To comprehend the need for the study of data, it is quite imperative to first of all comprehend what exactly data are. According to the Australian Bureau of Statistics (2013), data itself is a plural form for the word: datum which is a singular value for a particular variable of information…<\/p>\n
Azeez Olanrewaju Shoderu Mental Wealth Module Task 1 Discussion\u00a0University of East London, UK through UNICAF Download Article Introduction To comprehend the need for the study of data, it is quite imperative to first of all comprehend what exactly data are. According to the Australian Bureau of Statistics (2013), data itself is a plural form for the word: datum which is a singular value for a particular variable of information.\u00a0 Forms of Data In data science, data are mostly in numeric form and are gotten through observations or even measurements. Though, data come in two forms: the qualitative and quantitative value sets whereby the qualitative deals with non-numeric data forms and quantitative refers to the numeric value sets based on a single or multiple animals, places, persons or things (OECD, 2008).Classes of Data A data scientist employs the use of various data types while working on his projects. These data can be as well grouped into some main classes. The classes are majorly two: the primitive or primary data or atomic data type and the non-primitive or composite or complex or secondary data type. However, in the literature, there is also a third underlying class which is often referred to as the abstract data type (Placide, 2013). Primitive Data Types In a bid to achieve world-class education through online courses, some criteria must be set to take care of the disparity between the traditional classroom teaching and online courses. This is to say that the teacher responsible for impacting knowledge in the lives of the students online must initially set some learning pathways and teaching processes which will not be too rigid to implement virtually and the students in question should be allowed to create their own communication guidelines that would foster smooth studies amongst their peers and foster healthy relationship with their instructors online (Palloff and Pratt, 2007). Sener (2010) predicts that, in the near future, students will mostly be involved in one type of e-learning or the other be it fully or partially in their various courses of study mixed with traditional classroom teaching. In the same vein, students must be made to know how well to use the digital applications or platforms and devices of virtual education so that they can feel much more comfortable learning online just like in the traditional classroom setting in order to help develop a positive mindset towards education and increase students\u2019 involvement. Moreso, teachers should take note of the attained knowledge of the students while working with the gadgets and virtual learning environment together with their faculty (Palloff and Pratt, 2007). Data Analysis and Data Repositories \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 On the other hand, secondary data class includes the complex data structures like record (also known as structs), arrays (both one-dimensional and multi-dimensional), strings (combination of characters), pointers, linked list (single-linked list, doubly linked list, etc), stacks, queues, trees and graphs (Bullinaria, 2019). The primary data are used to form these unique data classes.\u00a0 Data needs to be collected in order to be worked on. Hence, there are some vital methods through which data scientists and other experts gather data. Some of the most common ways any kind of data can be elicitated either the qualitative or quantitative data are through the field work surveys (be it direct or indirect surveys) and observations. And, they can be later analyzed through the use of experiments (like manipulative, natural or observational), modelling (through simulation model) after of course developing hypothesis for the research.\u00a0 For the purposes of education, research and posterity, data are now kept in data repositories especially the digital kinds. All the contents of these libraries comprises of both the numeric and non-numeric types of data. In addition, these archives have in them databases and data catalogues that are readily accessible by students, researchers and data scientists online. Datasets in these repositories are identified through the use of metadata which show important information about the data searches that users are looking and the corresponding datasets (Ma, 2019). Thus, data repositories serve as a preservation tool for not just the data itself but all other properties of the research, academics and programming that corroborate the collection, development and enhancement of the data. Reference List Australian Bureau of Statistics (2013). Statistical Language – What are Data? Archived from the original on 2019-04-19. Retrieved 2020-03-09. Bullinaria, J. (2019). Lecture Notes for Data Structures and Algorithms. UK: University of Birmingham. Ma, X. (2019). Data Repository. In Schintler, L.A., McNeely, C.L. (eds.) Encyclopedia of Big Data. Switzerland: Springer, Cham In Press. http:\/\/dx.doi.org\/10.1007\/978-3-319-32001-4_59-1\u00a0 OECD (2008). Glossary of Statistical Terms. p. 119. ISBN 978-92-64-025561. Placide, D. (2013). Data Types and Data Structures. Cameroon: PCHS Mankon \u2013 Bamenda.<\/p>\n","protected":false},"excerpt":{"rendered":"
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