Data Science Terminology - Introduction
Unmasking the true relationship between Data Science, Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL).
In the last decade, data have transformed into a science by itself and also remodeled how organizations perform their operations. As the interest in data science has risen, so too has the common terms associated with it. Today, almost everyone has an idea about “AI”, “ML” or “DL”. However, wide and vacuous use of these terms has led them to turn into a buzzword among people, resulted in confused and even abused consumption in daily speak. In this article, I will clarify the accurate meanings of these terms and explain how they relate to each other.
Definitions
Here are the selected definitions of the terms:
Data Science: An interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
Artificial Intelligence (AI): The field of computer science dedicated to solving cognitive problems commonly associated with human intelligence, such as learning, problem solving, and pattern recognition.
Machine Learning (ML): The science of getting computers to act without being explicitly programmed.
Deep Learning (DL): Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making.
Big Data: High-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.
The Relationship
Although the definitions shed light on the meanings of the terms, the interconnection between those terms requires closer attention. The venn-diagram given below illustrates the relationship between data science related terminology:
The figure highlights:
- Deep Learning (DL) is a subset of Machine Learning (ML),
- Machine Learning (ML) is a subset of Artificial Intelligence (AI) and
- They have common elements with Data Science and Big Data.
Surely, there is no surprise that Big Data is a branch of Data Science, because Data Science is a broad scientific discipline that examines everything about data and big data is just a special type of data that has volume, velocity, variety, veracity and value (5 V’s of Big Data).
Data science does not only make use of AI techniques (including ML and DL), but also utilizes other approaches like causal inference or operational research. These are the examples of the area that Data Science encircles, and AI does not. On the other hand, such Robotic Process Automation (RPA) examples that does not essentially handle data (for example, automation of filling up a predetermined form with specific inputs) are an example of the field that AI includes, but Data Science excludes. The RPA example is also an element of the area that Machine Learning does not encompass.
Lastly, Logistic Regression, Decision Trees, Support Vector Machines and K-Means Clustering are some of the elements of Machine Learning set that do not belong to Deep Learning set.
Certainly, the developments in the Data Science and AI related fields will continue to accelerate and probably we will meet new terms on the way. Before welcoming the potential upcoming terms, it is critical to understand and internalize the fundamental terminology of today. So, this article provides the overview of those terms along with the distinction between them.
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DISCLAIMER
All views and opinions expressed in this post are my own and do not reflect the official policy or position of any agency, organization, employer, company or entity whatsoever with which I have been, am now, or will be affiliated.
