Comments for Introduction to Data Science, Columbia University

Comment on About the Course by Marco Shaw

Marco Shaw — Tue, 05 Mar 2013 01:39:51 +0000

August 2013 – http://shop.oreilly.com/product/0636920028529.do

Comment on Kaggle Competition Final Results! by jesse

jesse — Fri, 01 Mar 2013 17:38:28 +0000

Re: “there is some systemic problem preventing them (us) from doing so.”

I learned in my games psychology class that this sort of game does not appeal to everyone. It is the sort of highly competitive game that would appeal to young men more than any other demographic group. You have to be careful about designing a game with a prominent leader board since it discourages many players from taking part if they are not already confident in their skills and they are not highly competitive. If you wanted more participation by a broader demographic some things you could do are: make localized leader boards (like for classes) and add more cooperative elements focusing on what the group(s) as a whole can achieve. So that’s my two cents: it’s the game mechanics that doesn’t appeal to other people as much as it does young men, not the subject matter.

Comment on Next-Gen Data Scientists by Rachel Schutt speaks at Strata tomorrow about Next-Gen data science | mathbabe

Tue, 26 Feb 2013 11:27:59 +0000

[...] wrote a blog for the class and had a great post about being a next-gen data scientist. She has high hopes for the students in the class and wrote an aspirational list for them. It [...]

Comment on About the Course by Andrew

Andrew — Sun, 24 Feb 2013 17:14:16 +0000

Ditto for Coursera. I would like to take this.

Comment on Philosophy of Data Science: Embrace the Practical and the Profound by Vinay Ganti

Vinay Ganti — Thu, 14 Feb 2013 14:00:51 +0000

Great final post. I would love to chat with you further regarding your ideas on the future of data science curricula and pedagogy. I want to get a sense of how formalized you feel a field like this can become. Do you see a degree as a possibility here, kind of how business has become so professionalized via an MBA?

Do you have time to meet for coffee or a phone call?

Comment on Exploratory Data Analysis by Rachel Schutt

Rachel Schutt — Mon, 11 Feb 2013 16:54:57 +0000

I agree that EDA includes visualization (with a lower case “v”) to explore the data using graphical techniques. I was trying to distinguish between statistical graphics for exploratory purposes vs explanatory (to use your terms). Exploratory Data Analysis as a discipline or area started by John Tukey referred to statistical graphics used to understand the data yourself. Data Visualization tends, at least now in the vernacular, to refer to presentation and communication. I’m not trying to split hairs about the terminology, but rather trying to distinguish between these two distinct purposes for visual displays of information. The distinction is important because it lends itself to different methods, processes and choices.

Comment on 10 Important Data Science Ideas by Kaggle Digit Recognizer: A feature extraction #fail at Mark Needham

Kaggle Digit Recognizer: A feature extraction #fail at Mark Needham — Thu, 31 Jan 2013 23:26:13 +0000

[...] try and persuade me that we should try it out but it wasn’t until I was flicking through the notes from the Columbia Data Science class that it struck [...]

Comment on About the Course by Data Science 101: Training Undergrads to Be Curious Problem-Solvers First, Programmers Later | Data Informed

Wed, 30 Jan 2013 15:13:18 +0000

[...] Columbia, “Introduction to Data Science” was taught by Rachel Schutt, a senior statistician at Google’s research division in New York. [...]

Comment on Philosophy of Data Science: Embrace the Practical and the Profound by Anthony Power

Anthony Power — Sun, 20 Jan 2013 23:12:22 +0000

Thanx for sharing not only the practical, but the thought processes and evolution behind the idea.

cheers
anthony

Comment on 10 Important Data Science Ideas by Kaggle Digit Recognizer: Finding pixels with no variance using R at Mark Needham

Kaggle Digit Recognizer: Finding pixels with no variance using R at Mark Needham — Tue, 08 Jan 2013 00:49:29 +0000

[...] There was quite a nice quote from a post written by Rachel Schutt about the Columbia Data Science course which summed up the mistake we’d made: [...]