This section introduces Data Science. It explains what it is and why we need it. We discuss some of the reasons for doing Data Science and provides famous examples from around the world.
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/
US Supermarket Giants
Target: Optimising Marketing using customer spending data.
Walmart: Predicting demand ahead of a natural disaster.
Most projects are “Discovery Projects”.
Primary Business goals: Increase Revenue, save costs, save time.
Budgets can come from other parts of the business.
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.
Good Data => Good Data Science => Good Decisions
Amazon, Google, Facebook, et. al.
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.
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.