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    BIG DATA AND DATA SCIENCE

    Now that we know what is Big Data and data science, let us see how is related to the Big Data. 

    Data that scale to Big Data are of particular interest in data science, although the discipline is not generally considered to be restricted to such data. Data science actually employs techniques and theories drawn from many fields such as nanotechnologies, physics, robotics, mathematics, statistics, information theory and information technology.

    DATA SCIENCE WITHOUT BIG DATA

    According to Lynda.com’s Techniques and Concepts of Big Data with Barton Poulson, the three facets of Data science (Coding, Statistics and Domain Knowledge) apply even when the three Vs of Big Data (Volume, Velocity and Variety) are not present at the same time:

    • Only Volume – When lot of static data is there.
    • Only Velocity – When streaming data comes in and only a small window is analyzed at a time.
    • Only Variety – Static but complex data like face recognition, data visualization etc.

    BIG DATA WITHOUT DATA SCIENCE

    Lynda.com’s Techniques and Concepts of Big Data with Barton Poulson say about below valid cases:

    1. Big Data with only Coding and Statistics
      • This is where Machine Learning fits
      • E.g. spam filter, facial recognition
    2. Big Data with Coding and Domain Knowledge
      • E.g. Word Count, Natural Language Processing.

    However,

    1. Big Data with only Statistics and Domain Knowledge (without no knowledge at all of coding) is not possible.
    2. Big Data is also not possible with only one of Coding, Statistics and Domain Knowledge.

    REFERENCES: 

    1. https://en.wikipedia.org/wiki/Data_science
    2. https://www.facebook.com/dan.ariely/posts/904383595868
    3. https://en.wikipedia.org/wiki/Machine_learning
    4. Lynda.com’s Techniques and Concepts of Big Data with Barton Poulson
    5. http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
    6. Analyzing the Analyzers -An Introspective Survey of Data Scientists and Their Work by Harlan Harris, Sean Murphy, Marck Vaisman.

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