Unpacking Data… For Dummies

9th March 2020

Amy Murgatroyd

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Technology is an ever evolving and growing industry, with driverless cars being made, it’s got to the point where our imaginations are now becoming materialised through the help of Artificial Intelligence, but how?  We know how the main elements of AI come together and what role they have to play in the creation of these technological masterpieces.

The Breakdown:

Below is a diagram of Artificial Intelligence. You can see that Data Science and Machine Learning are both correlated, whilst also being part of Artificial Intelligence.


What about all of these buzzwords? What do they mean?

Big Data: Enormous amounts of data that can be processed via Data Science to understand patterns and human behaviour.

Data Science: With the masses of data that we need to provide machines with, we need to extract some insights from it. This is where Data Science comes in as it is the process in which big data is analysed and pieces of information are extracted through use of algorithms and processes.

Machine Learning (ML): The clue is in the name, the process enables machines to learn through experience, but ML is made up of algorithms (reinforced, supervised and unsupervised).

Artificial Intelligence (AI): Just like humans, we can essentially enable machines to make decisions and perform tasks based on experience (data) that we provide the machine with.

Artificial Neural Networks (Deep Learning): This is the part of AI that learns and makes decisions on its own.

Who uses Data Science and Machine Learning?

Big names such as Netflix, Amazon, and Facebook amongst many others have jumped on the Data Science bandwagon. With big names like this getting into Data Science, surely there’s some serious demand for Data Scientists? You’d be right to think so. According to LinkedIn’s US report, the demand for Data Scientists has grown annually by 37%! In order to become a Data Scientist and get the chance to work on some really exciting projects, all you need to do is get skilled up on the below:

  • Python
  • SAS and R Languages
  • Big data Platforms
  • Cloud tools
  • SQL databases
  • Statistics/Mathematical
  • Java, C/C++, Perl


Some of the big names making good use of ML are Google, Microsoft, and Twitter (to name a few). To become a Machine Learning Engineer, the skills that you’d need to become accustomed to are the following:

  • Understand Computer Science and programming
  • Statistics
  • Data Modeling and Evaluation
  • Understand ML algorithms and libraries such as TensorFlow and Spark
  • Deliver software engineering and system design


Data and Artificial Intelligence are highly complex concepts to get your head around, so hopefully our little data breakdown has enabled you to digest this information a little better. If you didn’t already know, here at Strategic People we specialise in the AI , Machine Learning, Data Scientists , Data Engineers markets in California and the Netherlands. Our clients range from start-ups to major corporations having supplied them both contract and permanent staffing solutions. We operate only within our niche vertical and over the years have build up an exceptional talent pool within this niche. With our focus solely being in this market place within California and the Netherlands, we can provide industry insights and an exceptional talent pool which benefits our clients.

For a chat about our work in the data space, get in contact with the details below.

We look forward to hearing from you! SP//

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