Tue. Dec 5th, 2023
Artificial Intelligence vs Machine Learning vs Data Science

Data is the new oil,” as Clive Humby famously stated. Without data, we would be unable to achieve anything meaningful; it holds the key to our planet’s progress. Artificial Intelligence and Data Science play a significant role in Machine Learning arena – these are the Light Batons that will ultimately defeat Darkness.

Though AI, data science, and machine learning are often considered interchangeable terms, they have distinct goals, methods, applications, and purposes. In this blog post we’ll look into the similarities and distinctions between these fields (big data vs. machine learning vs. artificial intelligence).

We’ll cover the fundamental concepts and techniques in each field, as well as their real-world applications and career options. This blog post will give you a comprehensive overview of AI, machine learning, and data science.

What Is Artificial Intelligence?

Artificial Intelligence vs Machine Learning vs Data Science
Image Credits: Unsplash

Humans may be superior creatures than other species, but we also possess some weaknesses. Most notably, humanity has failed to control its emotions.

Due to Human Emotions Play, we have experienced great failure in many areas. Our intellectuals cannot make decisions; our brain is wired for logic but due to emotional play it ain’t happening.

First What Is (AI)”Artificial intelligence is a system that appears intelligent. That definition, however, may not be precise enough; something as seemingly intelligent could just as easily be considered unhealthy.” At its most fundamental level, artificial intelligence refers to machines that act and appear human-like and can mimic human behaviors.

These behaviors, such as problem-solving, learning and planning, can be replicated through data analysis and pattern recognition – something we’ve already seen with Jim Simon’s Case Study.

Optimization, artificial neural networks and methods based on statistics, probability and economics are just some of the topics explored within this field which draws heavily from computer science, information engineering, mathematics and many other disciplines.

In the twenty-first century, Artificial Intelligence (AI) techniques have seen a resurgence due to advances in computer power, large amounts of data and theoretical understanding. Nowadays these AI methods are an integral part of technology industry solutions for complex issues in computer science, software engineering and other related disciplines.

Benefits of Artificial Intelligence(AI)

  • Automation is easy with AI: AI allows you to automate repetitive, high volume tasks by setting up reliable systems that run frequent applications.
  • Intelligent Products: AI can turn conventional products into smart commodities. AI applications when paired with conversational platforms, bots and other smart machines can result in improved technologies.
  • Progressive Learning: AI algorithms can train machines to perform any desired functions. The algorithms work as predictors and classifiers.
  • Analysing Data: Since machines learn from the data we feed them, analysing and identifying the right set of data becomes very important. Neural networking makes it easier to train machines.

What is Machine Learning?

Machine learning (ML) is the study of computer algorithms that learn automatically through experience and the use of data. It’s considered part of artificial intelligence. Machine learning algorithms construct models based on sample data – known as “training data” – in order to make predictions or decisions without being explicitly programmed to do so (data science vs machine learning vs artificial intelligence).

What is Data Science?

Data science (DS) is an interdisciplinary field that applies scientific methods, processes, algorithms and systems to extract knowledge and insights from structured Data and Unstructured Data. Data Science helps create insightful solutions from raw information that address real world complexities.

Data Science is the future, believe me: Without data science, no industry or business will be able to keep up. Already around the world, numerous transitions have taken place where data-driven business decisions were sought out ,and more are expected in the near future. Data science can be seen as “the oil of the 21st Century”, providing new opportunities across various industries. If you’re interested in this career path, your hard work will be rewarded with an exciting career, generous pay cheques and job security.

Data Science(DS) encompasses everything we’ve discussed thus far.

Perception > Planning > Action > Feedback of Perception

Data Science utilizes different parts of this pattern or loop to solve specific problems. For instance, in the initial step (perception), data scientists attempt to recognize patterns through data. Likewise, during planning, there are two components:

Discovering All Potential Solutions

Finding the ideal solution to reduce amo is no small feat.

Big data i.e, Data Science(DS) has a great impact on the World. Data-driven businesses are worth $1.2 trillion collectively in 2020, an increase from $333 billion in the year 2015. Data scientists are responsible for breaking down big data into usable information and creating software and algorithms that help companies and organizations determine optimal operations.

Skills Needed to become a Data Scientist :

Sure, here are some of the essential skills needed to become a data scientist:

  1. Programming Skills: A data scientist must have a strong foundation in programming languages such as Python, R, and SQL, which are commonly used in data science.
  2. Data Wrangling: This refers to the process of cleaning and transforming raw data into a more usable format. Data scientists should be proficient in techniques such as data cleaning, data integration, and data transformation.
  3. Machine Learning: Machine learning is an essential aspect of data science, and data scientists should have a good understanding of machine learning algorithms and techniques, such as regression, classification, clustering, and deep learning.
  4. Data Visualization: Data visualization involves presenting data in a visually appealing and understandable format. Data scientists should be skilled in using data visualization tools such as Tableau, Power BI, and ggplot2.
  5. Statistics: Data scientists must have a strong foundation in statistics, including probability, hypothesis testing, and regression analysis.
  6. Domain Knowledge: It is important for data scientists to have a good understanding of the domain they are working in. In LIS, this would mean having knowledge of information management, information retrieval, and information organization.
  7. Communication Skills: Data scientists need to be able to communicate their findings effectively to non-technical stakeholders. They should be able to explain complex technical concepts in a simple and concise manner.
  8. Problem-Solving Skills: Data scientists should be able to identify business problems and develop data-driven solutions to address them.
  9. Collaboration: Data science is a team sport, and data scientists should be able to collaborate effectively with other team members, including data engineers, business analysts, and subject matter experts.
  10. Continuous Learning: Data science is an ever-evolving field, and data scientists should be committed to continuous learning and staying up-to-date with the latest trends and techniques.

Scope of DS

Business intelligence is one of the fields in which data science plays an integral role. Data scientists sift through vast amounts of information to detect patterns and trends, then create reports with their conclusions. Business Intelligence professionals then take over from data scientists by applying their insights on current business conditions and forecasting future outcomes based on those predictions. A related field known as Business Analyst also draws upon data science techniques, business intelligence applications, and data analytics; often combining all three together to help companies make data-driven decisions.


AI, Data Science and Machine Learning are three distinct but closely related fields that have transformed how we think about computing, data analysis and decision-making.

AI refers to machines performing tasks intelligently, mimicking human intelligence; Data Science involves extracting insights from data and making informed decisions based on them.

Machine Learning, on the other hand, is an area within AI and Data Science that aims to develop algorithms that allow machines to learn from data in order to make predictions or decisions.

Each field presents its own challenges and opportunities, yet there is much overlap between them.

Though AI and Machine Learning are often used interchangeably, they are distinct concepts, with Machine Learning providing one approach to building AI systems. Data Science plays an integral role in both of these processes as it provides the necessary data and insights that fuel these systems.

Overall, the continuing growth and development of AI, Data Science, and Machine Learning is expected to have a profound effect on our world and way of life, revolutionizing how we live, work, and interact with one another. These fields will continue to advance with new technologies that help us address increasingly complex problems while creating exciting new opportunities for innovation and growth.

By Hari Haran

I'm Aspiring data scientist who want to know about more AI. I'm very keen in learning many sources in AI.

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