Initial Idea 1:Data Analytics

Data Analytics

As a start example, we all know that various sciences have existed since ancient times, when the thinkers and researchers have been striving to find answers to the mysterious questions and to reach any scientific result. So, there must be a procedure to follow to deal with the collected data and information. This last is called "Data analytics" which means in general the process that used to analyse a large set of data, and it aims is to gather those data element and organize them to come up with a useful information or a conclusion in context of computer science (TechTarget, 2022). And the different steps of Data analytics are:

• Identifying and determining the business question, the issues to solve, the matrices you want to measure.
• Collecting the basic data available at sources such as government records or social media application programming interfaces.
• Cleaning the data for analysis. dealing with the white spaces and other syntax errors.
• Analysing the data. we manage the data through some tools, at the aim of transforming it into an easy-to-understand graphical
•     format.
• Finally explaining the results of the analysis (coursera.org, 2022).

The first three steps of the data analytics Identifying, Collection, and Cleaning are usually known as the Data preparation which means making the data ready for the next step of the process analysis and processing. and sometimes it involves reformatting data, making corrections to data, data combining to supplement the data (Talend, 2022). Data preparation is an important step in the Data analytics process. here some of its benefits:

v   Identifying and fixing the errors before the data are removed from its original source as it became much difficult to deal with these errors after that (Talend, 2022).

v   Cleaning and reformatting and other steps of the data preparation ensures that the best quality of data that are been analysed. (Talend, 2022).

v   Get the best business decisions more data quality leads to more high-quality business decisions. (Talend, 2022).

Generally we differ tow general types of Data, without getting deeply in the different data types:

v   Quantitative Data: which means that it is countable data such as numbers, prices, attendance numbers, and different measures like heights and weights (Forbes, 2022).

v  Qualitative Data: which means that kind of information we can't count, however it is about what people think or feel about a gadget or something else. For instance, feedbacks (Forbes, 2022).

However there are several specific sorts of Data much more specific than the Quantitative and the Qualitative data: Big data, Structured, unstructured, semi-structured data, Machine data, Spatiotemporal data, Open data, Dark data, Real time data, Genomics data, Operational data, High-dimensional data, Translitic data (WhatWorks.org, 2022). For instance, we take "Big Data", big data are a massive amount of information That even sometimes can't be loaded into a local storage device (computers memory). This huge size of data are measured by MB ,GB ,TB, ZB, YB and it can be as an image, video, web, or audio, also it is trustworthy and correct and it has five characteristics: (Sandhu, Amanpreet Kaur, 2022).

                                  1.Volume    2.Velocity    3.Variety    4.Value    5. Veracity

 Another important type of Data is KDD or Knowledge Discovery in Databases which is a complicated procedure that identifies and indicates the useful figures and patterns in huge databases and involves a several steps and important decisions made by the users (Goldschmidt, R.R. and Lopes Passos, E.P. and Vellasco, M.M.B.R, 2002). And last but not least The Data visualisation also in another important type in the Data Science Education, and data visualisation tend to link between the use of Graphic visualisation with the purpose to obtain or transmit the information in an easier and faster way using a different techniques and  tools ({Cuadrado-Gallego, Juan J. and Demchenko, Yuri and Losada, Miguel A. and Ormandjieva, Olga, 2021).

References 

J. J. Cuadrado-Gallego, Y. Demchenko, M. A. Losada and O. Ormandjieva, "Classification and Analysis of Techniques and Tools for Data Visualization Teaching," 2021 IEEE Global Engineering Education Conference (EDUCON), 2021, pp. 1593-1599, doi: 10.1109/EDUCON46332.2021.9453917. 

Coursera.org, 2022. [online] Coursera. Available at: <https://www.coursera.org/articles/what-is-data-analysis-with-examples> [Accessed 21 April 2022]. 

Forbes, 2022. The 13 Types Of Data. [online] Forbes. Available at: <https://www.forbes.com/sites/adrianbridgwater/2018/07/05/the-13-types-of-data/?sh=10a41f9b3362> [Accessed 21 April 2022]. 

R. R. Goldschmidt, E. P. Lopes Passos and M. M. B. R. Vellasco, "Assistance in KDD goal definition process," The 2002 International Conference on Control and Automation, 2002. ICCA. Final Program and Book of Abstracts., 2002, pp. 234-235, doi: 10.1109/ICCA.2002.1229783. 

A. K. Sandhu, "Big data with cloud computing: Discussions and challenges," in Big Data Mining and Analytics, vol. 5, no. 1, pp. 32-40, March 2022. 

Talend, 2022. [online] Talend.com. Available at: <https://www.talend.com/resources/what-is-data-preparation/> [Accessed 21 April 2022]. 

Techtarget, 2022. What is Data Analytics? - Definition from WhatIs.com. [online] SearchDataManagement. Available at: <https://www.techtarget.com/searchdatamanagement/definition/data-analytics> [Accessed 21 April 2022]. 

WhatWorks.org, 2022. Data Types | What Works. [online] Whatworks.org.nz. Available at: <https://whatworks.org.nz/data-types/> [Accessed 21 April 2022]. 

P. M. G. Jr., R. S. M. Barros and D. C. L. Vieira, "On the Use of Data Mining Tools for Data Preparation in Classification Problems," 2012 IEEE/ACIS 11th International Conference on Computer and Information Science, 2012, pp. 173-178, doi: 10.1109/ICIS.2012.79.