Recently, I participated in an email interview about what being a Statistics major entailed, how I got interested in the field and the future of Statistics. I figured this might be of interest to those that are contemplating majoring in Statistics, or considering a career in Data Science.
A: “When I was a kid, I really enjoyed looking at graphs, plots and maps. My parents and I could not make of what was behind the interest. At the same time, I was also heavily interested in education. My mother was a teacher and the first set of statistics I ever encountered were standardized test scores. I strived to understand what the scores attempted to say about me, and why such scores and tests are so trustworthy. When the stakes increased with the AP and SAT exams, I began reading articles published by the Educational Testing Service and learned a ton about how these tests are constructed to minimize bias, and how scores are comparable across forms. It fascinated me how much science goes into these tests, but in the end of the day they are still just one factor in the whole picture of a student. This niche interest lead me to statistics, psychometrics in particular, and although I no longer study psychometrics, I found what I learned to be incredibly valuable.”
Q2: “I noticed you have bachelor’s, master’s, and doctoral degrees in statistics. How did your graduate study build on what you learned in your undergraduate program?”
A: “For me, the undergraduate and graduate programs were night and day. The undergraduate program focused more on modeling and data analysis. The graduate program focused more on thinking about data and how to develop a scientific “common sense” about how to work with, express and make automated decisions based on data. The graduate program was much more mathematically and computationally intensive than the undergraduate major. My graduate study actually built more on my mathematics major in college because many of the concepts in graduate statistics require knowledge of linear algebra, numerical analysis and real analysis. Fortunately, our statistics major requires upper division math courses.”
Q3: What was the most interesting part of majoring in statistics? What did you find most challenging?
A: “The most interesting part of majoring in statistics was seeing how many fields can grow and transform based on insights from data and statistics. In my case, I found it most interesting seeing how it integrates and interacts with computer science. Every time someone surfs through Facebook, enters a Google search, or looks at an item on Amazon, data about what you are doing are collected and algorithms process this data to enrich the experience by, say,recommending books and offering special deals on Amazon, recommending friends and showing relevant stories on Facebook, and the most groundbreaking of all: returning relevant search results.
The most challenging part for me was the mathematical theory. Although I loved math, I sometimes had trouble connecting the theory to the application, and statistics is such an applied field. I look at it as a rite of passage and once I saw enough theory relevant to my interests, the learning process became easier.”
Q4: How do you apply what you learned in your statistics education in your current line of work?
A: “It is ironic, but it is the more basic concepts of statistics and probability that I use everyday rather than the complicated models I learned. Concepts such as independence, confidence, power, accuracy etc. are important building blocks for building my own models, or for choosing an existing one from those that I learned in school.
I always start with some exploratory analysis such as computing some statistics and making plots that show relationships clearly. Then I set explicit guidelines for the input and output of the model I want to build and note any critical assumptions that are violated or that must be met. I then try several different methods and models and validate their results using common metrics taught in undergraduate statistics before settling on a final model configuration.”
Q5: What skills did you learn in the statistics major that you find useful for work and everyday life?
A: “The training in mathematics I received as part of the statistics major taught me how to think logically, and this is very important in my work in computer science. I think patience was another very important skill I learned. I love what I do, and sometimes I take for granted that others have the same mathematical training that I do because I am so entrenched in it. Through my experience teaching as well as consulting as a student, I gained a better sense of the challenges and difficulties many people face when thinking about and interpreting statistics and how to better communicate results and ideas.”
Q6: Any advice for students who are considering majoring in statistics?
A: “My advice for students majoring in statistics is to choose an additional major or minor that uses statistics and is of interest to the student. I do not consider statistics to be a “standalone” major. When interviewing for a job, employers want to know why an interviewee is passionate about their company. For example, if interviewing for a finance company, the company wants to hear about passion for finance, and see education or experience in such fields. Another way to accomplish this instead of double majoring is to do some internships, projects or research in a field of interest.”
Conclusion: Finally, could you tell me a little about yourself for an intro bio we will include before the Q&A interview? For instance, what university(ies) did you attend, what degree(s) have you earned, what is your current job title, where do you work and for how long (you can be general here, or include a link to your professional website or blog if you have one), and what are you career goals?
A: “I attended University of California, Los Angeles (UCLA) for my B.S. (Statistics, Mathematics of Computation), two M.S. (Statistics and Computer Science) and Ph.D. (Statistics). I currently work for an Internet advertising startup in Santa Monica, CA as Chief Data Scientist/Research Engineer, and have been working in the field for three years. Whenever I get a free moment, I write about statistics, data mining and computer science topics on my blog at http://www.bytemining.com. I plan on dedicating the rest of my life working with and communicating about data and turning online phenomena into knowledge that can be used to progress technology and change the world!”