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BIG data: Issues and debates  

Resources related to data, for aspiring data scientists, and for participants in the Fall 2016 Common Reading Program at AUC
Last Updated: Mar 7, 2017 URL: Print Guide RSS Updates

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What is Data? What is Big Data?


The word 'data' derives from Latin.  It is often taken to mean "a thing known (or assumed) to be a fact, based on direct observation". (Think about this... deeply)

There are many different classes of data types. Within the context of this guide, we will mostly be considering quantitative, machine-readable data. 

You may be most familiar with the following:

  • numerical data (e.g. prices, measurements, coordinates)
  • boolean (logical) (i.e. "TRUE" or "FALSE")


This term is sometimes used vaguely. Most often, it means:

  • extremely large collections of data -- so large they they can only be interpreted computationally (see: Pence, "What is Big Data?")
  • analytical methods employed to interpret large data sets
  • approaches to decision-making, which are informed by analyses of large data sets

Big Data as seen by IBM -- this site presents some key aspects of big data very clearly. 




Data Management



Parts of this guide were adapted from the excellent guide ( ) by 

Jennie Murack, Geospatial Data Librarian and Statistics Specialist at  MIT Libraries, whose kind permission I herewith gratefully acknowledge.



This guide contains information for the 2016-17 Common Reading Program at AUC: 

Big Data – What is Your Digital Footprint? (The readings are linked on the right.)

In order to engage in a conversation, we have to agree on the words we use. So ....

  • Look at the definitions of "data" and "Big Data" in the box on the left
  • Take this quick tutorial and quiz to learn about QUANTitative and QUALItative data. 
  • Watch the short  video below to learn what "data science" is about
  • Consider some basic ethical issues related to Big Data analysis (video: BBC / summary:  IBM / essay: NIH)

... then, explore the tabs above to learn more. Enjoy the program!


In a nutshell...


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