Life After Graduation
What can I do with a Degree in Statistics
With an undergraduate degree in Statistics, you can get a job, go to graduate school in Statistics, go to graduate school in a field other than Statistics, or do something else. The last alternative is possibly the most interesting, but beyond the scope of this document. Statistics students who plan on graduate study in a non-statistical discipline probably know how Statistics fits into their plans. Here, we will deal with the first two options.
Get a Job
With an undergraduate degree in Statistics, you are a more attractive candidate for jobs that require understanding of statistical information. For example, in an industrial setting you will be able to readily understand information based on statistical quality control, even though you may not have a course in it. In a management or sales position in an insurance company, you will be able to understand actuarial data, even though you are not an actuary. In a marketing or market research setting, lots of the data come from sample surveys and are subjected to standard statistical analyses. You will be able to understand and use these data, even though you may not be crunching the numbers yourself.
A degree in Statistics is also an advantage to Computer Science students. In many companies, statistical analysis is an important category of computing; a programmer/analyst who knows what the software is trying to accomplish is more valuable than someone who just knows the hardware and the operating system. All other things being equal, job applicants who appear to know some statistics have an advantage in almost every field. A degree in Statistics makes it appear that you know something about it, even though a degree in something is neither a necessary nor a sufficient condition for being an expert in it.
On the other hand, an undergraduate degree in Statistics — even a specialist degree — does not qualify you as a professional statistician. You may wind up doing statistical analyses for a living, depending on the type of company or organization you're in, but you probably will not be hired in that capacity initially. It happens, but in our experience it is rare.
Let's face it; with a degree in History, are you qualified as a professional historian? With a degree in Mathematics, are you going to be hired as a mathematician? With a degree in Physics, are you going to get a job as a physicist? With a bachelor's degree in Statistics, you should not count on being employed as a statistician. You will probably be hired as something else.
In fields like History, Mathematics and Physics you need a PhD in order to be taken seriously as a professional. In fields like Engineering and Computer Science, an undergraduate degree is just fine. Statistics is in a sort of intermediate category. A master's degree qualifies you to do applied statistics for a living in a medical, business or government setting. A PhD is an extremely powerful credential for applied jobs, and also allows you to seek university faculty positions. It is possible to be a university teacher with a master's degree, but it is somewhat rare.
Go to Graduate School
If you really like Statistics and you're sure that's what you want to do for a living, you should consider graduate study. The Specialist program at UTM is designed as a preparation for graduate school, but a degree in Statistics is not absolutely necessary for admission at most schools. What you need is at least a few Statistics courses (STA257H, 261H and 302H as a minimum), as much Mathematics as possible, and a high cumulative grade point average.
Here are some guidelines about what grades you need.
- If your cumulative GPA is 3.5 or above (and you've taken a lot of Math), you're golden. Start the application process in the fall of your last undergraduate year; this way you will be eligible for financial aid.
- If your cumulative GPA is between 3.0 and 3.5, you may or may not be accepted. It will help if your poorer grades came very early in your university career, and if they were not in Math, Statistics or Computer Science. Strong letters of recommendation may help too, particularly if they are written by individuals known to the the people reviewing your application. Note, however, that most professors are much more restrained when writing to people they know personally. In any case, you should apply to several schools, because you may not be accepted at your first one or two choices.
- If your cumulative GPA is much below 3.0, you can still go to graduate school, but you need to be persistent and flexible. You also need to be willing to study in the United States. In the United States, it is possible to get into many reasonable master's programs with a C or C+ average. They are hard up for students. Of course there is some inconvenience involved in getting a foreign student visa and so on, but think of all the time you have saved by not studying!
Generally, a master's program should take 1.5 to 2 years, and a PhD program is expected to take 4 or 5 years (2 or 3 if you get a master's degree first). The specialist program at UTM is designed as a preparation for master's level study.
Let's be clear about one point. We are not talking here about what you know, or what you are truly qualified to do. We are talking about the perceptions of potential employers. The material taught in our 300- and 400-level courses has a large overlap with what is taught in Master's level courses, both here and in other schools. In theory, the topics are covered at a more advanced level in graduate school. This tends to be true at U of T, and is less true at some other schools. When you are done, you may or may not know any more than you did at the end of your undergraduate studies, but your career earnings should be significantly higher. You will get more respect, and the work will be rewarding, if you like analyzing data. You'll need about a year of on-the-job experience before you are actually as qualified as you appear to be, but that's no problem. It's true in any field. In any respectable master's degree program, there will be at least a one year course in mathematical statistics (same material as STA257 and STA261 but deeper and faster), in which your brains are pulverized and poured back into your ear. This is thought to be a beneficial experience.
At the University of Toronto, there are MSc and PhD programs in both the departments of Statistics and Biostatistics. The Statistics department here is widely believed to be the best in Canada. The Biostatistics department is really a Statistics department (and a good one), but it is affiliated with the medical school, it does not offer any undergraduate courses, and it tends to focus on statistical problems that appear in biomedical research. The Biostatistics department's policy is to send their students to the Statistics department to take basic courses. So apart from courses in survival analysis and categorical data analysis, they tend to offer mostly research seminars in specialized topics. Both the Statistics and Biostatistics departments are interested primarily in producing a few good PhDs.
Many PhDs in Statistics go into the private, public and medical sectors as applied statisticians on the fast track. Their training, however, is in statistical research. In Statistics, as in all other disciplines, PhD students learn to create new knowledge in their field, to formulate important new questions and answer them. Every theorem and statistical technique you learn is the result of somebody's research. Researchers in Statistics mostly are professors at universities, and their students tend to view them exclusively as teachers. In fact, at U of T the typical professor's job description consists of 40 per cent teaching, 40 per cent research and 20 per cent administrative duties and community service. This is true in most fields, not just Statistics.
For a PhD in Statistics, what you need more than anything else is a solid mathematical background. This is why we have increased the mathematical requirements of the specialist program. Basic Calculus is certainly not enough for doctoral study in Statistics. Real Analysis, Complex Variables and Topology are very helpful content areas. Learn how to do proofs! This is the main way that knowledge is created in the mathematical disciplines, and what a PhD says is that you know how to create new knowledge.
One other thing you should be aware of is that after the first year or so, taking courses and doing well is not the main point. Research training is primarily a process of apprenticeship. You find somebody who is doing something interesting (or he/she finds you), and the person trains you one on one. Probably the most important choice a PhD student makes is whom to work with. Almost any professor will tell you more than you want to know on this topic, if given half a chance.