In today’s decade, Artificial Intelligence (AI) is used widely. Virtually all commercial industries have profited from AI advancements, but there is just as much (if not more!) hype surrounding AI as there is true AI. AI, data science, machine learning, deep learning, and more terms. They are used in the middle of this, which further confuses things.
In this article, I have provided insight into two of these terms—Data Science and AI—and discussed how they relate to one another. Let’s start with their formal definition first.
Artificial Intelligence(AI)
Artificial intelligence refers to any technique where a computer program tries to do things that naturally occur to the human brain. People regularly show intelligence by reading written language, hearing speech, recognizing objects in photos, and planning activities to maximize their free time. Our brains automatically pick up the majority as we grow and interact with the world, and they are then developed and advanced through formal schooling.
Humans are highly skilled at these things, whereas computers find them fairly challenging. AI is typically used to describe computer algorithms that can learn and carry out these tasks.
Data Science
Data Science is an umbrella phrase for data-derived knowledge, just as AI is for intelligence. Data science is a collection of techniques and procedures for deriving knowledge from data.
Data processing, statistical analysis, data storytelling, and other data science practices are examples of how data might be processed, analyzed, and presented. Sometimes these analyses are straightforward. Sometimes they can be difficult. However, it's all data science.
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AI: Does Data Science need it?
Usually, sure. Before attempting to learn from the data, it is frequently advantageous for a human (or data analysis computer) to review the data. In order to help an AI continue to learn, data scientists frequently clean the data, extract the important information, and then provide this knowledge to the AI. This interaction frequently enhances AI learning because the AI may focus on particular data points.
However, the most advanced AIs of today can sort through vast amounts of data with little to no pre-processing. There is also automated software that can assist in selecting and preparing data for the AI.
Do we need AI for data science?
Sometimes. Data Science can be used to understand, explain, and communicate concepts about data. For instance, statistical analysis, which does not require a sophisticated AI, can examine rainfall data to determine whether the average rainfall indicates a rising or decreasing trend. However, AI can be used to uncover insights from data that are obscured by traditional data science techniques. This is especially true when dealing with complex data types or when data quantities are exceptionally high.
What is superior? – Data Science or AI
From the above content, we can say that these two terms may appear to be antagonistic or competitors. That is not the situation. The broad field of data includes understanding data and assisting computers in learning from the data and using their insights to solve problems automatically.
AI and data science complement each other effectively and are both crucial for business. In the future, we can expect them to interact without any issues, doing away with the need to pick one over the other. Further, careers in data science and AI are also increasing and many organizations are looking for data science professionals for their overall growth. Start upskilling with the most updated artificial intelligence course in Bangalore now for a lucrative AI career.
