Data Analysis 5: Data Reduction – Computerphile

Too much data? Dr Mike Pound on how best to reduce your dataset. This is part 5 of the Data Analysis Learning Playlist:

This Learning Playlist was designed by Dr Mercedes Torres-Torres & Dr Michael Pound of the University of Nottingham Computer Science Department. Find out more about Computer Science at Nottingham here:

This series was made possible by sponsorship from by Google.

The music dataset can be found here:

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham:

Computerphile is a sister project to Brady Haran’s Numberphile. More at

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19 thoughts on “Data Analysis 5: Data Reduction – Computerphile

  1. You're completely wrong about how Spotify finds recommendations – they don't analyse the music whatsoever (like Pandora did back in the day) they just go on what other people put in their playlists and find people who are similar to you and pick songs from their playlists that aren't in yours.

  2. one viable definition of 'big data' is, when it crashes excel… making you wait an unreasonable amount of time, when processed in R, could be another one.

  3. Amazing series, great topic, great teacher! I'm actually excited for next semester now where I get to do this stuff in class!

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