101: Why Data Science?

What is Data Science?

  • Software Engineering, Maths, Automation, Data

  • A.k.a: Machine Learning, AI, Big Data, etc.

  • It’s current rise in popularity is due to more data and more computing power.

For more information: https://winderresearch.com/what-is-data-science/

Examples

US Supermarket Giants

  • Target: Optimising Marketing using customer spending data.

  • Walmart: Predicting demand ahead of a natural disaster.

Discovery

  • Most projects are “Discovery Projects”.

  • Primary Business goals: Increase Revenue, save costs, save time.

  • Budgets can come from other parts of the business.

Automation

  • The automation of tasks is a wider trend within industry.

  • Software Engineering is the automation of processes.

  • Data Science is the automation of decisions.

  • Data Science offloads the burden of a decision to an automated process.

Data Science is an Asset

Good Data => Good Data Science => Good Decisions

More examples

  • Signet Bank

  • Amazon, Google, Facebook, et. al.

  • Caesar’s Entertainment.

The Job Market

Global Engineering shortage.

In 2017 the UK engineering sector requires 100,000 graduate-level engineers per year. 40k are UK nationals. 40k are foreign nationals. 20k deficit.

The state of engineering, Engineering UK, 2017

  • Hampering the UK’s presence in global engineering
  • Brexit?
  • Hard to get more detailed numbers.

Data science:

  • UK: “Rare as unicorns” - Guardian
  • US: 100,000 shortage - Gartner
  • US: 140,000-190,000 shortage - McKinsey
  • US: 181,000 needed by 2018 (IDC)

Big numbers, but take with a pinch of salt.

  • UK Median salary: £65k, vs. £45k for all of IT Source

More articles

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201: Basics and Terminology

Now we have a firm understanding of how business problems map to solutions we need to learn the techniques to deliver the solutions. This section introduces the basic terminology and concepts used in data science.

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