Data Science is a field that has been around for a while now. Machine learning is a fairly new discipline and has now become more about building algorithms and self-learning solutions. Even as the boundaries between both of them continue to blur, the disciplines stand discrete in their own rights.
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.
Data Science, at its base, is a way to extract important information from structured and unstructured data. Data Science also focuses heavily on being able to derive informed decisions and strategic moves from data often termed as ‘insights’. Insights are one of the biggest products of practising data science and offer numerous advantages.
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.
Another one of the core components of data science is business acumen, as, without this, meaningful and usable insights cannot be derived. The individual wrangling the data and trying to extract knowledge from it must also be aware of the workings of the company.
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.