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Difference Between Data Science vs Artificial Intelligence

Data science vs Artificial Intelligence are two buzzwords that have become increasingly popular in recent times. Although they may sound similar, they are quite different fields. In Data Science vs. Artificial Intelligence, Data Science involves using mathematical and statistical methods to analyze data and gain insights from it. For example, a Data Scientist may work for a healthcare company analyzing data to identify potential health risks in a specific population.

In contrast, Artificial Intelligence focuses on creating machines that can perform tasks that would normally require human intelligence, such as recognizing speech, understanding natural language, and making decisions based on data. One example of Artificial Intelligence in action is a virtual assistant like Siri or Alexa. In fact, these assistants can understand and respond to spoken commands using natural language processing algorithms.

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In this article, we will delve into the differences between Data Science and Artificial Intelligence. Additionally, we will discuss how these fields are transforming the way we live and work.

What is Data Science?

Data science involves the study of data using math and technology to find insights and answers. Essentially, its goal is to help individuals and organizations make better decisions and predictions by uncovering patterns and trends in data.

How Data Science Works? (With Example)

Let us understand how data science works through an example of Amazon.

As an online retailer, Amazon collects data on customer behavior, product searches, purchase histories, and more. This data is stored in their data centers for further analysis.

Amazon sifts through the data to identify patterns and trends. For example, they may identify the most popular products or the most frequent search terms.

 Using machine learning algorithms, Amazon builds predictive models that can forecast future outcomes. For instance, they may use customer data to predict which products are likely to be popular in the coming months.

Amazon uses A/B testing to experiment with different features on its website, such as different product layouts or pricing strategies. This helps them determine which features are most effective at driving customer engagement and sales.

Through the use of NLP, Amazon is able to analyze customer feedback from sources such as product reviews and social media. As a result, they gain valuable insights into customer sentiment and are able to identify areas for improvement in their products and services.

Amazon uses data visualization tools to present its findings in a way that is easy to understand. This includes using charts, graphs, and other visual aids to communicate complex data to stakeholders.

By leveraging data science techniques, Amazon can make data-driven decisions that improve its business outcomes. For example, they may use insights from their data to optimize their supply chain or improve their customer experience.

Scope of Data Science



Business Analytics Analyzing data to improve business outcomes in areas such as marketing, finance, supply chain management, and more.

Healthcare Analyzing medical data to improve patient outcomes and reduce costs in areas such as disease diagnosis, drug discovery, personalized medicine, and more.

Education Analyzing learning data to enhance the learning experience for students in areas such as adaptive learning, intelligent tutoring systems, and learning analytics.

Cybersecurity Analyzing network traffic to detect and prevent cyber attacks in areas such as intrusion detection, threat intelligence, and security analytics.

Data Science Jobs & Salary

Role Responsibility & Skills Salary

Data Entry Specialist Enter and process data and maintain accurate records.

Microsoft Office


$30,000 – $57,000

Data Analyst Create surveys on required subjects to collect and evaluate data.

Microsoft Office

Data analysis


$47,000 – $89,000

Data Engineer Translate algorithms into code and maintain data set patterns.

Programming languages

Data analysis


$67,000 – $134,000

Data Scientist Collect and verify the data and create predictive systems.


Machine learning

Data analysis

$70,000 – $137,000

Data Architect Designs the structure to organize relational (project-based) data.

Database architecture

Data modeling

Programming languages

$82,000 – $168,000

Data Science Manager Sets definite objectives and methods to attain the goals.

People management

Machine learning

Statistical analysis

$103,000 – $187,000

Data Science Director Establishes strategies, provides guidance and plans overall goals.


People management

Big data analytics

$122,000 – $212,000

Vice President, Data Science Systemize the company’s tools and technologies to build solutions.


Machine learning

People management

$146,000 – $299,000

What is Artificial Intelligence?

Artificial Intelligence (AI) is when computers are designed to do things that normally require human intelligence. It’s like giving a machine a brain so it can learn and think like a person. One example of AI is a chatbot that can talk to you like a human. You might have used a chatbot when you’ve had a question about customer service on a website or app. The chatbot can understand what you’re asking and respond with an answer just like a real person.

Self-driving cars are another example of AI. These cars use sensors and software to analyze their surroundings and make decisions about how to drive. They see and understand the world around them, much like a human driver. AI has many exciting possibilities, but it also raises important questions about how we use this technology and its impact on our lives.

How Artificial Intelligence Works? (With Example)

To understand how Artificial Intelligence (AI) works, let’s take the example of a face recognition AI system called “Find a Face”.

The “Find a Face” system studies numerous images of human faces to train itself to recognize different facial features. The system studies feature such as the position of the eyes and the shape of the nose, which helps it recognize and differentiate human faces from one another.

The “Find a Face” system uses sophisticated machine learning algorithms to identify unique patterns in the data. For instance, it may recognize that certain groupings of pixels in an image often indicate the presence of an eye or other facial feature. By detecting these patterns, the system gets better at identifying faces accurately.

After completing the training phase, the “Find a Face” system analyzes new images that it hasn’t seen before to test its ability to recognize faces. The system attempts to detect the position of facial features in each image and match them to the corresponding landmarks.

By analyzing the results of its testing, the “Find a Face” system fine-tunes its algorithms to enhance its facial recognition accuracy. For example, if the system struggles to detect a particular facial feature, it may modify its algorithms to look for other related data patterns. This iterative process of refining algorithms continues until the system can accurately recognize human faces across a broad range of images.

Scope of Artificial Intelligence  

Areas Scope

Natural Language Processing Enabling machines to understand and respond to human language in areas such as chatbots, virtual assistants, and sentiment analysis.

Robotics Enabling machines to act in the physical world in areas such as autonomous vehicles, drones, and manufacturing automation.

Image and Video Analysis Enabling machines to recognize objects, faces, emotions, and more in areas such as facial recognition, object detection, and video surveillance.

Recommendation Systems Providing personalized recommendations for products, services, and content in areas such as e-commerce, entertainment, and social media.

Artificial Intelligence Jobs & Salary

Role Responsibility Salary

Software Developer Develop automated software to simplify accomplishing tasks.




$53,000 – $120,000

Software Engineer Structure separate software functions into one developed program.

Javascript & Java



$66,000 – $133,000

Artificial Intelligence (AI) Researcher Create and execute AI tools and evaluate the system’s results.

Machine learning

Deep learning


$68,000 – $179,000

Machine Learning Engineer Build, test, and implement AI machine learning algorithms.

Machine Learning


Deep learning

$79,000 – $156,000

Solutions Architect

Enterprise solutions

Software architecture

Cloud computing

$90,000 – $144,000

Product Manager, Software Mediate between clients and internal teams and manage software development.

Product management

Agile software development

Business strategy

$93,000 – $162,000

Chief Technology Officer (CTO) Lead a team of technical professionals and make crucial business decisions.


Software architecture

IT Management

$81,000 – $395,000

Vice President (VP), Engineering Manage a team of researchers, developers or engineers to attain assigned goals.

Project management

Engineering design


$188,000 – $259,000

(Source: Payscale)

Data Science vs Artificial Intelligence (Infographics)

Below is the Top 7 Comparison between Data Science vs. Artificial Intelligence.

Key Differences between Data Science Vs. Artificial Intelligence

The following main differences between data science vs. artificial intelligence are:

Data Science is a comprehensive process that aims to collect massive amounts of data for analytics and visualization. Artificial Intelligence, on the other hand, is the execution of a predictive model to forecast future events.

Data Science includes a broad range of statistical methods, whereas AI employs algorithms.

The tools used in Data Science are far more extensive than those used in AI. It is because Data Science involves several steps for analyzing and generating insights from data.

SAS, SPSS, Keras, R, Python, and other tools in data science, whereas AI uses tools such as Shogun, Mahout, Caffe, PyTorch, TensorFlow, and Scikit-Learn.

Users build models using statistical insights with Data Science. AI, on the other hand, is to create models that resemble cognition and human understanding.

In contrast to AI, data science does not require a high level of scientific processing.

Final Thoughts FAQs

Answer: Data science and artificial intelligence are two distinct fields with different problem-solving approaches. Data science relies on analytical tools to collect and interpret data, while artificial intelligence uses algorithms and software to identify patterns in data.

Choosing between the two depends on your career goals. If you’re interested in research, data science may be the better path. On the other hand, if you want to develop intelligent software products, machine learning or AI could be the best option for you.

Answer: In terms of salaries, the average income for AI professionals is higher than that of data science professionals. While data science professionals earn an average of $72,000 per year, those in the AI industry make an average of $130,000 per year. However, it’s important to note that salaries can vary depending on the specific role and responsibilities of an employee within either field.

Answer: It’s not easy to say which is easier to learn, AI or data science. They overlap, but AI has two parts called machine learning and deep learning, which both use data science. Data science uses machine and deep learning to understand data. Generally, it’s simpler to learn data science than AI, because data science is about analyzing data using math and stats, while AI is about making machines that can do things like humans do.

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