When I work with companies and executive teams, I often find that there is some confusion about the differences and overlaps between data science, machine learning, and artificial intelligence.

1. How Does Data Science,
Machine Learning,
And AI Overlap?

2. © 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
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Text
IntroductionIntroduction
When I work with companies and executive teams, I often find that there is
some confusion about the differences and overlaps between data science,
machine learning, and artificial intelligence. So, I thought it would be worth
creating a quick and straightforward guide to these three terms, which are
closely related, sometimes used interchangeably, but really convey different
meanings.
How Does Data Science, Machine Learning, And
Artificial Intelligence Overlap?

3. © 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Data Science
Let’s start with “data science” (DS) – because it’s really the foundation of the other
concepts.
You may have heard the phrase “data is the new oil” – or a variation of it. And while I’ve
questioned the validity of that analogy myself, it does a good job of explaining why data
(especially once it is turned into insights and knowledge) is so important in the business
world today.
It actually draws on an older universally-acknowledged truth – that “knowledge is power.”
And just as the medieval thinkers and strategists who coined that phrase found that
educated people tended to acquire respect and authority, today’s business leaders
understand that the more insights they have – about their customers, their competition
and their own operations – the more likely they are to be successful. The field of data
science was developed to help turn data into insights and knowledge. It draws heavily on
a combination of mathematics, statistics and computer science, with the aim of developing
algorithms (rules) for extracting insights and knowledge from data.

4. © 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Data Science
Of course, mathematicians and statisticians had carried out research with the same aim for
centuries already, but it was the addition of computers to the mix that gave birth to the
modern discipline known as data science. Today, data science has become a critical part of
most businesses, and the role of data scientist has become one of the most thought after
and well-paid jobs in organisations.
There are two particular skills in the data scientist’s toolkit that have proven to be hugely
valuable. These are data mining and predictive analytics. Data mining is simply the process
of discovering patterns in data that can be translated into insights whereas predictive
analytics involves using data to determine the statistical likelihood of certain outcomes of
a process or operation.

5. © 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Artificial Intelligence
These are both essential components of Artificial Intelligence (AI) systems – although it’s
worth noting that the meaning of the term “AI” has evolved considerably over the last
century.
AI refers to machines capable of thinking, learning, and acting in the manner similar to
humans.
AI systems can be programmed by humans or can be created and improved using data
with limited or no involvement of humans, which brings us very neatly onto the third term
I wanted to cover in this piece – machine learning (ML).

6. © 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Machine Learning
The easiest way to describe the relationship and overlap between ML and AI is that ML is
one of the current state-of-the-art methods in our attempts to build learning machines.
Whereas AI is a somewhat fluid term used to describe a general concept, ML is an AI
methodology, and therefore a subset of AI.
Conducting ML involves building algorithms capable of being trained on data, rather than
specifically trained by a human (usually a data scientist) on how to carry out a task.
Although the concept isn't new – it was first discussed seriously in the mid-20th-century - it
relies on access to large volumes of data, as well as a great deal of computing power. Both
are needed to train algorithms until they are sufficiently good at their task. And it’s only in
relatively recent years, thanks to the emergence of the internet and the falling cost of
processing hardware, that this has become a viable reality for business.

7. © 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Machine Learning
Hopefully, this SlideShare will give you a basic understanding of the differences – as well
as the relationships – between the concept of DS, AI, and ML. To put it even more simply –
in today's business environment, a data scientist uses machine learning to achieve artificial
intelligence.
Another way to look at it is that ML is just one technique that a data scientist can use to
try to extract insights from information. Others could include building neural networks or
delving into deep learning – which are subjects I cover in posts that you can find in our
content section.

8. © 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2018 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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