Data science Vs software engineering! A primer to dispel the confusion surrounding the dependent yet different concepts
Data Science and Software engineering has too many qualities in common there is a hell lot of confusion regarding where one end and where the other starts invoking a typical data science Vs Software engineering haze. It is an established fact that advanced technologies like artificial intelligence, machine learning, IoT, Cloud, and Blockchain will be the main pivots around which the tech world will revolve and so more is the synergy and more blurred are the lines. To understand what data science Vs software engineering is all about, to their nitty-gritty, one should get to know what they have in common.
Going by the pace at which the software sector is growing, it is pretty much evident that there is an urgent need for the development of digital technology. SaaS, a sector that is playing a crucial role in delivering critical software services to companies, has become the quickest-growing sector. The growth of services like cloud computing technologies, open source, programming services, and systems services has aided to a great extent in the development of advanced technologies like machine learning, artificial intelligence, and computer vision which depend on data and data analytics. Notwithstanding the dependencies, data science and software programming share a few stark differences.
Data as commodity
Software engineers are involved in front-end or back-end development, developing operating systems, and designing software. Basically, software engineers need not use data because the software programs are supposed to be universal, ie., work for every kind of data. Of late, with the evolution of customization, software programmers are taking to Data Driven Development (DDD). It essentially involves developing software or software products for a certain set of data. On the other hand, Data scientists work on predictive models and develop machine-learning capabilities based on the data generated by those models. For example, a software engineer may develop an order entry system tool that the company may use for the coming 20 years, and the data science engineer will analyze the correlation between geographical distribution and sales and the changes therein from time to time.
Probabilistic Algorithms Vs Deterministic Algorithms
Software engineering is meant for creating tools and products that can generate the same result every time they run. A simple bill-calculating program will generate the exact result for four entries of $5, ie., $20 every time. On the other hand, data scientists are ‘programmed’ for predicting rather than deliver run-of-the-mill outputs. Data science, to a large extent, depends on maths and stats and hence the predictive nature of their decisions. That means they cannot certainly tell that you will buy a hockey stick but can predict it with 99% accuracy.
Degree of Autonomy
Software engineers may have to manage a large group for the very reason that code development is a collective task. Data scientists though might have to supervise a smaller team, oftentimes can work alone, and hence have a greater degree of autonomy. However, it depends on the company’s size and requirements. Compared to software engineers, Data Scientists have fewer people under their wing but when it comes to reporting they report to more members of authority than software engineers.