One often makes use of terms such as Data Science and machine learning interchangeably. However, while there is indeed an overlap between the two, they are distinct from each other in terms of roles as well as responsibilities.
Data Science happens to be a field that has been around for a while now. Machine learning is indeed a fairly new discipline and has now become even more about building algorithms as well as self-learning solutions. Even as the boundaries between both of them do continue to blur, the disciplines stand discrete in their respective own rights.
The Pillars of Data Science
One of the primary characteristics of Data Science is that it happens to be a multi-disciplinary study, and heavily utilizes scientific methodologies. More often than not, Data Science does exist at the junction of statistics, business knowledge as well as technical skills.
Data Science, at its base, is indeed a way to extract important information from structured and unstructured data. Data Science also does focus heavily on being able to derive informed decisions and also strategic moves from data often termed as ‘insights’.
This does make statistics one of the biggest parts of data science, as it in fact stands as a fundamental part of the approach. When trying to make sense of data, statistics is an invaluable tool as it does wrangle the data in an approachable manner.
Another one of the core components of data science happens to be business acumen, as, without this, meaningful as well as usable insights cannot be derived. The individual wrangling of the data and trying to extract knowledge from it indeed lead to awareness of the workings of the company.
As mentioned previously, insights are indeed important in a corporate setting. They can also enable the creation of new business strategies as well as avenues for development. They can also rather identify potential revenue leakages, pain points, and non-profitable ventures, as well as rather provide a more comprehensive view of the company’s operations.
Statistics alone is not sufficient 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 happens to be an integral part of any data scientist’s approach to a problem. The rise of accessible machine learning has no doubt made it an ever-present part of data science.
At its base, machine learning is the process of writing an algorithm that can indeed learn as it consumes more data. ML has driven the importance of having a data scientist in every sort of big company. Owing to a large amount of data that data scientists have to handle, algorithms powered by ML are extremely important.
As of now, ML algorithms are indeed able to move the needle from descriptive and reactive business strategies to prescriptive as well as proactive business strategies. Moreover, this does represent a move from insights derived from collected data to predictions and projections derived from past patterns.
Machine Learning does allow data scientists to take on 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 indeed now becoming one of the more important parts of the functioning of an organization. An important distinction that has to be in fact made towards understanding the difference between this as well ML is that data science is a generalist approach while ML is a specialist approach.
Data Scientists
One does 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 do differ from organization to organization, as the needs of every company are different.
On the other hand, ML engineers are mainly tasked with creating tools that are made use of by data scientists. This does include cutting-edge models and efficient algorithms for use by data scientists. This is where one of the core differences between the designations that come in.
While it is indeed 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 does also include 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 really extensible to other companies, owing to the differences between business practices.
Therefore, it is important to make a distinction between data science as well as machine learning.
