Difference Between Data Science and Artificial Intelligence

Data Science:

In 1974, Peter Naur proposed data science as an alternative name for computer science. Data Science is a subset of Artificial Intelligence. Simply data science is a collection of data to analyze and we make a decision on behalf of it. It uses scientific methods, processes, algorithms, and insights from many structural and unstructured data. the person who works in data science is known as data scientist. 

Advantages and disadvantages of Data Science:

Advantages: 

Data Science allows businesses to extract valuable insights from data and make data-driven decisions, which can lead to increased efficiency and profitability. It also helps businesses identify patterns and trends, detect anomalies, and optimize processes. Data Science can be used in a variety of industries, including healthcare, finance, and retail.

Disadvantages: 

Data Science requires a large amount of data to be effective, and collecting and cleaning data can be time-consuming and costly. There is also a shortage of skilled Data Scientists, which can make it difficult for businesses to find the right talent.

Artificial Intelligence:

 At a conference at Dartmouth College, Hanover, New Hampshire, where the term Artificial intelligence was coined (1956). It’s a human-like intelligence provided to the machines where machines act and think as humanly. They solve problems faster than human beings. Speech recognition, translation tools, etc. are the building areas of AI. AI is all about machine learning deep learning etc. We can emulate cognition and human understanding to a certain level

Advantages and disadvantages of Artificial Intelligence:

Advantages:

 Artificial Intelligence can automate repetitive and time-consuming tasks, improve efficiency, and reduce human error. It can also analyze large amounts of data quickly and accurately, and provide personalized recommendations and insights. Artificial Intelligence has the potential to transform many industries, including healthcare, transportation, and finance.

Disadvantages: 

Artificial Intelligence requires large amounts of data to train models, and biased data can lead to biased results. There are also concerns about the ethical implications of Artificial Intelligence, such as job displacement and privacy concerns. Additionally, the development of Artificial Intelligence can be expensive and time-consuming.

Similarities between Data Science and Artificial Intelligence:

• Both rely on large amounts of data to be effective.
• Both use statistical techniques to analyze data and extract insights.
• Both are interdisciplinary fields that draw from computer science, mathematics, and statistics.

Difference Between Data Science and Artificial Intelligence

S. No.	Parameters	Data science	Artificial Intelligence
1.	Basics	Data Science is a detailed process that mainly involves pre- processing analysis, visualization and prediction.	AI(short) is the implementation of a predictive model to forecast future events and trends.
2.	Goals	Identifying the patterns that are concealed in the data is the main objective of data science.	Automation of the process and the granting of autonomy to the data model are the main goals of artificial intelligence.
3.	Types of data	Data Science will have a variety of different types of data, including structured, semi-structured, and unstructured type of data.	AI uses standardized data in the form of vectors and embeddings.
4.	Scientific Processing	It has a high degree of scientific processing.	It has a lot of high levels of complex processing.
5.	Tools used	The tools utilized in Data Science are far more extensive than those used in AI. This is due to the fact that Data Science entails a number of procedures for analyzing data and developing insights from it.	The tools used in AI are less extensive compared to Data Science.
6.	Build	By using the concept of data science, we can build complex models about statistics and facts about data.	By using this we emulate cognition and human understanding to a certain level.
7.	Technique used	It uses the technique of data analysis and data analytics.	It uses a lot of machine learning techniques.
8.	Use	Data science makes use of graphical representation.	Artificial intelligence makes use of algorithms and network node representation.
9.	Knowledge	Its knowledge was established to find hidden patterns and trends in the data.	Its knowledge is all about imparting some autonomy to a data model.
10.	Examples of Tools	R, Python, etc. are the tools used in data science.	Tensor flow, sci-kit-learn, Kaffee, etc are the tools used in AI.
11.	Models	Models are built in Data Science to generate statistical insights for decision-making.	Models are created in Artificial Intelligence that is believed to be analogous to human understanding and cognition.
12.		Data Science looks for patterns in data to make decisions.	AIs look to intelligence reports to make decisions.
13.	Applications	Its applications are advertising, marketing, Healthcare, etc.	Its application is robotics, automation, etc.
14.	When to use?	Data Science will be employed when: The problem necessitates quick mathematical computation. Exploratory data analysis is required (EDA) You must employ predictive analytics. It is necessary to identify patterns and trends. Statistical knowledge is required.	AI will be employed when: There are repetitive chores involved. You must conduct a risk assessment. Rapid decision-making is required. Exactness is necessary. You demand logical decision-making free of emotional bias.
15.	Examples	Process optimization, Customer trends, and financial analysis are some examples.	Robots, Chatbots, online gaming, and voice assistants are some examples.

Conclusion:

 while Data Science focuses on extracting insights from data, Artificial Intelligence focuses on building intelligent systems that can perform tasks that normally require human intelligence. Data Science is an interdisciplinary field that uses a variety of techniques to analyze data, while Artificial Intelligence is mainly a computer science field that heavily relies on machine learning.