A Brief Overview: Data Science Vs. Machine Learning Vs. Artificial Intelligence


Over the past decade, the technological revolution has been expanding at an exponential rate, and organizations are adopting new technologies. IT spending is expected to grow to 4.4 trillion U.S. dollars by 2023, an increase of 3.9 trillion dollars since 2020. Due to the technological revolution, Machine Learning, Artificial Intelligence, and Data Science have changed our day-to-day lives. 

Data Science, Artificial Intelligence, and Machine Learning are sometimes used interchangeably. There are significant differences between the two, though may seem similar to a layman. Machine Learning is the process of designing programs that run on their own and can learn on their own, in concert with other machines or humans, or both. Artificial Intelligence – the science that seeks to make machines capable of making decisions similar to those made by humans – relies heavily on Machine Learning to make it possible. 

As a result of Data Science, systems are developed that collect and analyze disparate pieces of information to get to the bottom of business challenges and find solutions to practical issues in the real world. As part of Data Science, Machine Learning is used for finding patterns in data and automating the process by which data is analyzed. Artificial Intelligence and Machine Learning have both grown rapidly as a result of Data Science. 

There are many differences between Data Science, Artificial Intelligence, and Machine Learning, which are discussed in this article. 

What Is Data Science? 

Data has become one of the biggest assets for businesses in the day and age that we live in. Having access to more data will allow Data Scientists to generate better insights. As a result of data analysis, they have been able to discover patterns that were unknown before. In addition, this allows them to make better decisions and stay ahead of the curve by making informed decisions. 

Data Science is a multidisciplinary field that basically involves the analysis of large data sets, whether they are raw or structured, to extract insights from the data. A data scientist is a professional who uses computer science, statistics, Machine Learning, and predictive analysis in order to establish solutions for questions that are yet to be answered. 

There are many sources of data from where Data Scientists collect and extract information , which they then analyze for results. It is not the end of their work, however. Furthermore, they are also responsible for transforming the results they get into solutions and sharing the findings in order for businesses to make the right decisions based on the findings. 

Real-world Examples 

  • Predictions for manufacturing demand: The manufacturing industry is the first real-life example of Data Science in action. Creating forecasts for product demand is one of the main functions of Data Science for many manufacturers. By utilizing this solution, they are able to optimize supply chains and deliver orders without having to worry about over-ordering or under-ordering. 
  • Analyzing healthcare data to make predictions: Predictive analytics in healthcare is another example of Data Science that could contribute to the field of healthcare. Predictive models analyze historical data, learn from it, identify trends and then generate accurate predictions based on those trends. 

What Is Artificial Intelligence? 

An Artificial Intelligence system is nothing more than a computer simulation of the intelligence of humans. As a result of Artificial Intelligence (AI), machines can think, learn, and solve problems in the same way that human brains do. Artificial Intelligence is capable of rationalizing just like we do and taking actions based on what is most likely to lead to the achievement of a goal. Machines with Artificial Intelligence are capable of executing tasks by imitating the intelligence of humans in order to accomplish the desired task. 

Real-world Examples 

  • AI in Banking and Finance: A number of banks are using Artificial Intelligence technologies to detect fraudulent activity and for a variety of other purposes. There are already a number of banks that are making use of Artificial Intelligence-based solutions to provide customer service, detect anomalies and prevent theft of credit cards. 
  • AI in Gaming: It is no secret that Artificial Intelligence has played a major role in the growth of the game industry over the last few years. It is mostly AI that is utilized in video games to determine the behavior of non-player characters (NPCs) that appear in the game. When it comes to creating a video game, a great deal of AI is involved in establishing how a computer opponent will act. Depending on the game, the behavior can range from basic patterns in action games to chess systems that can defeat world champions and professional players in their own games. 

What is Machine Learning? 

The concept of Machine Learning is a subset of the concept of Artificial Intelligence. Through the use of deep learning, machines can learn, improve, and evolve through the acquisition of new experiences without the need to program them in order to do so explicitly. A major goal of Machine Learning programs is to be able to access data, utilize it, and learn for them based on that data. 

Real-world Examples 

  • Speech recognition: Speech can be translated into text with the help of Machine Learning. The conversion of live voice and recorded speech can be performed by using certain software applications. Furthermore, it is possible to segment the speech based on the intensity of time-frequency bands as well. 
  • Statistical arbitrage: The arbitrage strategy in finance is a way in which securities can be managed and traded in a large volume using an automated trading system. By analyzing a set of securities through the use of economic variables and correlations between them, a trading algorithm is used to evaluate a set of securities. 

How Do Data Science, AI, and Machine Learning Work Together? 

Data Science, Machine Learning, and Artificial Intelligence are intersecting fields that need to be considered. Together, they allow us to manage business operations better, avoid risks, lead a healthy and productive life, and enjoy the pleasures of work, life, and leisure safely and enjoyably. 

In conjunction with Data Science, Machine Learning, and Artificial Intelligence (AI), predictive analytics is made possible so that data scientists can forecast consumer behavior in order to provide better customer service for retail stores through improved inventory control and delivery systems. With such technology, it is possible to develop conversational chatbots, which increase customer service, healthcare support, and the ability to control smart TVs with voice recognition, among other things. 

It is possible to provide personalized product recommendations, financial advice, and medical care using Machine Learning. A combination of Artificial Intelligence Data Science, and Machine Learning, enables us to detect fraud and prevent cyber attacks on an unsurpassed scale. 

Comparison Between Data Science, Artificial Intelligence, and Machine Learning 

Check out the table below to understand the major differences between AI, ML, and Data Science. 

Machine Learning  Artificial Intelligence  Data Science 
ML is a subset of Artificial Intelligence.  AI includes Machine Learning and other technologies.  Data Science Includes various Data Operations. 
This can be done by using a set of efficient programs that are capable of using data without the need for explicit instructions.  By combining large amounts of data through iterative processing and intelligent algorithms, Artificial Intelligence (AI) is able to assist computers in learning automatically in order to assist us in our daily lives.  As part of the Data Science process, data is sourced, cleaned, and processed in an attempt to take meaning out of it for analysis. 
Statistical models are used in Machine Learning.  The principles of Artificial Intelligence are based on logic and decision trees.  Structured and unstructured data are the two types of data that are dealt with by Data Science. 
Spotify and Facial Recognition are two of the most popular examples that can be found today.  Among the most popular applications of Artificial Intelligence are chatbots and voice assistants.  Examples of applications of Data Science include the detection of fraud and analysis of healthcare data. 

Comparing The Skills Required To Become Specialists In Each

In the fields of Data Science, Artificial Intelligence, and Machine Learning, there is a wide variety of career choices available. There is a number of overlapping foundational computer science skills that are required for all three practices as they are interdisciplinary in nature. However, there are differences in the processes, the techniques, and the use cases. 

Data Science 

In Data Science, data is collected, processed, analyzed, visualized, and predicted. Analyzing data remains a key focus of Data Science. Coding, data visualization, statistics, and programming skills are required. It is important for data scientists to work in every industry since they are responsible for identifying medical conditions, optimizing logistics, improving city planning, fighting fraud, and improving shopping experiences. 

Artificial Intelligence 

Models developed by Artificial Intelligence specialists can replicate human intelligence. A computer program uses AI to learn, reason, and correct itself. Model evaluation requires programming skills, statistics, and signal processing techniques. In addition to making autonomous vehicles a reality and making payment technologies safe to use, AI specialists are behind our access to personal assistants, entertainment apps, and social media apps powered by Artificial Intelligence. 

Machine Learning 

Machine Learning is the process of allowing machines to learn from data and produce accurate results. It involves automating the analysis of large datasets and making correct decisions with minimal human intervention using Machine Learning. Mathematical and natural language processing skills are necessary, as well as statistics, probability, and data modeling. In addition to detecting defects in parts, improving manufacturing processes, enhancing inventory management and supply chain management, and preventing crime, Machine Learning specialists develop applications based on algorithms. 


So now you must have a basic awareness of the differences between Data Science, Artificial Intelligence, and Machine Learning. If you’re willing to begin your career in any of the respective fields, then you need to check out the UNext Jigsaw’s Data Science and Machine Learning course. 

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