Artificial Intelligence 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 around data science. The phrases AI, Data Science, Machine Learning, Deep Learning, etc., are used almost synonymously of this, which further complicates things.

What is AI?

Any technology where a computer program is attempting things that naturally occur to the human brain is referred to as artificial intelligence (AI). Every day, people demonstrate intelligence in actions, including reading written language, hearing speech, identifying items in pictures, and scheduling activities to make the most of their time. Most are picked up spontaneously by our brains as we develop and interact with the environment and are then improved and progressed through formal education.

While computers find these activities fairly difficult, humans are quite adept at them. AI is typically used to describe computer algorithms (means of structuring programs) that are able to learn and carry out these tasks.

What is Data Science?

Data Science is an umbrella phrase for knowledge derived from data, just as AI is for intelligence. Data science is a collection of techniques and procedures for deriving knowledge (insights, lessons, etc.) from data. Any type of data may be used (stock prices, sensor data from rainfall meters, voice recordings, satellite images, etc.). Data processing, statistical analysis, data storytelling, and other data science practises are examples of how data might be processed, analysed, and presented. Sometimes these analyses are straightforward (like average rainfall). They can be considerably more difficult at times. However, it's all data science.

Does AI need Data Science?

It is frequently beneficial for a human (or data analysis machine) to study the data before attempting to learn from it. Data scientists frequently clean the data, take out the key information, and then give this information to an AI so it can continue to learn. Because the AI may concentrate on specific portions of the data, this intervention frequently aids in the improvement of AI learning.

However, today's most sophisticated AIs are capable of sorting through massive amounts of data that have had little to no pre-processing. Additionally, there is automated software that can help prepare and choose data for the AI. As a result, not all sophisticated AIs need traditional data science.

Does Data Science need AI?

Sometimes. Data Science can be utilized independently to comprehend, articulate, and share ideas regarding data. For instance, statistical analysis, which does not require a sophisticated AI, can be used to examine rainfall data to determine whether the average rainfall exhibits an increasing or decreasing trend. AI can, however, be used to extract insights from data that are hidden from view by conventional data science methods. This is especially true when dealing with complex data types (like video) or extremely high data volumes.

Conclusion

These two terms can seem to be at odds with one another or to be rivals. That is not the situation. Understanding data, as well as assisting computers in learning from the data and applying their insights to automatically solve issues are all part of the large topic of data and machine intelligence. Data science and artificial intelligence are both essential for businesses and work well together. We can anticipate a seamless interaction between the two in the future, eliminating the necessity to choose between them.