For Student Researchers - Data Discovery

Overview¶

As a Data Discovery student researcher, you’ll work on a team with a project mentor, using data to understand real-world problems. Through the associated Data Discovery course, you’ll earn course credit for your work on the project and develop professional skills to support your progress.

Timeline¶

Exact program deadlines vary by semester. Typically, Data Discovery applications open to students about 2 weeks before instruction begins. For the most competitive application, apply by the priority deadline, about 1 week before classes start. The application closes during the first week of class. Most project mentors will schedule an interview before making an offer. You can add yourself to the Data Discovery course waitlist once you’ve scheduled an interview. If you accept an offer, you’ll be moved from the waitlist and admitted to the course. Work on the project will end during the last week of class, with a final presentation during RRR week.

Application¶

Max applications: You may apply for up to 10 projects (you may participate in only one).
Application questions: You’ll be asked to answer these three questions in your application:
1. Please describe your relevant core skills, additional skills, and any experience with the topics listed for this project (50-150 words).* Include the context (e.g., coursework, internship, or personal project).
2. What experiences do you bring that would help make this Data Discovery project successful? Consider including examples of times you showed leadership, resourcefulness, or the ability to work independently (max 250 words)*.
3. What do you wish to gain through participating in this project? Your response should address why you’ve chosen this project in particular (max 250 words)*.

Application Advice for Students¶

Overall, mentors look for:¶

Interest in their specific project and eagerness to learn about their topic. Do some research into the mentor’s topic, organization, or research agenda in addition to the project description they provide.
A good fit between your current skillset and the kind of training they’re looking to provide (e.g., domain knowledge vs. technical methods).
Concise, concrete examples that demonstrate (not just name) your skills and experience.
Team players who are willing to do some tedious work.

For a compelling written application:¶

Express what interests you about this project specifically.
Don’t cut and paste from one response to the next without editing and verifying that everything applies.
Keep responses brief and concise. Include only information that the person leading the specific project would be interested in.
Don’t submit applications written by generative AI.
Take the time to customize your short answer questions for each project. (Your profile will be repeated across applications.)

During the interview:¶

Be succinct and provide concrete examples with your responses.
Try to include examples outside of class projects or assignments.
Interviews are also used for fact checking: You should be able to back up what you said on paper.
Ask good questions about the topic of the project.

Skills mentors look for:¶

“Soft skills” like collaboration, perseverance, and flexibility can be even more important than technical skills. Provide specific examples of how you’ve embodied these skills–just including them on your resume doesn’t actually say a lot.
Being able to demonstrate times you’ve worked on a team is especially useful.
Many mentors are eager to fill you in on their domain or subject, but expect you to come in with a solid grasp of the tools.

Things to know before applying to Data Discovery and choosing a project:¶

Your commitment to the project is for the duration of the program. Be realistic about your other commitments this semester. You’re committing to your mentor, your teammates, and to the Data Discovery program and must maintain these responsibilities.
Working with real-world data involves tedious tasks. Do not apply if you aren’t prepared to grind.
Be prepared to work as a member of a team. Not everyone will be the leader or the star.
Not all tasks are glamorous, and you may not end the project with a flashy or innovative finding. Research can be a slow, iterative process. Your contributions to the project will still be valuable, and this authentic experience will teach you a lot about how research gets done.

Career Engagement Resources¶

Preparing for interviews: https://career.berkeley.edu/prepare-for-success/interviewing/interview-preparation/
Big Interview: https://berkeley.biginterview.com/
Writing a resume: https://career.berkeley.edu/prepare-for-success/resumes/