andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2345 knowledge-graph by maker-knowledge-mining
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Introduction: Rajit Dasgupta writes: I have been working on a website, SlideRule that in its present state, is a catalog of online courses aggregated from over 35 providers. One of the products we are building on top of this is something called Learning Paths, which are essentially a sequence of Online Courses designed to help learners gain mastery over a certain subject. We have recently released a Learning Path on Data Analysis , contributed by Claudia Gold, an early data scientist at Airbnb. We’d love it if you could look at it and tell us what you think. We are always looking for constructive feedback. I clicked through and took a look. It’s pretty cool. I haven’t tried to assess the actual teaching materials (they’re mostly about programming, not statistics) but I like how it’s structured based on pointers to existing resources, which seems like an excellent compromise between (a) someone trying to write the material all himself or herself (which would require either limiting the sco
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1 Rajit Dasgupta writes: I have been working on a website, SlideRule that in its present state, is a catalog of online courses aggregated from over 35 providers. [sent-1, score-0.719]
2 One of the products we are building on top of this is something called Learning Paths, which are essentially a sequence of Online Courses designed to help learners gain mastery over a certain subject. [sent-2, score-1.053]
3 We have recently released a Learning Path on Data Analysis , contributed by Claudia Gold, an early data scientist at Airbnb. [sent-3, score-0.479]
4 The material here is well structured into sections, but each section then has a link. [sent-9, score-0.469]
5 My only suggestion is that this particular learning path be called Data Programming, which seems like a more accurate title than Data Analysis. [sent-10, score-0.826]
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Introduction: Rajit Dasgupta writes: I have been working on a website, SlideRule that in its present state, is a catalog of online courses aggregated from over 35 providers. One of the products we are building on top of this is something called Learning Paths, which are essentially a sequence of Online Courses designed to help learners gain mastery over a certain subject. We have recently released a Learning Path on Data Analysis , contributed by Claudia Gold, an early data scientist at Airbnb. We’d love it if you could look at it and tell us what you think. We are always looking for constructive feedback. I clicked through and took a look. It’s pretty cool. I haven’t tried to assess the actual teaching materials (they’re mostly about programming, not statistics) but I like how it’s structured based on pointers to existing resources, which seems like an excellent compromise between (a) someone trying to write the material all himself or herself (which would require either limiting the sco
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Introduction: Following up on our previous post , Andrew Wilson writes: I agree we are in a really exciting time for statistics and machine learning. There has been a lot of talk lately comparing machine learning with statistics. I am curious whether you think there are many fundamental differences between the fields, or just superficial differences — different popular approximate inference methods, slightly different popular application areas, etc. Is machine learning a subset of statistics? In the paper we discuss how we think machine learning is fundamentally about pattern discovery, and ultimately, fully automating the learning and decision making process. In other words, whatever a human does when he or she uses tools to analyze data, can be written down algorithmically and automated on a computer. I am not sure if the ambitions are similar in statistics — and I don’t have any conventional statistics background, which makes it harder to tell. I think it’s an interesting discussion.
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Introduction: After reading Rachel and Cathy’s book , I wrote that “Statistics is the least important part of data science . . . I think it would be fair to consider statistics as a subset of data science. . . . it’s not the most important part of data science, or even close.” But then I received “Data Science for Business,” by Foster Provost and Tom Fawcett, in the mail. I might not have opened the book at all (as I’m hardly in the target audience) but for seeing a blurb by Chris Volinsky, a statistician whom I respect a lot. So I flipped through the book and it indeed looked pretty good. It moves slowly but that’s appropriate for an intro book. But what surprised me, given the book’s title and our recent discussion on the nature of data science, was that the book was 100% statistics! It had some math (for example, definitions of various distance measures), some simple algebra, some conceptual graphs such as ROC curve, some tables and graphs of low-dimensional data summaries—but almost
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Introduction: Rajit Dasgupta writes: I have been working on a website, SlideRule that in its present state, is a catalog of online courses aggregated from over 35 providers. One of the products we are building on top of this is something called Learning Paths, which are essentially a sequence of Online Courses designed to help learners gain mastery over a certain subject. We have recently released a Learning Path on Data Analysis , contributed by Claudia Gold, an early data scientist at Airbnb. We’d love it if you could look at it and tell us what you think. We are always looking for constructive feedback. I clicked through and took a look. It’s pretty cool. I haven’t tried to assess the actual teaching materials (they’re mostly about programming, not statistics) but I like how it’s structured based on pointers to existing resources, which seems like an excellent compromise between (a) someone trying to write the material all himself or herself (which would require either limiting the sco
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Introduction: A student in my intro class came by the other day with a lot of questions. It soon became clear that he was confused about a lot of things, going back several weeks in the course. What this means is that we did not do a good job of monitoring his performance earlier during the semester. But the question now is: what do do next? I’ll sign the drop form any time during the semester, but he didn’t want to drop the class (the usual scheduling issues). And he doesn’t want to get a C or a D. He’s in big trouble and at this point is basically rolling the dice that he’ll do well enough on the final to eke out a B in the course. (Yes, he goes to section meetings and office hours, and he even tried hiring a tutor. But it’s tough–if you’ve already been going to class and still don’t know what’s going on, it’s not so easy to pull yourself out of the hole, even if you have a big pile of practice problems ahead of you.) What we really need for this student, and others like him, is a road
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