Statement of Purpose: Please describe your aptitude and motivation for graduate study in Data Science, including your preparation for this field of study and your future career goals. Please be specific about why the U program would be a good intellectual fit for you.

Short Answer: Discuss a time that you made a decision based on data. How did the data influence a good or bad decision making process? Make sure to choose an example and to describe it in such a way that clearly illustrates your analytical and problem-solving abilities. Limit your response to 300 words.

2 statements of purpose are needed. 500 each

They did not give a limit for UC , but for S, which asked the same question, that one said limited to 500 words

Statistical and data analytics is my passion and strength. I really love using data visualization tools such as Tableau and Power BI where I am able to tell a story to my audience based on the dashboards I create. Statistical analysis has also been something I enjoy doing as I am able to do various linear regression tests and develop conclusions based on the leveraged dataset. Finding a career path was difficult, but taking AP Statistics in 11th grade inspired me to get into the field of data analytics. There were many concepts that were taught such as sampling distribution, hypothesis testing, confidence intervals, and linear regression tests which are essential in the workforce where businesses use data to make the best possible decisions. Pursuing an online masters degree in data science at U will allow me to enhance my skills in data mining, data visualization, statistical analysis, and machine learning. In five years, I want to solidify my position as a data analyst or data scientist for a fast-growing company that builds next-generation solutions with a purpose. This program is a great opportunity for me to climb that ladder in the field of data science and I am ready for any challenge that comes my way.

For a statistical analysis class, I analyzed a dataset from 2019/2020 NFL to see whether height and weight contributed to quarterback success. The three dependent variables that were studied were: Completion percentage, touchdown percentage, and quarterback rating. Data were plotted on a scatter plot using a simple linear regression model to look at the correlation between the quantitative and categorical variables. The key component to this model was to see how it fits the data based on how close the data was fitted to the regression line and the r-value (coefficient of determination). The sample consisted of only thirty-six players since we only wanted to use quarterbacks that threw 100 or more completions. Since the r value was low, it was determined that height and weight were not prominent factors in determining quarterback success. However, every statistical test has room for error. For example, I didnt look at a teams offensive line rating and a good offensive line helps protect the quarterback and gives him time to throw. Another factor that wasn’t taken into account was a teams running game. Having a good running game allows an offense to be more balanced and unpredictable. Age was also not looked into when determining quarterback success because it is natural for older quarterbacks to not be as quick and agile as they were in earlier years. There are also other intangible factors such as work ethic and having proper leadership skills. These intangible factors are further proof that physical attributes alone do not contribute to quarterback success.