The Pillars Of Data Science
One of the primary characteristics of Data Science is that it is a multi-disciplinary study, and heavily utilises scientific methodologies. More often than not, Data Science exists at the junction of statistics, business knowledge and technical skills.
This makes statistics one of the biggest parts of data science, as it stands as a fundamental part of the approach. When trying to make sense of data, statistics is an invaluable tool as it wrangles the data in an approachable manner.
As mentioned previously, insights are important in a corporate setting. They can enable the creation of new business strategies and avenues for development. They can also identify potential revenue leakages, pain points, and non-profitable ventures, as well as provide a more comprehensive view of the company’s operations.
Statistics alone is not enough to derive insights from the deluge of data that most companies handle today. This is where training models and algorithms come in.
The Roots Of Machine Learning
Machine Learning is an integral part of any data scientist’s approach to a problem. The rise of accessible machine learning has made it an ever-present part of data science.
At its base, 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.
Today, ML algorithms are able to move the needle from descriptive and reactive business strategies to prescriptive and proactive business strategies. Moreover, this represents a move from insights derived from collected data to predictions and projections derived from past patterns.
Machine Learning allows data scientists to take their roles to the next level, and also offers a novel way of management. Nowadays, an understanding of machine learning is integral to be a data scientist.
Data Science Is More Than ML
Data Science is now becoming one of the more important parts of the functioning of an organisation. An important distinction that has to be made towards understanding the difference between this and ML is that data science is a generalist approach while ML is a specialist approach.
Data Scientists heavily benefit from a broad subject matter expertise area. This is owing to the varied nature of their role, as they will also be required to communicate the insights and their benefits to a non-technical audience. Even as they are generalists, data scientists differ from organisation to organisation, as the needs of every company are different.
On the other hand, ML engineers are mainly tasked with creating tools that are used by data scientists. This includes cutting-edge models and efficient algorithms for use by data scientists. This is where one of the core differences between the designations come in.
While it is possible to directly scale machine learning capabilities by hiring more individuals, it is not possible to do so with data scientists. Hiring a data scientist also includes a period of learning and training, where the employee is required to know about the company’s processes.
Data Science operations cannot be scaled up directly, as there will be diminishing returns with a team of data scientists. The designation is also not extensible to other companies, owing to the differences between business practices.
Therefore, it is important to make a distinction between data science and machine learning.