Artificial Intelligence vs. Data Science – Know the Differences

Data science and artificial intelligence (AI) applications are experiencing a truly stellar moment. The ability to use data to discover opportunities generate meaningful knowledge and support decision-making processes are widely accepted worldwide. Various industries are increasingly adopting disruptive technologies such as artificial intelligence, machine learning, and the internet of things. A study published recently by Market Study Report LLC confirms this trend, it is estimated that the global market for data science and artificial intelligence platforms will have a CAGR of 30% by 2026. Considering the huge talks about data science and artificial intelligence, we take the opportunity to cover the concepts of Artificial Intelligence Vs. Data Science and educate our readers on both the topics.

Data Science

Data Science, as its name suggests, deals with data. It is a multidisciplinary field focused on extracting insights that can help a company make better decisions. For example, if a bank analyzes the data collected on its multiple platforms, it can discover which customers are creditworthy (or insolvent) or identify which financial products new clients are buying.

To learn more about data science, read our blog – What is data science?

Today, the availability of large volumes of data means more revenue, thanks to Data Science. With predictive analytics, you can identify hidden patterns in data that you did not even know existed. For example, an online travel company may find that people traveling with US airlines to Amsterdam opt for a luxurious cruise on the city’s canals. Using prescriptive analysis, the company can further learn that people who fly Business prefer a night cruise, while those who fly economy class book bike tours. This data-driven information can be extremely valuable for targeted advertising or cross-selling strategies.

Must Read – Top Industries Hiring Data Scientists in 2021

A data scientist uses tools such as statistical models, data visualization methods, hypothesis tests, and machine learning algorithms. You may be wondering why some Data Science applications remind you of artificial intelligence applications. Well, this is because Data Science overlaps the field of artificial intelligence in many areas. So moving on to artificial intelligence now!

Artificial Intelligence (AI)

According to IBM, in computing, the term artificial intelligence (AI) refers to any human-like intelligence exhibited by a computer, robot, or other machines. In popular usage, AI refers to the ability of a computer or machine to mimic the human mind.

Learn more – What is Artificial Intelligence?

Artificial Intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and that have the ability to iteratively improve from the information they collect.

AI technology is improving business performance and productivity by automating processes or tasks that previously required human power. AI can also make sense of data on a scale that no human ever could. This ability can generate significant business benefits. For example, Netflix uses autonomous learning to provide a personalized streaming experience to its viewers and it helped the company grow its customer base by more than 25% since 2019.

You may like – Future of Artificial Intelligence: Top Industries It Will Affect

Today, it is possible to see how AI completes what we want to say when we write on our mobile devices, provides street directions when we drive, cleans our houses, and suggests what we should buy or see on electronic platforms.

Also Read – Top Real-World Artificial Intelligence Applications

Artificial Intelligence Vs. Data Science

	Artificial Intelligence	Data Science
Meaning	AI is the implementation of a predictive model to anticipate events	Data Science involves pre-processing analysis, prediction, and visualization
Technique	AI is all about algorithm design, development, efficiency, conversions, and commissioning of these designs and products.	Data Science is a generic term for statistical techniques, design, and development methods
Observation	AI is used to handle data autonomously, leaving aside people and letting the machine work alone	Data Science was developed to find hidden patterns and trends in data, its goal is to extract the most useful, process it, make sense of it, and ultimately put it on the table to make important decisions.
Problem-solving	Artificial Intelligence is designed to build models that emulate human knowledge and understanding to a certain level	Complex models can be built to achieve statistical techniques and insights using data science
Tools	Uses tools like Mahout, Caffe, PyTorch, TensorFlow, Scikit-Learn, etc.	Uses tools like Python, SAS, SPSS, Keras, R, etc.
Processing	High-level processing of scientific data for data manipulation	Medium level of data processing for data manipulation
Applications	Used in – Achieving task accuracy Quick decision-making Logical decision-making without emotional interference Managing repetitive tasks Translating, recognizing sounds and objects, and/or participating in business and social transactions Autonomous cars, OTT personalization, etc.	Used in –  Identification of patterns and trends When statistical knowledge is a requirement In need of an exploratory data analysis (EDA) and predictive analytics Situations requiring quick mathematical processing

Conclusion

Both data science and artificial intelligence go hand in hand. The application of AI algorithms and subsequent automation of data-driven tasks will lead to significant improvements in the productivity of data scientists. Companies are combining the power of human intuition with artificial intelligence to advance in an increasingly competitive environment. The future of artificial intelligence and data science looks bright and both technologies will be enriching each other and work together in the conquest of problem-solving and decision making.

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