Topic 3: What does it take to be a data scientist?

The myriads of data science applications across sectors and disciplines hints the number of skills needed to be developed, in order to deliver more accurate and useful outputs.

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  • As seen in the graph, statistics and math are required to create models for the homogenisation real world observations.
  • Computer science enables the processing of large amounts of data.
  • Business related skills are beneficial for translating research to practical goals of a company.
  • Communication and visualization makes it easier to explain findings and patterns.
  • Finally, domain expertise is required for specific data science applications.

Source: ​​Corea, F. (2019). An Introduction to Data. Switzerland: Springer Publications. IBM Cloud Team (2021), ‘Python vs. R: What’s the Difference?’, IBM, available at: https://www.ibm.com/blog/python-vs-r/ibm.com/blog/python-vs-r/