Difference of Data Science, Machine Learning
Often, phrases are used in the world of technology as if they are interchangeable ideas or often, synonyms. However, such ideas are often mistaken and a nuanced understanding will surely lead to realizing such differences. Such is the case with machine learning and data science which are often used like synonyms as if machine learning is the only process that data science is involved in and vice versa. To curb such misunderstanding, it is time you start striking out the differences between two fields.
Of course, there is considerable overlap between these two fields as is there between deep learning, statistics, AI, IoT, operational research and data science. These fields surely have a huge shared ground on which most businesses are played out. But, beyond them, the breadth of each technology is much more and it is the borders where innovation is happening to stretch the idea of these technologies.
The breadth of the fields
Data science is surely humongous in its breadth. So much so, you may realize that there is no overlap between the works of two data scientists and still they can be prolific in their own terms. There is a lot of overlap, however, with many other scientific fields precisely because it needs to borrow various methods to handle big data, especially unstructured sets, and perform real-time transactions.
Data scientist is a specific designation however, not to be confused with various similar designations such as statistician, data engineer, and business analyst and data architect. Again, there is considerable overlap between these various roles but they do not necessarily sound synonymous by any means. There are data scientists who work with data through coding and hence, is mostly working on applications ranging from design to modelling, forecasting to inference. However, there are others who are doing production-specific data science with great background in software engineering.
Differences between two hot trends
Machine learning and deep learning are surely going places and it seems that they are hardly going to become irrelevant in some time to come. Machine learning deals with a bunch of algorithms derived by the data scientist to be applied on a specific set of data. There are classic algorithms and statistical measures that can be used as primary tools to develop more complex such algorithms.
However, all such algorithms need to be implemented on data sets manually or has to be controlled at least by human intervention. But, when deep learning comes in, the whole process becomes automated and this is precisely what AI does. AI, however, is a sub-branch of computer science and has got to do with everything that an able human can. Natural language processing, another aspect often mentioned, is a part of AI that deals with written language or texts.
The schism between data science and machine learning
Of course, machine learning is the most used tool in data science nowadays and hence, an obvious mistake in this regard is to consider machine learning as the equivalent of data science. Machine learning uses quite a few statistical tools such as regression to work on the data. However, there are many statistical data modelling algorithms that are not necessary in machine learning or don’t even fit in the bill.
Unsupervised clustering, for example, works on data that does not come a priori to the process. Hence, data science has under its aegis quite a few fields such as data engineering, distributed architecture, BI and data integration etc. Data scientists work in one of these fields and often specialize in a particular knowledge due to prior training.
Same field, separate applications
The world of data science is so vast that often, even within the same field, there is considerable difference of approach precisely because of the difference in applications involved. From internet traffic to website content, from healthcare to business decisions- being a data scientist is all about being flexible and working across applications. Only then can you start learning data science as a whole instead of focusing on specific developments. There is another branch called deep data science which involves neither coding nor statistical models but shows extreme sensitivity towards data which requires unique statistical technology that will focus on specific applications.