Data science has popularly been referred to as the GOAT job which means the ‘greatest of all times’. The popularity associated with data science is not because it is a merely lucrative job. In fact, data science has numerous advantages over other types of jobs that make it the best in industry. That said, there are a lot of doubts and dilemmas related to data science. In this article, we attempt to resolve the dilemmas of data science and clear doubts regarding the same. We do this by answering some of the most important questions in the domain of data science.
Data science emerged as a field within computer science engineering that was interdisciplinary in nature. It was due to this reason that data science developed a strong overlap with other branches and exceeded the popularity of its parent branch viz. Computer Science Engineering. In the initial stage of development, data science was considered as an IT job. However, in the present time data science is more than an IT job because it has become a full fledged discipline in itself. The greatest thing about data science is that its contribution to the research work in Computer Science Engineering in general and other interdisciplinary fields in particular has been phenomenal. It would not be incorrect to note that data science has served as an effective bridge between engineering fields and the fields of social science.
What is the future scope of data science?
In the present times, data science has already established itself as a prominent discipline. In the future as well, data science would continue to ride along the growth curve and its scope would increase enormously. We are now looking towards three main branches through which the scope of data science can expand in the coming time. Firstly, social data science is emerging as a prominent field within data science to cater to the needs of sociological studies by proposing new research methodology and making new breakthroughs. Secondly, cultural data science is also emerging as a popular discipline within data science. In addition to this, data science is actively contributing towards the growth of the ongoing digital revolution. While data science is expanding its scope in the domain of social science, it is actively contributing to the advancement of technology. We conclude that the scope of data science is witnessing exponential growth and this trend is slated to continue in the coming times.
The programming dilemma
The programming delma is one of the most widespread doubts among all the data science aspirants. Most of the data science aspirants consider that if they are unable to program, they would not be able to become data scientists. However, data science being an interdisciplinary branch is much more than mere programming skills.
We cannot ignore the fact that programming is one of the most important cornerstones of data science. Most importantly, the programming language of python is the core and heart of data science. Different types of models are conceived with the help of the programming language of python. Other programming languages like R can also be used for this purpose.
However, there are certain areas within data science where programming skills are not very essential. This is the prime reason that data science attracts students from a broad range of disciplines.
How is data science different from big data analytics?
Big Data Analytics is all about the management and processing of huge amounts of data that is generated in the present time from various sources. These sources include wearable devices, electronic gadgets, iot devices and other participating devices in a smart environment. The social media platforms and other applications of the internet are also very rich sources of data. This enormous influx of data has created limits on our traditional processing capabilities. This is where the role of Big Data Analytics comes into play. On the other hand, data science is a much broader field that incorporates Big Data Analytics under its umbrella. In addition to this, it also includes fields like artificial intelligence, machine learning, business analytics and the like.
In one word, the dilemmas regarding data science are only short lived as this field would prove to be the future of the technological revolution.