Data Science vs Machine Learning and AI | The main Differences

Difference Between Data Science, Machine Learning, and AI

Even though the terms data science, machine learning, and artificial intelligence (AI) fall in the same domain and are connected to each other, they have their specific applications and meaning.

Driving the success of Data Science

We will start with the term Data Science, as it assumes the top-most position in the hierarchy of data-related technologies.

Data Science

Data Science is an interdisciplinary field of systems and processes to extract information from data in many forms. It builds and modifies Artifical Intelligence Softwares to obtain information from huge data clusters and data sets.

Data science covers a wide array of data-oriented technologies including SQL, Python, R, and Hadoop, etc. However, it also makes extensive use of statistical analysis, data visualization, distributed architecture, etc.

Data scientists are exceptionally skilled professionals whose expertise allows them to quickly switch roles at any point in the lifecycle of data science projects. They can work with AI and machine learning both.

Sidetrade, a leading company in the domain of data-science, realized, early-on, the data exploitation challenges its clients faced and immediately set up a dedicated Data Scientist team to work with its Product Managers, aptly put it:

“Data Scientists, of course, have to work closely with IT development teams to guarantee the usability of any solution once it’s in production”

Jean-Cyril Schütterlé VP Product & Data Science, Sidetrade Group

Data Science and AI

Artificial Intelligence represents an action planned feedback of perception.

Perception > Planning > Action > Feedback of Perception

Data Science uses different parts of this pattern or loop to solve specific problems. For instance, in the first step, i.e. Perception, data scientists try to identify patterns with the help of the data. Similarly, in the next step, i.e. planning, there are two aspects:

a) Finding all possible solutions,
b) Finding the best solution among all solutions

It is data science that creates a system for part b above using part a.

Data Science, Machine Learning, and AI

Although it’s possible to explain Machine Learning by taking it as a standalone subject, it can best be understood in the context of its environment, i.e., the system it’s used within.

Simply put, machine learning is the link that connects Data Science and AI.

That is because it’s the process of learning from data over time. So, AI is the tool that helps data science get results and the solutions for specific problems. However, machine learning is what helps in achieving that goal.

A real-life example of this is Google’s Search Engine.

  • Google’s search engine is a product of data science
  • It uses predictive analysis, a system used by Artificial Intelligence, to deliver intelligent results to the users
  • For instance, if a person types “best jackets in NY” on Google’s search engine, then the AI collects this information through machine learning
  • Now, as soon as the person writes these two words in the search tool “best place to buy,” the AI kicks in, and with predictive analysis completes the sentence as “best place to buy jackets in NY” which is the most probable suffix to the query that the user had in mind.

Visual representation of the linkage between AI, Machine Learning, and Data Science

The diagram above is a helpful visual representation of the linkage between AI, Machine Learning and Data Science.

To be precise, Data Science covers AI, which includes machine learning. However, machine learning itself covers another sub-technology, which is deep learning.

Deep Learning is a form of machine learning but differs in the use of neural networks where we stimulate the function of a brain to a certain extent and use a 3D hierarchy in data to identify patterns that are much more useful.

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