Data Science as a Field

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At present, we have been seeing a rapid adoption of data, analytics and artificial intelligence (AI) across many business domains. 

 Why rapid adoption of data , analytics and AI?

  1. A need to make faster, better, and evidence-based decisions.
  2. The availability of large volumes of data known as big data
  3. Advances in computing power including cloud and edge computing
  4. Novel machine learning algorithms.

There are many more creative definitions of big data. Some describe it in terms of “four Vs” while others use just three.

IBM’s Four Vs of big data

  • Volume: The amount of data being produced and the amount being captured.
  • Variety: The number of different forms of big data being captured.
  • Velocity: How quickly the data is moving (the data in motion).
  • Veracity: Associated with uncertainty. How you can make decisions when you don't know how accurate the data is

Skills of Successful Data Scientists
  • Business and domain expertise
  • Expertise in machine learning algorithms
  • Expertise in software engineering
  • Expertise in data storage (both traditional relational databases and noSQL databases) and data engineering
  • Visual presentation and communications expertise

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