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🧐 Student FAQ
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🧐 Student FAQ

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Table of contents

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Will show this page in Spring 2023, when prospective students are enrolling.

How do I know what projects will look like in a domain?

The domains we've listed (click the link in the top right corner of the page) vary specificity; different mentors may have different visions for how much flexibility those in their domain have in determining their project.

When you choose an employer after graduation, the precise nature of your work will be unknown, and may change depending on the circumstances. Generally, your choice depend on your compatibility with the general area in question. Take the same perspective here; you won't know precisely the topic, only the general area. If you would like to know more about a specific area, look up the mentor's research page or take a look at previous Capstone projects they have mentored, if any (the 💾 Archive page is good for this).

What should I consider when deciding on a domain?

When considering domains of study, you should consider:

  • Whether you prefer a class focused on methods, systems, or an applied domain.
  • Whether any specialized knowledge will be highly leveraged in the domain. While all domains are open to all students who meet the prerequisites, the process of "getting up-to-speed" in the domain during Quarter 1 will require more work if you have no exposure to the specialized knowledge.

Beyond that, really all domains cover interesting questions that offer a great opportunity for two quarters of research!

What if I can't sign up for my prefered domain?

Being a data scientist requires getting up to speed in an area you may not know. Really, any of these areas are quite interesting, once you get into the problem! Please be open-minded about your area of choice 😊; remember that in whatever domain you end up in, you will be bringing your strong quantitative skillset.

Furthermore, not everyone realized that the problems that may seem mundane are often more interesting, more realistic to 'real-world' problems a data scientist might tackle, and yield more interesting results. A project on a trendy topic runs the risk of coming across like a cursory data science blog, as oppossed to the mature project this has the potential to be.

What should I consider when choosing a domain (mentor)?

When considering potential domain mentors, be aware that they will be acting in very different capacities than a course instructor. Here are a few points to consider:

  • The biggest factor in a successful project is personalized attention and a small class. You should prefer to enroll in domains with smaller enrollment, even if the you think you might like a different mentor/subject more.
  • The skillset required for mentoring relies more on expertise in an area and less on the track record of an course instructor.
  • You mentor may be a source of letters of recommendation in the future. As such, research faculty leading small sections give you the best opportunity for future opportunities!

List of domains by section

For a listing of current domains, click the first link in the top right corner of this site.