Today Data Science and machine learning tend to be used interchangeably. While there is a significant overlap between the two, there is a distinction to be made in the roles and responsibilities that each of them encompasses.
Data Science as a field has been around for quite some time now but machine learning is a fairly new field at the intersection of computer science and statistics. It is now about building algorithms and models that learn with data. Even though the boundaries between the two continue to be blurry, theres still a difference.
Data Science is a multi-disciplinary study that heavily utilizes scientific methodologies. Data Science exists at the crossroads of mathematics, statistics, business data and technical skills.
Data Science focuses heavily on making informed decisions on the basis of data. This is often termed as 'insights. Insights offer businesses great advantages in terms of decision-making. This is what makes statistics a big part of data science. It is a fundamental part of the approach in Data Science. Another core component of data science is business acumen. Without this, meaningful insights cannot be derived and implemented. The person or team handling the data to extract knowledge from it must also be aware of the significance of the data, and what it means to the organization.
As discussed earlier, insights are very important in a corporate setting. They enable the creation of new business strategies and avenues for development. They can also be used to identify potential revenue losses, pain points, and unprofitable ventures.
Statistics alone cannot derive insights from the large amounts of data that most companies generate and collect today. This is where training models and algorithms come in.
Machine Learning is an integral part of the data scientists approach to a problem. The rise of accessible machine learning has made it an ever-present part of data science.
At a fundamental level, machine learning is the process of writing an algorithm that can learn as it consumes more data. ML has driven the importance of having a data scientist in every big company. Owing to a large amount of data that data scientists have to handle, algorithms powered by ML are extremely important.
Traditional definitions of machine learning define Machine Learning as an application of Artificial Intelligence that gives computer systems the ability to "learn" from the data and improve from past failures and experience, without being explicitly programmed to do so. While this textbook definition gives us a sense of machine learning in very technical terms, it fails to give us a broad outlook of its potential and how it is relevant today.
To conclude, machine learning is the future that is knocking on our doors with increasing urgency. The question is, are we ready to open the door and welcome it into our lives?