In Jahan and Rosling’s speeches, they each discuss a variety of human development aspects, but in very different ways. The main concepts from each were similar, but they were presented in unique ways. In Jahan’s speech, he emphasized how human development is for, by and of he people, and he focused mostly on the people that go into data science, rather than the data science behind human developmet. In Rosling’s speech, he emphasizes how important data science was to human development, and how while the use of data science was impactful, it would only be helpful if it is publicly available. They both spoke of the humans behind data science, and how we shouldn’t get caught up in the technology. We need to focus on using these new techniques to help those who need it. He dicusses problems and gives a unique way to measure all kinds of work using data science: the focus measure. The focus measure focuses mostly on having a long healthy life, knowledge and a decent standard of living. He describes fully how these measures can help us narrow in on using data science to more quickly improve human development. On the other hand, Rosling gives visuals to describe his speech, using actual data science, and not just giving the measure and his thoughts on what needs to be better included in the improvement on data science. Rosling gives the example of fertility and life, and income. He does this by using clear visuals created by data science to show how life span has improved from many countries from 1964. Through using this tool, he is able to quantify the data he speaks of, and demonstrate how the life span or the income level has risen through the years. Overall, I feel like Jahan does a better job of informing the areas where data science needs to be improved, and Rosling does a better job of using data science to explain how specific areas are improving.
Both men give specific examples, and these examples do a very good job of demonstrating how data science can and should be used to quantify the areas of the world that could use a bit more quantified data, such as areas that have been affected by a tragedy, or those in low-income countries. In Jahan’s example of work and labor, he explains how women are to watch and take care of the children, clean, cook, and do the other jobs around the house, but it is not seen as “work”, per se. Another example he gives is the arts. Jahan describes how fruitful the arts can make our life, yet the process of creating isn’t always seen as work. Musicians, artists, writers, and more aren’t always seen as a part of the labor force. Jahan looks to create a way to actually include all of the work done by those around the world. This time including the jobs with wages, and the labor done without a paycheck. Instead of focusing solely on the economy, Jahan looks to focus on the “richness of life.” In a similar way, Rosling compares the health and wealth of those in lesser developed countries. He looks at the clear connection between having big families and shorter life expectancies, versus the smaller families and higher life expectancies in more developed countries. He uses data science to show how the richness of life has gone up for many third world countries since 1962. Thye both are looking at the fullness of life, but Jahan presents the countries that may need more help having that rich life, whereas Rosling describes how lives have been enriched.
Jahan and Rosling display clear and consise examples of how data science can capture each and every persons life, and translate it into a number that can help data analysts figure out how resources and time can be best spent. The examples used in their speeches show how human development can be improved, and they specify the types of areas that need more improvement. The overlying picture was most about how to not get caught up in the numbers, and focus on using the data and technology in a way where those who need help can have quicker access to it.