiple platforms
- - Have a ton of fun along the way!
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There’s no point in science writing if people aren’t interested in reading it, which is why we specialise in adding a humorous touch—infusing technical writing with a dash of literary flair to help explain complex issues.
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Anyone can describe their work to their peers—but reaching the general public is harder. Our program will help you draw parallels with familiar concepts to better explain complicated topics in a short space. You will also work on grounding your findings in everyday experience and answering the question: how is this relevant to the layperson?
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- Share your findings
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+ Learning through Teaching
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- - Get your work published so others can find it
- - Learn how to frame your arguments and present your data
- - Use literary techniques to tell a compelling story
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There’s no better way to understand a field than to explain it to someone who knows nothing—a fact observed by no less than the brilliant physicist Richard Feynman himself.
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Articulating your process work and findings for a lay audience will help you understand your own process better and streamline how things get done. On top of that, making your work accessible will spread awareness beyond your niche and help make you and your work more known to a wider audience.
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Due to the data-science and coding-intensive nature of the course, we are currently only able to take on participants with some knowledge of Python.
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The course will make use of Python-based data analysis software, which we will help you familiarise yourself with. Towards this, we will be screening participants with a quick questionnaire.
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Since this program is intended mainly for the communication aspects of data science, you are expected to have a data science project ready to be presented before applying. The topic, methods, and datasets will be left to your discretion—we come in to help you polish things up and lay it all out. Towards this, we will be screening participants with a quick questionnaire about their projects.
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Once your topic is chosen, we will work together on finding links, resources, and publicly available datasets. Of course, all details and final choices will be up to you.
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Depending on your project, you may also use a Google Colab notebook to document the technical side of your project including data analytics, methods, and visualization.
