Here is a good overview of the first week of the Columbia Data Science course.


I’m attending Rachel Schutt’s Columbia University Data Science course on Wednesdays this semester and I’m planning to blog the class. Here’s what happened yesterday at the first meeting.


Rachel started by going through the syllabus. Here were her main points:

  • The prerequisites for this class are: linear algebra, basic statistics, and some programming.
  • The goals of this class are: to learn what data scientists do. and to learn to do some of those things.
  • Rachel will teach for a couple weeks, then we will have guest lectures.
  • The profiles of those speakers vary considerably, as do their backgrounds. Yet they are all data scientists.
  • We will be resourceful with readings: part of being a data scientist is realizing lots of stuff isn’t written down yet.
  • There will be 6-10 homework assignments, due every two weeks or so.
  • The final project will be an internal Kaggle competition. This will be a…

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