Think about an area of study or field you care about (outside of your formal assignment topic), maybe the major you intend to choose. Based on what you read and know about data science and its related areas (data analysis, stats, and ML), briefly (1-2 paragraphs) write about how you think these disciplines could possibly be used in your field (it’s okay to think ambitiously). If methods are already being used, what are they and to what extent (if you know)? If you are a prospective data science major, what ideas do you have for how data science could be used in ways you believe they are currently not? 

I’m intending to major in kinesiology on the pre-med or pre-PA track. A class I am taking is health-related exercise prescriptions, and we are making exercise plans for any and all people, especially those who are inactive trying to become active or have cardiovascular conditions. I think data is gathered about the health of a person and it is analyzed to figure out if they have risk factors, and to figure out where their exercise plan needs to start based on their baseline information. Statistics is then used to see what kinds of progress they have made in their workouts and the trends in their numbers from the baseline to where they are now. Lastly, machine learning is used by putting in the person’s statistics and using that to predict where this person can go in terms of exercise and setting goals for how to improve. In the field of medicine, data science is very important, especially in terms of vaccines, which is what I’m looking at for my project. It is also important in looking at the spread of disease, for example COVID. Data analysis look at the data of the number of cases in proportion to the population and give honest insights to what the data means. In the article it mentioned that the data from the US of number of cases in proportion to population needed to be compared to other developed countries to show how bad of a job the US did with the pandemic and how many more cases the US experienced than other countries. Statistics then uses the data to apply probability and use math to make causal claims. With COVID data, you could look at the different areas where there were many cases or many deaths, and use that data to create algorithms to show why there were more deaths in one area or one age group, than another. Lastly, machine learning takes the variables and makes models that work well and are simple. It uses the models to make predictions. For the COVID example, machine learning could figure out where there were the most amount of cases that led to death and using the why that happened from statistics, make a model to predict where or what population is most likely to experience the most deaths due to COVID. This information can then be used to allocate resources and make sure those most at risk are protected.