My Statistics Degree: A great start, but not the whole story
I had to learn many valuable skills from actual work experience
When you are a student in university, you may think that your education ends when you graduate. However, that certainly was not true for me. I have a Master of Science degree in statistics from the University of Toronto (a top-notch school overall and in this particular subject), but I had to learn many valuable skills and concepts in my jobs after academia. Here is a partial list:
Decision trees
Random forests
Propensity scores
Gradient boosting
SQL programming
Simpson’s paradox
Precision and recall
Sensitivity and specificity
Offsets in Poisson regression
Fisher’s test of independence
Chi-squared test of independence
Positive and negative predictive value
Receiver operating characteristic (ROC) curves
As a statistician, I have used all of these tools to some extent in my work experience. Some of them (like SQL) are incredibly basic or widespread, but I never even heard of them during my academic studies.
Learning is a lifelong process1. Embrace this journey, and you will open all kinds of doors for your professional growth.
What are some technical skills that you learned outside of your classroom that have proven to be valuable in your career? Please share in the comments!
I wrote this article after reading Rick Wicklin’s reflections on his 28th anniversary of working at SAS. He advised, “Grow your skills: I was not hired to do statistics, but I soon realized it would be a big career advantage. Keep learning even after your formal schooling ends.” Rick writes a good blog called The DO Loop; it is about SAS programming, statistics, and applied mathematics.