If you are in the data science industry, chances are you are often mistaken to be also handling Artificial Intelligence, Deep Learning, and Machine Learning in some way or the other. Many professionals working in the data science companies have a hard time clarifying and justifying the absolute fact that data science is an altogether different specialization compared to AI and Machine Learning. Alas, because data science is hard to understand in a normal context, a large number of people are unable to fully comprehend the differences between Data Science, Artificial Intelligence, and Machine Learning.
So, when people ask me, is data science hard, I often answer what my mentor taught me: “Data science is not hard, but it’s just too much dynamic to be able to be boxed in a conventional set of definitions and philosophies.”
Let’s understand why data science is hard, and its association to AI, Deep Learning, and machine learning is stirring such a huge interest among keen followers.
Data science is one of the most competitive job markets in the world. The ecosystem has grown so rapidly in the last 2-3 years that many organizations now position themselves as purely data science companies. According to a recent data, the world generates 2.5 Quintillion bytes of data each day—and nearly one fourth of this data is produced from organizational operations in the form of website content, email marketing, marketing activities, and social media promotions. Each company is either planning to hire a new data scientist or already has one to handle the huge aspirational aspects of the organizations that want to go to the next level of working with data science. But, there is still a huge gap between expectations and reality. Well, you see, data science isn’t really an easy skill to master. In fact, researchers prefer to call data science among the hardest skills to learn and master in the current scenario, especially when innovations are coming hard and fast, and the philosophies associated with conventional data science are getting shattered so quickly.
This is where new capabilities such as Artificial Intelligence and new modeling techniques such as machine learning have played a huge role in taking data science to its current position of invincibility.
Artificial Intelligence: Artificial Intelligence simply involves the philosophy that supports the theory that computers can learn, simulate and respond to human instructions in a similar manner as another adult human. AI as a science gained massive popularity in the 90s when computerization of activities took center stage. After the internet revolution, the AI industry is probably the biggest attraction for data scientists who are trying to convince everyone that the future of mankind lies with how computers gain next level Artificial Intelligence. Some of the comparative studies show that AI is already on the verge of evolving into something we know call “Artificial Brain”, and this could be based on harder skills associated with cognitive learning, reasoning, and vision analysis.
Machine Learning: Machine Learning is just an advanced branch of AI that involves creating logical sequences called algorithms or decision trees to train, supervise and control machines so that we can generate a reasonable outcome without performing brain tasks. What began as a software revolution has now become a full blown specialization that brings together finer nuances of IT, Data Science, computer applications, mathematics, statistics, and neurology… The entire Machine Learning industry depends on the data science practices that clearly prove why is data science considered hard in the modern context of building virtual assistants, robotics, automation decks, and voice bots.
Deep Learning: Narrowing further down into the machine learning family, we have deep learning technologies that are focused on doing just one thing- simulating the human brain outside the cranium. This artificial brain is based on a Deep Learning technique called Artificial Neural Networking (ANN), which essentially grows on its own like a synthetic neural network fed by trillions and trillions of data. Deep refers to the stage at which the machine actually starts producing recurring reliable output such that no human supervision is required beyond the initial trigger.
So, by virtue of its expansiveness, deep learning is the hardest data science domain today.