Artificial Intelligence vs Data Science
AI and data science are two of the numerous critical technologies today. Although data science uses AI in its operations, it does not fully reflect AI.
We shall examine the distinctions between data science and AI in this essay. We will also discuss how scholars worldwide are affecting modern AI.
AI and data science are commonly used in the same sentence. Data science can assist with some aspects of AI, but not all of them. Data science is currently the most popular field worldwide.
True AI, however, is still a long way off. Though it’s a common misconception, AI and current data science are not the same things. Let’s compare data science with artificial intelligence to clear up any misunderstandings.
What is Data Science?
The technology that is currently ruling enterprises all over the world is data science. The fourth industrial revolution in history is the outcome of it. This results from the enormous data growth and businesses’ growing dependence on data to provide better goods. Our society has transformed into one that is data-driven. Data has become an absolute need for industries that depend on data to make informed decisions.
Numerous underlying fields, such as mathematics, statistics, and programming, make up data science. Thus, data scientists must be proficient in understanding data trends and patterns. Due to the high level of competence required, data science has a steep learning curve. Data scientists also need to have.
Data extraction, modification, visualisation, and maintenance are among the many stages and techniques used in data science to foresee future events. A data scientist should be knowledgeable about machine learning techniques.
Artificial intelligence, which we will discuss more in this essay, is exemplified by these machine learning algorithms. Businesses require data scientists to assist them in making decisions based on data. They aid enterprises in evaluating their performance and making crucial recommendations to enhance it. They help the product development team create goods that appeal to buyers by researching consumer behaviour.
What is artificial intelligence?
Artificial intelligence is a group of intricate computer systems that mimic human intelligence. AI-programmed computers may “learn” as they operate, becoming more adept at solving specific problems as they gather more data. It also involves decision-making processes, picture and speech recognition, understanding human speech, and translations.
Artificial intelligence is a technology developed by humans that enables computers to read, analyse, and learn from data to help decision-making. These findings form the basis of judgements that would be challenging for others to recognise.
Current technology divided artificial intelligence into two categories: general AI and applied AI. General AI functions include speaking, interpreting, recognising sounds and objects, and participating in economic and social activities. Self-driving cars and other sensor technology are instances of applied AI. In self-driving cars, artificial intelligence and creative memory are employed. To understand patterns and designs, algorithms are utilised.
Algorithm executions have advanced to the point that they may now be carried out from laptops and mobile devices.
Critical differences between AI and Data Science
1. Scope
Data Science is concerned with several fundamental data activities, whereas Artificial Intelligence is solely interested in implementing ML algorithms.
2. Data Formats
Vectors and embeddings are typical forms of data used in artificial intelligence, whereas structured, semi-structured, and unstructured data are used in data science.
3. Tools
Data science tools include Keras, SPSS, SAS, Python, R, and others, while artificial intelligence technologies include Mahout, Shogun, TensorFlow, PyTorch, Kaffe, and Scikit-learn.
4. Applications
Artificial intelligence applications are used in several sectors, including manufacturing, robotics, automation, and healthcare. At the same time, data science applications are used in various industries, including marketing, banking, advertising, and internet search engines like Google, Yahoo, and Bing.
5. Process
The Artificial Intelligence (AI) method uses the predictive model to anticipate what will happen in the future. But data pre-processing, analysis, visualisation, and prediction are all part of data science.
6. Techniques
Data Science uses various statistical tools, whereas artificial intelligence techniques use computer algorithms to solve issues.
7. Purpose
Automating the process and granting data models autonomy are the fundamental aims of artificial intelligence. But the initial goal of data science is to find obscure patterns in data. Each has a unique set of objectives that are separate from the other.
8. Different Models
Artificial intelligence models are developed that are thought to be comparable to human comprehension and cognition. In data science, models are created to produce statistically meaningful information for decision-making.
9. Level of Scientific Processing
Artificial intelligence will require a very high level of scientific processing compared to Data Science, which uses less scientific processing.
Average salaries of a Data Scientist and an AI Engineer
An average data scientist makes $116,654 a year. Companies that provide these high salaries are aware of the importance of big data and are eager to use it to better business decisions. Starting salaries are likewise growing more desirable in this developing field. An entry-level data scientist may earn up to $93,167 annually, but an experienced data scientist may make up to $142,131 annually.
Similar to this, the annual salary of an artificial intelligence engineer exceeds $100,000. With a low of $90,000 and a high of $304,500, the average national yearly pay in the United States is 164,769 dollars. Given the massive growth in job opportunities for AI developers, salaries for these professionals will keep rising.
Final Words
While artificial intelligence is still being thoroughly investigated, data science has already started to have a significant influence on the market. Data is altered by data science so that it may be viewed and examined.
Artificial intelligence enables the production of better-than-before commodities and promotes autonomy by automating various jobs. Data science is used to analyse data and make wise business decisions, which has several advantages for businesses.
Many AI-based companies provide jobs with just AI-related job titles, such as “NLP Scientist,” “Machine Learning Engineer,” and “Deep Learning Scientist.” To perform numerous operations on data, data science techniques are developed in languages like Python and R. Today; important decisions are based on data that data scientists have analyzed. Data science must thus be a vital component of any company.
