Why Data Science?

Hey Curtis! I heard you left your PhD program?! What happened? What made you want to do that? What are you up to now?

Data Science?! What is data science? What made you want to shift to that?

I’ve been asked these questions by a lot of people over the last couple months: friends, family members, previous classmates and instructors, and more and more. Even the instructors and my classmates in the Data Science Immersive that I’m nearing the end of have asked. So to all who have asked and to anyone here are the answers:

1. Why did you leave your Ph.D. program?
   A: There isn’t really a simple answer to this, but the simplest answer is that after dealing with the pain and grief of my mother’s passing in December of 2020 I didn’t want to go back to being a Doctoral Student. I talked with my advisor and after I realized I still had around 2 more years to finish the Ph.D. I decided I didn’t want to continue on that path. Learning that my partner and I were to welcoming our first child into the world in 2022 lit a bit of fire under me to find a new career path as well.
2. What is Data Science?
   A: Data science is a field that is often described as the intersection of computer science and statistics. A good data scientist typically has a broader understanding of computer science and coding (Python, R, SQL, etc.) than a statistician, and more knowledge of statistics and modeling than that of a typical computer scientist. They also have to be able to translate the results and output into a format that their audience and stakeholders can understand and then use to assist in their solving or answering their problems. Most data scientists use machine learning models to help them answer various questions and solve problems. A model is essentially a simplification of reality. As George Box said in 1987 “Essentially, all models are wrong, but some are useful.”
3. What is Machine Learning?
   A: Machine Learning refers to a process that allows a computer to learn without needing external programming or direction. A Machine Learning model then could be said to be a model that a computer learns, that attempts to simplify reality (using the data it is provided with), and outputs a model that can be used to answer a question or solve a problem. Machine learning is broken into supervised learning: models in which we have prior knowledge of what our outputs should be (price or sales data, etc.) and unsupervised learning: models where we don’t have a defined output and desire to examine how our data is structured or clustered. For more insights on Supervised and Unsupervised learning I recommend Devin Soni’s article here.
4. Why Data Science?
   A: Data science felt like a natural next step from the work and experiences that I had had as a graduate student and psychological researcher. I have taken a number of statistics, research methods, and measurement courses during my graduate study and felt that transitioning into a field that used data to inform or drive decision-making was fairly similar to how researchers use prior results to drive future projects.
5. I stumbled upon your blog from the internet/Medium and have no idea who you are, or why I should care. (Why Should I care?):
   A: You don’t have to care, but if you made it this far I’d like to think you do care or are at least interested. If that’s true, I’d ask that you stick around to learn more about me and my journey.
6. I stumbled upon your blog from the internet/Medium and have no idea who you are, or why I should care. (Who are you?):
   A: Hello! I’m Curtis M. HopeHill, what follows are my professional credentials and experiences. I have a B.A. in Psychology, a Master’s in Educational Psychology, and completed 40/60 credits towards a Ph.D. in Educational Psychology prior to leaving my doctoral program. I have over 8 years of experience conducting quantitative research in a university setting, the majority of which focused on investigating the impacts of stress and anxiety on college students; as well as methods to ameliorate the causes of stress and anxiety and dampen their impacts. I spent two years teaching introductory psychology as a graduate instructor and spent 3.5 years of working as a research assistant on topics ranging from: the impact of childhood trauma on college students, obesity and optimism bias, conducting a program review of our undergraduate psychology program, and most recently working on a geoscience education grant aimed at improving geoscience students’ math skills and affective skills. Whenever I was able to self-select my work or research I always looked for a role or project that I hoped would have a positive impact down the line. Unfortunately outside of rare moments teaching introductory psychology, I rarely saw any relevant impact from the projects I worked on. This isn’t to say they didn’t have an impact, or were wastes of time, but simply to illustrate that the ripples were often small and not immediately felt.
   This last part is the more complex answer to why I left my Ph.D. program. Studying and working towards a doctorate is exhausting, stressful, and very often overwhelming. You are asked to juggle multiple important tasks all at the same time, while being paid very little money for the amount of work you are truly doing. There are many motivators that can get someone through the daunting gauntlet that is graduate school. I came to realize in my final semesters that the biggest motivators for me were impact and purpose, these were the reasons I had gone to graduate school; and I realized that these were two things I no longer had.
   So I left the program. I took time for myself to mourn, to process, to learn and grow. I realized that I still wanted to find a job where I could have purposeful work and make an impact. I recalled a course on Data-Analysis for Decision-Making, a sort of primer on how to be a statistical consultant and work with real, live, and incomplete data. I kept seeing Data Science on a number of the job boards I was looking at this past summer. I started learning more about data science and analytics and realized it seemed a natural next step given my statistical background and research experience. Fast forward a couple weeks and I had applied and been accepted into one of General Assembly’s Data Science Immersives.

Final Thoughts:
This is the beginning of my journey as a data scientist and as a writer/blogger. I’ll be writing more in the coming day, weeks, and months on topics both personal and professional. If you are interested in following along with me on this journey please feel free to follow my medium account. Thank you for your time!

Curtis M. Hope Hill