🗳️ Course Representative

2025/26 Autumn Term

We are recruiting a Course Representative for DS105A (2025/26).

📑 Brief

Why become a course rep?

1. Course reps directly impact the student experience at the DSI. You’ll attend official meetings with DSI staff to voice your suggestions, concerns, and recommendations.

Previous DS105 reps significantly improved the course, influencing lab sessions, assignment timing, lecture content, and course structure. It was their feedback that inspired us to split the course into two half-unit courses, DS105 and DS205

2. Enjoy perks like guaranteed spots on upcoming DSI Industry Trips!

3. Build a strong profile for the Henry Wynn Prize, which recognises outstanding collegiality and engagement beyond the classroom.

The Process

The Data Science Institute (DSI) is excited to announce that the elections for Course Representatives for DS105A are now open!

If you are taking DS105A this term, you can nominate yourself, and all students get to vote anonymously on the candidates. The nomination and voting process will be conducted via the #social channel on Slack.

As a Course Representative, you’ll work with staff to ensure your peers’ feedback is seen and acted on and that student voices are represented in institutional decision-making. It is also a fantastic way to develop new skills, to know the Institute better, and bolster your CV.

You can see the positions available in the table below:

Course	Number of Positions
DS105A	1

📝 Commitments

What can you expect from this role:

🏅 Engagement and Perks

• You get a guaranteed place in the upcoming DSI Industry Trips!
• You will have a direct channel of communication to voice suggestions, concerns, and recommendations to the teaching and administrative staff at the DSI.
• You build a strong foundation for the Henry Wynn Prize, a £250 annual award recognising student excellence, collegiality, and engagement beyond the classroom.

Teaching Committees

As a course rep, you will be invited to attend one or two Teaching Committees ¹ at the DSI during Autumn Term:

• Week 07 (exact date to be confirmed)
• Week 10 (exact date to be confirmed)

Plus, some informal coffee chats with our staff.

🧑 Why nominate yourself?

• You will play a significant role in making a difference and improving the academic experience for yourself and your peers
• You will build strong interpersonal skills, including confidence, effective communication, and dealing with and resolving complex issues
• You will get to know your classmates much better, as well as senior staff in your department and around the university
• You demonstrate the collegiality and external engagement valued by the Henry Wynn Prize

🗳️ The Election Process

There are two stages to this election:

1. Nominations

• Write down a short paragraph (max. ~50 words) explaining why you feel you would be great for the role!
• Head to your Slack group and post your short paragraph on the #social channel.
• You can nominate yourself anytime but no later than Tuesday, 21 October 2025, 9.00 p.m..

2. Voting

• We will set up an (anonymous) poll via Mentimeter during the W04 lecture.

💬 Q & A

Q: What if no one nominates themselves?

A: We will be sad. But oh well, what can we do?

Q: What if there is a tie?

A: We will do a swift run-off election within a day.

Q: Can I nominate myself to represent more than one course?

A: We love the enthusiasm! But, no. If you are taking multiple DS courses this Autumn Term, say DS101A and/or DS202A, you can only take the course rep role in one course.

🏆 About the Henry Wynn Prize

The Henry Wynn Prize is an annual prize (£250) recognising student excellence, expertise, collegiality and engagement beyond the classroom. It is awarded annually to an undergraduate student who has completed one of the DSI’s module options.

The prize recognises excellence and is awarded to a high-performing student who has demonstrated an outstanding application of domain expertise to data science. The recipient must have not only excelled within the taught curriculum but also shown outstanding initiative in applying their knowledge beyond the classroom, contributing in a meaningful way to the wider data science community at LSE.

Key selection criteria include:

• Domain Expertise: Clear and effective application of domain expertise from your field of study, showcasing how your knowledge integrates with data science
• Collegiality: Actively fostering a sense of community among peers, consistently engaging in course-related communication channels and beyond
• External Engagement: Engagement with data science beyond the taught course

Being a course representative is an excellent way to demonstrate the collegiality and external engagement valued by this award.

Footnotes

1. Teaching Committees are official meetings where academic and professional service staff at the DSI discuss how the courses are going, devise adjustments for what is not going as planned, and plan new ways to boost recognition of innovative work of students enrolled in DSI courses. These meetings typically involve Lecturers, the Teaching Support Officer, the Communications Officer, the Institute Manager, and the Director of the DSI.