REPRESENTATION OF DATA SET

Select one (or more) sets of data and find out what kind of questions the data set should answer. When publishing,
Identify the name of the particular collection of data,
Distribution of world wealth.
Describe the data forms and problems addressed by them.
The website gathers and tracks data for low-income countries, developing countries and transition countries on income disparities. The data reveals how the income distribution has evolved over the years. The data is represented in a graph that shows the income distribution of different countries in the world.
The site provides a database that analyses income inequality from 1962 to 2015, how income distribution has been changing over time, some countries Lorenz curve have moved towards the line of equality, which means that the income distribution has improved in those countries, while in some countries, the Lorenz curve has moved further away from the line of equality, hence their income distribution has deteriorated.The data has also been visualized perfectly, they have provided a very interactive visualization tool. (4) Give two types of research questions that might be explored using the data.a. What is the correlation between income distribution and the per capita income of the countries within the data sets??b. How can income inequality be improved in the developing countries to be the same as the developed countries??(1) Give the titles of the videos you watched, then add at least 3 comments on what you found was especially interesting and important about the data representation techniques shown and how these techniques could enrich and enliven how we present data about human behavior.a. The best stats you’ve ever seen.One of the most interesting things in this video is how the data is being represented. For instance, he begins by representing the fertility rates in different countries in a graph where the countries are represented by the circles, with the size of the circle depicting the population of the country. This presentation helped kill the fallacy that the world is divided into two, we and them. With we representing the western developed countries with small families and long life, while them depicting large families and short life. It was discovered that the trends have changed from the 1960s to today where the size of the family has changed in developing countries .so the world is not so much of we and them, the present data sets tends to disagree.Secondly, another interesting fact about the video is how he is representing the world income distribution. Showing how that the few richest people owned most of the world with the most of the people owning less in the 1980s, however the trend has slightly changed, especially in Asia, more people have crossed from poverty as the years goes by with most of the people going toward the middle class. The data has been presented in charts that shows how the income distribution changes from one year to the next.b. The beauty of data visualization.What’s interesting in this video is that the significance of visualization is explained. Visualization is used to find the connection between data. You might have a wide data load such that you can’t make sense out of it, visualization would help you get the relation between different data sets. Visualizing helps get the pattern within the data hence deriving information. From the video, visualization has been useful in coming up with the billion dollar O-gram. It’s a visualization tool that uses colored boxes to explain data set representation.In summary therefore, the videos are well presented and we can learn a lot from the presentations of data in order to get meaning and patterns out of the data sets.ReferencesBibliography. (2016). Big Data, Open Data and Data Development, 109-120. doi:10.1002/9781119285199.biblioDaWaK (Conference), In Bellatreche, L., & In Mohania, M. (2014). Data warehousing and knowledge discovery: 16th International Conference, DaWaK 2014, Munich, Germany, September 2-4, 2014. Proceedings. MLDM (Conference), & In Perner, P. (2015). Machine learning and data mining in pattern recognition: 11th International Conference, MLDM 2015, Hamburg, Germany, July 20-21, 2015, Proceedings. Spencer, H. (2017). The data of ethics. Yao, Y., Hu, Q., Yu, H., & Grzymala-Busse, J. W. (2015). Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings. Cham: Springer International Publishing.