Data science is not a subset of artificial intelligence. The two technologies share similarities and differences, but one can’t be called a subset of the other subject. Both of these subjects belong to the information technology industry, but their functionalities, goals, and use cases differ.

To properly understand the similarities and differences between data science and artificial intelligence, let’s take a closer look at these two subjects.

What is Artificial Intelligence?

As the name suggests, Artificial intelligence deals with creating computers and machines that can think in similar ways to the human brain and use that knowledge to make decisions. Machines or robots are usually built for a singular task that they can do well. With the advent of AI technology, more and more companies wanted to create artificial intelligence that can interact with human beings, understand their language and requirements, and fulfill a multitude of functionalities by the process of thinking themselves.

Some of the day-to-day examples of artificial intelligence include:

• Recommendation engines: When you are using any streaming service, such as Spotify or Netflix, you will see that these platforms provide recommendations for what you should watch or listen to next based on your previous streaming data. It is done for every user on the platform and is possible/powered by an AI-driven recommendation engine. This engine collects your data, matches it up against its database, finds more suitable recommendations according to your choices, and presents new offers to you regularly.

• Intelligent Assistants: The best example of artificial intelligence is intelligent assistants available on your mobile phones and desktops that help you accomplish many tasks. Personal assistants such as Siri, Cortana, Alexa, and Google Home pick up on your voice commands and can perform many tasks just from your voice input.

What is Data Science?

Data science is a field of interdisciplinary study used to process large amounts of structured and unstructured data to figure out their trends and then use those trends to solve real-life problems for various industries. The applications of data science are not limited. It can be used in almost every industry by using their specific data and finding out ways to optimize the business to get an edge over the competitors in the industry. 

The working process of data science starts with a question: “how can you optimise your business further?” The scientists gather large amounts of data in every format relevant to the question. Then the data is converted into metrics and statistics that everyone can understand. The final statistics are presented to the business owners, who can monitor the data and find out how they can optimize their business further, the areas where they can improve most, and how they can improve in those areas. 

Data science is a potent tool; some of the best success stories of data science are:

• One of the best success stories of data science is how data science was used by Alaska Airlines to save 480 thousand gallons of fuel and reduce 4600 tons of carbon emissions from being exposed to the environment.

• The car manufacturer brand Nissan used data science to study their spending on marketing and how they can improve their marketing return on investment (ROI). They also used big data to understand the demand for their products in various parts of the world. They used it to decide on the pricing of their models at multiple locations and how they could improve their brand and use it to market and capture the customer base of a new generation.

Differences Between Artificial Intelligence & Data Science

These fields involve computer science, technology, mathematics, and statistics; they might seem similar at face value. Still, the more you delve into each subject, you will realize that they are not identical except for the face value. The advanced functionalities of both of these technologies are vastly different from one another.

Data science is a broad term that generally translates to using data in large formats and crunching it down to create understandable statistics that can be used to optimize business. In comparison, artificial intelligence can be seen as computer programs that use statistics and data to automate specific problems.

Final Thoughts

As the two fields are different, the education of these two fields also differs. You will not be able to go for a career in data science with a dedicated education in artificial intelligence and vice versa. So, you need to choose the right engineering college to understand these two fields properly and weigh your options before joining any particular course. When choosing your college, feel free to check out JIET DAT, one of the best colleges in Rajasthan and India, to pursue your AI and data science education. For more information about the courses, please check out the college website.