Tracing the exponential growth trajectory of data science and reasons for this phenomenal rise

The trajectory of growth that data science has traced in the last few years has been phenomenal. Credit goes to not only the interdisciplinary nature of this field but also the novel techniques introduced by it. The interdisciplinary nature of this field includes subjects like artificial intelligence, machine learning, big data engineering, business intelligence, and the like. Similarly, the novel techniques include data mining, processing, visualisation, and analytics. The outbound growth trajectory of data science can also be attributed to a plethora of data analytics courses online. In this article, we trace the associated factors that have gradually contributed to the rise of data science over the last few years.

Interdisciplinarity of data science 

The interdisciplinarity of data science is not only related to the large network of subjects under its domain but also its multisectoral range of applications. In addition to this, data science also includes under its gamut a spectrum of soft and hard skills. As an example, soft skills include problem solving approaches, effective communication and dialogue, personal engagement, customer interaction, client feedback, as well as critical thinking. It us the combination of soft skills that helps professionals in collaboration and team work. On the other hand, hard skills include techniques related to software development, testing, and debugging. Other important techniques or skills that can be included in this category include machine learning, deep learning, advanced algorithms, reducing dimensions of complex problems, and predicting a likely outcome from previous data sets.

Strong just a position with big data analytics 

With the growing importance of data to various technical and non-technical organizations worldwide, the question of adequate management still looms large. This is where big data engineering or big data analytics come into play. Big data analytics helps organizations to tackle large data streams with a lot of ease. While the storage space is increased by data lakes, the analytics capabilities are boosted by edge computing. Edge computing handles the processing of large data streams very close to the source, thereby decreasing latency. In this way, the entire ecosystem of Big Data Analytics allows us to mine out quantitative and structured information from large unstructured data sets. This information plays a pivotal role in predictive analytics and strategic decision making for an organization.

The coding exercise

It is difficult to imagine a subject related to computing, analytics, and data mining which does not require holistic coding exercises. However, this is where the subject of data science drifts away from the traditional methods. It reduces the necessity of herculean coding exercises and this is one of the prime reasons for its popularity among generalists. Although python is a default programming language when it comes to machine learning and data science, the levels of complexity associated with it can be easily understood in a span of a few weeks. There are a lot of aspects of data analytics which do not require coding at all.

Concluding remarks 

The pace at which data science is growing suggests that it would soon find its place in the formal educational setup of various countries.