Today, we hear about data science, machine learning, and artificial intelligence from everywhere. Sometimes these terms are even used interchangeably.
But it’s not the right way to treat them, and in this post, we’re explaining why. We’re going into all the details about the difference between data science, machine learning, and artificial intelligence. And show how these technologies are interconnected.
But first, let’s have a quick look at what each of them stands for.
Data Science: What Is It Exactly?
The central aspect of data science is getting new results from data. DS is based on strict analytical evidence and works with structured and unstructured data.
In fact, everything connected with data selecting, preparation, and analysis relates to data science.
Data science allows us to find the meaning and required information from large volumes of data. As there are tons of raw data stored in data warehouses, there’s a lot to learn by processing it.
What is it used for?
- Tactical optimization (improving marketing campaigns, business processes)
- Predicted analytics (forecast of demand and events)
- Recommendation systems (like those of Amazon, Netflix)
- Automatic decision-making systems (like face recognition or drones)
- Social research (processing of questionnaires)
For instance, Netflix uses its data mines to look for viewing patterns. This allows staff to understand users’ interests better and make decisions on what Netflix series they should make next.
Companies that rely on data science
Who’s responsible for DS implementation? There’s always a human behind the technology – a data scientist who understands data insights and sees the figures.
Mostly, data scientists should be capable of:
- Understanding of SAS and other analysis tools
- Skills in programming (R, Python, SQL, RapidMiner)
- Ability to process data
- Skills in statistical analysis
But that’s just the tip of the iceberg.
DS specialists may also need expertise in domains like simulations and quality control, computational finance, industrial engineering, and even number theory.
What’s Artificial Intelligence?
The core purpose of artificial intelligence is to impart human intellect to machines.
AI can relate to anything – from apps for playing chess to speech recognition systems. Just like the Amazon Alexa voice assistant, which recognizes speech and answers questions.
Artificial intelligence focuses explicitly on making smart devices that think and act like humans. These devices are being trained to resolve problems and learn in a better way than humans do.
AI application examples include:
- Game-playing algorithms (like Deep Blue)
- Robotics and control theory (motion planning, walking a robot)
- Optimization (like Google Maps creating a route)
- Natural language processing
- Reinforcement learning
One of the best examples of AI appliance is self-driving cars and robots.
And here’s how Amazon uses smart robots. Amazon Prime used to be powered by people whose jobs revolved around getting products from warehouses to customers’ doorsteps.
It’s a predictable algorithm that didn’t change at all. So the company decided to optimize this repetitive and boring job – and hand it over to robots.
Amazon built distribution centers to enable same-day delivery closer to customers’ homes and put robots into these centers.