All businesses and inventions are endorsing the newest development is technology to create more. This world is the world of big data, AI, BI, and machine learning. Despite the cons, the pros are unlimited. Before specializing in any of them, we need to understand the difference between Data science and AI.
What is Data Science?
Data Science definition says that it is a combination of algorithms, data analysis, domain knowledge, mathematics, coding skills, statistic, computation, and other such processes to extract data from any kind of data at hand.
The three Vs of Big Data easily describe what data science holds. They are Velocity, Variety, and Volume. These three vectors of data science keep expanding in their axis to denote the three aspects of big data.
Data Velocity:
Velocity is Data science that measures the rate of flow of data flowing in. Since data is eventually growing every second, it is important to measure the speed at which it is entering the collection and observation zone. They can be measures in real-time, batches, or periods.
Data Variety:
There are numerous types of data that are used in big data. These are the varieties in forms of structures, unstructured, PDFs, documents, videos, and other formats available.
Data Volume:
It simply denotes the size of data that keeps increasing. Depending on the data variety, it could be in kilobytes, megabytes, or TB.
What is Data Science Used for?
Data Science sounds like a complicated term. However, this system is a simple and continuous process to read data and use them to our benefit. The data science cycle goes around by observing information at hand, interrogations, and creating an appropriate hypothesis, predicting, testing the predictions, and finally developing new theories to improve products and services.
At the stage of testing the predictions, new data is gathered for the verification of results. This phase is where the hypothesis either fails, gets verified, or face a modification.
Data Scientist:
A data scientist is responsible for the Big Data Science system, especially the end when they have to communicate to managers, share partners, stakeholders, and other related authorities.
What is Artificial Intelligence (AI)?
Artificial Intelligence known as AI has been the talk of the years in most industries. AI is the introduction of human intelligence into machines. This enables the robot to act, work, and behave like humans for the task they are programmed for.
So, what are these behaviours that they are programmed for? The most usual answer is problem-solving and learning. So, a robot mimics human actions through programmes to fulfil the purpose they are made for. For copying human actions, the need to possess human intelligence which is input to them through codes.
What is AI Used for?
Artificial Intelligence by definition could be easily understood. The use is diverse depending on the industry where technology is essential. AI is the power with which the work efficiency and productivity level increases every day be it for any field.
AI is of two types, Weak/ Normal AI or Strong/ AGI (Artificial General Intelligence). These could be understood by saying that weak AI can perform only one task at once, and AGI is meant for an Advanced version when the machine behaves like a living thing to execute the operations using their thought process.
So, we collect data and develop a new system to improve our products and services using various resources when it comes to big data science and AI. But what are the major features of Data Science vs Artificial Intelligence that make the two completely different from each other?
Big Data vs Artificial Intelligence: Difference Between Data science and Artificial Intelligence
- Data Science is used for Marketing, Promoting, Advertisings, Search Engines on WWW, etc. Artificial Intelligence is used in most industries that need operations to be done through machines like hotels, healthcare, automation, etc.
- AI is algorithm-based, while Data Science is based upon statistics.
- In Artificial Intelligence, forecasting is done to understand future requirements. However, data science is a continuous process of collection, observation, forming hypotheses, testing them, and forming theories.
- AI involves Machine learning and deep learning. Data Science, on the other hand, uses data analytics.
- Data Science welcomes data in any form, be it structured or unstructured. Artificial Intelligence has data in the form of vectors and such.
- The purpose of data science is to observe the different sets of data at hand to use them to their benefit. Artificial Intelligence is related to embedding human intelligence into machines for problem-solving purposes.
- Where artificial intelligence is used for making robots act like humans with such psychological behaviour, data science models are used for decision making.
- AI is fully based on programmes and algorithms, whereas data science is a mix of programmes, computation, statistics, expertise in the domain, visualisation, and more.
Conclusion on Data Science vs AI
One can understand the difference between data science and artificial intelligence by interpreting their definitions. Both are an essential part of many industries to develop in what they do. They are not to be confused with each other as both are based on completely different fundamentals.
AI is not accepted as an official term by most of the webmasters and analysts. Most of the common people who rather depend on AI for their industry’s productivity. Artificial Intelligence has only stepped into the world and will be going through various inventions. Data science on the other hand has already progressed enough to be used confidently for the company’s merits.
