Data Science vs Artificial Intelligence

Data science (DS) and Artificial intelligence (AI) are two different things. Data science is a comprehensive process for analyzing data through statistical techniques or machine learning. On the other hand, artificial intelligence provides a way to predict future events with the help of computer algorithms. The processes involved in data science and AI are different from each other. Data science makes use of various statistical techniques such as linear regression, logistic regression, support vector machines, clustering analysis, Bayesian analysis, and natural language processing, whereas AI uses a predictive model to make future predictions which are implemented using computer algorithms like Bayesian networks.

Data science (DS) analyses large amounts of data, utilizing existing algorithms and extracting useful information. On the other hand, AI helps machines or computers learn by themselves without any human intervention. Hence, Data Science and Artificial Intelligence seem to work together to provide more efficient data analysis services.

Data Science and AI are employed in various industries to accomplish tasks such as navigation, scheduling, decision-making, etc. Both Data Science and Artificial Intelligence are witnessing remarkable growth in the market, and both are constantly evolving with newer techniques. However, they have differences, like Data Science is not limited to a single technology, whereas artificial intelligence is an umbrella term for all kinds of technologies.

• Section 1: What is data science?

• Section 2: What is the work of a Data Scientist?

• Section 3: What is AI?

• Section 4: What is the work of an AI engineer?

• Section 5: The Key difference between Data Science and AI.

What is data science?

Data science is the practical application of mathematics, computer science, and engineering to solve problems in information technology and the related fields of the real world. The companies behind the modern data-driven economy create new applications and tools to make life easier for everyone. Still, many people have no idea what data is or how it can be used. What does "data science" mean? And how are businesses using it?

In its most basic form, data science involves extracting data that can be used to create insights or value from extensive data. The value can come in many forms: information about customers, products, competitors, or services. Data science uses data and analytical techniques to determine relationships between objects, entities, or phenomena.

What is the work of a Data Scientist?

A Data scientist is a professional who has the ability to extract information from raw, unorganized data and amalgamate it into meaningful and actionable signals. The primary and messy data can be from any industry and can be generated from various sources.

The job of a Data Scientist professional is to work closely with the management to help them make better decisions by predicting future events.

Data Scientists make up the core of what is left of the Digital Human Revolution. They are the ones who can make sense of big data, capitalize on it, and make decisions based on the knowledge that is accurate and actionable. 

Data Scientist is a job title that typically refers to a highly trained technical analyst responsible for extracting useful information from the company's data to help the company make better decisions. For example, an audit may require the Data Scientist to work with the finance department to create a detailed analysis of sales trends. In contrast, a potential acquisition may require the assistance of data scientists in creating complex models of customer demographics and purchasing behavior to determine which types of products would be most profitable to pursue.

The skillset required to become a Data Scientist is R Programming, Python Coding, Hadoop Platform, SQL Database/Coding, Apache Spark, Data Visualization, Intellectual curiosity, Business acumen, Communication skills, and Teamwork.

What is artificial intelligence?

Artificial intelligence (AI) is one of the biggest buzzwords today in technology. You are reading this because you are probably interested in knowing more about AI. Good! Because here, I am going to give you a clear definition of what AI is.

Artificial intelligence - the capability of a machine to imitate intelligent human behavior. AI is something that can understand context and patterns in data, can perform human tasks like recognizing faces or emotions, and presents itself in many shapes, forms, and sizes. 

What is the work of an AI engineer?

Artificial Intelligence is used to power software systems that are capable of learning and increasing their capabilities. These AI systems process data through various sensors and inputs and feed that data into higher-level systems which analyze and respond. The Artificial Intelligence (AI) Engineer is an IT expert who aims to develop intelligent algorithms capable of analyzing, learning, and predicting future events. Their role is to create machines or systems capable of reasoning, like the human brain.

The following table shows the critical difference between Data Science and AI.

Data Science	Artificial intelligence
Data Science is a comprehensive process that involves pre-processing, analysis, visualization and prediction.	On the other hand, AI is the implementation of a predictive model to forecast future events.
Data Science comprises of various statistical techniques.	whereas AI makes use of computer algorithms.
Data Science is about finding hidden patterns in the data.	AI is about imparting autonomy to the data model.
With Data Science, we build models that use statistical insights.	On the other hand, AI is for building models that emulate cognition and human understanding.
Data Science does not involve a high degree of scientific processing	whereas AI involve a greater degree of scientific processing to complete the task
The tools involved in Data Science are a lot more. This is because Data Science involves multiple steps for analyzing data and generating insights from it.	whereas tools involved in AI are significantly low.
Data Science applications are used in the field of Internet Search Engines like Google, Yahoo, Bing, Marketing Field, Banking, Advertising Field and many more.	Artificial Intelligence applications are used in many sectors like the Healthcare industry, transport industry, robotics industries, automation industries, and manufacturing industries.
• Python or R • Jupyter Notebook • TensorFlow • Statistics • NLP (Natural Language Processing) • SQL • Tableau or other similar visualization tools	• Java, Python, or C++ • AI Systems • Deep Learning with TensorFlow, PyTorch • Deployment models in production • Computer Science • Architecting or delivering cloud solutions • Spark • Scala

Conclusion:

In this Data Science vs Artificial Intelligence, we got to know the difference between the terms used interchangeably. Artificial Intelligence refers to a broad domain that is still largely unexplored. Data Science is a field that uses AI to generate predictions and focuses on transforming data for analysis and visualizations. 

Thus, in the end, we conclude that while Data Science is a job that performs analysis of data, artificial intelligence is a tool for creating better products and imparting them autonomy. I hope you liked our explanation of "Data Science vs. Artificial Intelligence." 

You can read our blog, Jobs of the Future: Artificial Intelligence, to get an idea of what AI will be like in the future. 

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