Data is considered the “oil of the 21st century”, making data science, Machine Learning, and Artificial Intelligence ‘hot topic’ fields today. There has been a rise in interest in data science over the last decade or so. This is due to the growing demand for data science in almost every industry – healthcare, information technology, urban planning, or sports. This demand is expected to increase further in the coming years. If you are looking to learn more about this ever-evolving field, this blog might be helpful to understand and distinguish between data science, Machine Learning, and Artificial Intelligence. 

What is Data Science?

According to an article by Science Daily, in 2013, about 90% of the world’s data was generated over two years. The volumes of data released by each individual collected and combined into different groups over a long period. This unbelievable feat is made possible through data science. Data science is a broad term that covers a multitude of disciplines. Generally, data science is understood as the study that utilises extensive unruly data that is cleaned and analysed to communicate valuable insights to aid in decision making. It also includes managing and maintaining data systems and their processes, ultimately trying to make sense and find meaning from numbers. Companies can leverage data to uncover patterns, behaviours, or trends requiring technical training and education. Read this to gain an understanding of the Top Skills Required for a Data Scientist. 

Data science follows a life cycle with five stages. An individual can find their niche within any of the five stages. They are – 

Capture – Data Acquisition, Data Entry, Signal Reception, Data Extraction

Maintain – Data Warehousing, Data Cleaning, Data Staging, Data Processing, Data Architecture

Process – Data Mining, Clustering/Classification, Data Modelling, Data Summarization

Analyse – Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative AnalysisCommunicate – Data Reporting, Data Visualisation, Business Intelligence, Decision Making

What is Artificial Intelligence?

Artificial Intelligence or AI is a subset of Data Science. Stuart Russel and Peter Norvig, in their textbook “Artificial Intelligence: A Modern Approach”, describes AI as “the study of agents that receive precepts from the environment and perform actions”. An agent can perceive its environment through sensors and act upon that environment through effectors. It essentially replicates human intelligence in machines, programmed to exhibit traits of a human mind like learning and problem-solving. 

If broken down further, Artificial Intelligence is the ability given to a computer to make sense of large amounts of data, learn from it and make decisions or inferences that are otherwise difficult for humans to carry out. AI can also adjust the knowledge gained based on new inputs that were not used initially when imparting the ability to these machines. 

Check out this blog to get insight into the Impact of Artificial Intelligence on Business

What is Machine Learning? 

Machine Learning is a subset of Artificial Intelligence. It is the component of AI that enables the machine to learn from large amounts of data and apply knowledge to new data that enters the system without the need for consistent programming or human assistance. It gives computers tacit knowledge that allows these machines to determine trends and patterns and make connections and predictions. Almost all industries utilise Artificial Intelligence and Machine Learning. 

To better understand what Machine Learning does is to observe its application in our day-to-day life. A prime example is virtual personal assistants like Siri, Alexa, or Google Home. Machine Learning is applied here based on information gathered by the machine as we interact with it, thus rendering outputs that are customised to our preferences. Another example that is intrinsic to our lives is the use of GPS navigation services. It is through Machine Learning that details on traffic congestion are obtained.

Data Science vs Machine Learning vs Artificial Intelligence

Relationship between Data Science, Artificial Intelligence and Machine Learning

All three of these studies are at the forefront of the digital revolution of this century, and there is a temptation to use data science, Machine Learning and Artificial Intelligence interchangeably. However, this is not the case. Artificial Intelligence is a discipline of Data Science, and Machine Learning is a subset of Artificial Intelligence. The concept that connects all three is the use of Big Data. Each of these fields attempts to make sense of this data for various purposes.

Difference between Data Science, Artificial Intelligence and Machine Learning

Data Science, Artificial Intelligence and Machine Learning are related to each other but are distinct in purpose and application. 

	Data Science	Artificial Intelligence	Machine Learning
Primary Function	Produce Insights for Decision Making	Produces/Perform an Action	Machine Learning produce Predictions
Models	Structured and unstructured data	Logic and decision trees	Statistical Models
Skills	Understanding SAS and data analytical tools, R, Python, SQL, Tableau, Statistical Analysis	TensorFlow 2, Scikit Learn, Keras	Hands-on experience with MALLET, Apache Tomcat/ Open Source, C++, Python
Application	Tactical Optimisation, Predictive Analysis, Fraud Detection, Social Research	Natural Language Processing, Reinforcement Learning, Robotics and Control Theory	Recommendation System, Facial Recognition

Jobs and Salaries

An increase in demand for data science, Artificial Intelligence and Machine Learning makes them a part of the top career paths of the future, as sought out by interested candidates. Each of these fields is not mutually exclusive, and the respective jobs are bound to overlap. 

Based on estimations made by the U.S. Bureau of Labor Statistics – Beyond Numbers, data science and mathematical science occupations are projected to grow by 28% from 2016 to 2026, resulting in about 50,500 new jobs due to the increased use of big data by businesses and governments. Here are a few of the possible jobs and their salary in each field of study as per Payscale.

Job	Median Annual Income
Data Scientist	$114 k
Senior Software Engineer	$133 k
Machine Learning Engineer	$125 k
Artificial Intelligence Specialist	$135 k
Systems Architect	$169 k
Enterprise Architect	$ 152 k

Despite the overlap in data science vs Machine Learning vs Artificial Intelligence, what differentiates one from the other in the application and specific functionalities. These fields are poised to grow at an exponential rate, and for those looking to kick start their journey, the future is bright.
For more information, head on over to Virginia Tech India to learn more about data science, Artificial Intelligence, and Machine Learning.