Ask DT: Non-technical introductions to data science for students?
116 days ago
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115 days ago
Anyone working in public policy is going to benefit massively by investing the time to learn some technical skills. Foremost among them is probably statistics, and fortunately this is helpful for data science too. Time spent doing the basics in R or Python will not be wasted and there are many, many great courses a google search away. R might be better to start on than Python because it is free, but similar to Stata which is common in university and research settings. But Python is also great because: https://xkcd.com/353/ Next, they may need to brush up on their data visualization skills. There is plenty of art left in this science, and some of my favorite reads on this include anything by Edward Tufte: https://www.amazon.com/Edward-R.-Tufte/e/B000APET3Y/ref=dp_byline_cont_book_1 For practical guidelines to making simple and clear presentations, I also highly recommend: https://www.amazon.com/Street-Journal-Guide-Information-Graphics/dp/0393347281 One other thought, is that public policy often involves maps. A good use of summer downtime might be to get familiar with GIS systems, the leader being ESRI and their ecosystem of tools. Start here and learn the basics of geospatial analytics: https://learn.arcgis.com/en/ If they're interested in machine learning or specifically deep learning, there's plenty of non-technical content out on blogs, but doing the time in linear algebra and some calculus is going to pay dividends. That may seem daunting, but a fun book I recently purchased makes linear algebra accessible in comic form - not a bad way to start: https://www.amazon.com/Manga-Guide-Linear-Algebra/dp/1593274130 The sooner your nontechnical friend embraces a little bit of technical know-how, the better off she will be. There's plenty of room for the humanities in data and data in the humanities! Enjoy!
116 days ago
I have a younger nontechnical friend of the family who has recently graduated with a degree in public policy, but her coursework was light on math and she expressed an interest in learning more about data science over the summer before she heads off to grad school next year. So I'm looking for resources that could be considered light and digestible enough for a nontechnical, somewhat burnt-out humanities major, but with enough concrete information and techniques to potentially be of practical value for a potential future decision maker. My Google quest so far ended up with Nate Silver's Signal and the Noise, and I was considering something like that Harvard Kennedy School course that was on HN recently: https://opportunityinsights.org/ but having no real experience with these resources and having just gotten wind of this site, I would appreciate if there was someone more knowledgeable than me who could offer their perspective.
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