he field of data science is inseparable from Information Technology (IT). Said differently, technology is the one of the major proponents in data science and data ecology apart from the units of data used for analysis. Equally, information is really the main concept studied in this IT profession. It is unlikely that data be analyzed digitally while neglecting technological tools. Thus, it is mandatory to know the ways by which technology affects data science and its popular branch: Machine Learning (ML) an important aspect of computational linguistics…
Azeez Olanrewaju Shoderu
Mental Wealth: Professional Life (Data Ecology) Module Task 5 Discussion University of East London, UK through UNICAF Scholarship
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Introduction
The field of data science is inseparable from Information Technology (IT). Said differently, technology is the one of the major proponents in data science and data ecology apart from the units of data used for analysis. Equally, information is really the main concept studied in this IT profession. It is unlikely that data be analyzed digitally while neglecting technological tools. Thus, it is mandatory to know the ways by which technology affects data science and its popular branch: Machine Learning (ML) an important aspect of computational linguistics.
Technology
Truthfully, the scope of the meaning of technology has expanded over the decades. It keeps taking new definitions and concepts. For the overall picture of what technology is and what it is not. It is quite necessary to uphold Kroes and Van de Poel (2009)’s two definitions of technology:
“1. Technology as a process (activity): is a collection of processes of designing, developing, producing, maintaining and disposing of technical artifacts;
Technology as a product (object): is a collection of technical artifacts, that is, what comes out of technology as a process in so far the latter is restricted to the design, making and maintenance of technical artifacts” (Kroes & Van de Poel, 2009, p. 62).
These suggest that technology can be used to refer to the procedure involved in analyzing data likewise it can indicate the device or software employed to run such scientific projects as well.The Influence of Technology
The role played by technology in our lives can rarely be usurped in today’s society and economy. In fact, we can notice the huge potential for advancement that technology now provides in every career path in our community (Alfahad, 2012). The sphere of technology increases frequently as new means of connecting with one another surfaces from time to time; many individuals now get to converse with their peers, loved ones and colleagues through various virtual outlets seamlessly. It is no more news that interactive devices like mobile phones, social media platforms and the internet have become a major player in affecting how people the world over now interact online (Hertlein, & Ancheta, 2014).
Impact of Information and Communication Technology (ICT) in Data Science
Indeed, the sector of ICT has grown at an alarming rate together with the new technologies around us and so as the demand for data created by ‘digital footprints’ of internet users skyrocketed as well. Moreso, the enormous capacity of gadgets manufactured worldwide has necessitated the large storage and sharing of data over the internet on a grand scale within the slightest period of time. Even, these would suggest that the proliferation of ICT tools such as Cloud Computing, Internet of Things have been initiated by the impact of ICT in the scheme of things. Surely, all these prove the importance of data science towards the breaking down of these data, analyzing them in order to extract valid business strategic conclusions to better help in our decision making process (Joshi, 2018).
Effects of ICT to Machine Learning
According to Sarker (2021), data is one crucial factor in artificial intelligence (AI) especially machine learning (ML) that is applied to convert all the manual works to automated and smart systems. To put this idea into perspective, imagine how a caller on the mobile phone contacts a call center and gets referred to an automated assistant who is more or less a robot or software who replies to your queries based on the computer programming that has been configured into it. Also, on the social media platform used by internet users be it Facebook, Instagram or anyone out of others, it can be noticed that many of them now make use of chatbot messaging which is a form of AI where some type of ML algorithm (like supervised, unsupervised, semi-supervised or reinforcement learning) is practically employed in easing the interaction between a client and the company in terms of their product and service offers. Lastly, without the leverage of a kind of technological product or process in ICT, the study or profession of data science and machine learning will not be possible or existent.
Reference List
Alfahad, F.N. (2012). Effectiveness of using information technology in higher education in
Saudi Arabia. Procedia – Social and Behavioral Sciences 46, 1268-1278.
Hertlein, K.M., & Ancheta, K. (2014). Advantages and disadvantages of technology in relationships: findings from an open-ended survey. The Qualitative Report, 19(11), 1-11.
Joshi, H. (2018). Data science | transforming the ICT domain. Aegis School of Business.
Kroes, P., & Van de Poel, I. (2009). Problematizing the notion of social context of technology. In S. H. Christensen, B. Delahousse, & M. Meganck (Eds.), Engineering in context (pp. 61-74). Denmark: Academica.
Sarker, H. I. (2021). Machine Learning: Algorithms, Real‑World Applications and Research Directions. SN Computer Science: A Springer Nature Journal. 160(2), 1-21, Singapore Pte Ltd.