Join the dsTRAIn

As Data Science is an interdisciplinary field, the Data Science TRAIn Lab will actively seek to have lab members with a wide variety of expertise and interests. Successful lab members will be motivated students who are enthusiastic about learning new skills and who are intrigued by the unknown. The lab will consist of members 1) who have a strong interest the fields of data science and machine learning or 2) who have a strong interest in a particular domain (art, music, political science, economics, fashion, etc) and questions arising from that domain that could be answered using data science and machine learning techniques.

Prospective lab members will need to apply to be in the lab. The application will have 3 parts: a written section, an interview, and a reference check. Prospective lab members will be asked to read about and reflect on the purpose of the lab. They will be asked to demonstrate their enthusiasm for learning beyond individual assignments and to discuss what they are excited about exploring in this lab.

Prospective lab members will be asked for references who support their application into the lab. There are two options for submitting references. Option 1: Students submit two references. Option 2: Students submit one reference, attend an open lab meeting, and meet with a current dsTRAIn member.

The lab is not accepting applications at this time.

Lab members will be chosen based on the following guidelines:

1) Motivation beyond assignments

2) Enthusiasm for learning

3) Willingness to try new things and ask questions

4) Strong interest in either or both:

a) The fields of data science and machine learning

b) A particular domain and questions from that domain that can be addressed using data science or machine learning

Lab members who have been accepted to the lab may choose to stay active in the lab as long as they would like. Lab members must commit to the lab at least one week before the start of term. Any lab member who has participated in the lab for at least one term and who chooses to become inactive for at most 2 terms does not need to reapply to the lab, but longer periods of inactivity will require reapplication into the lab.

Being on the dsTRAIn

The Data Science TRAIn Lab will meet once per week during term for 2 hours. Lab members will participate in a discussion about the paper chosen for that meeting and actively participate in choosing the next paper. Additionally, lab members will present updates on their individual projects, solicit help and ideas from others, and constructively offer suggestions to other lab members regarding projects. Lastly, we will use lab meetings as an opportunity to continue making progress on lab projects.

All Data Science TRAIn Lab members are expected to spend between 1 and 3 hours per week preparing for each lab meeting. Each lab member should be prepared to discuss the weekly paper and should spend some time on any lab project that is of interest. Recognizing that reading papers is a learned skill, we will spend the first few meetings of term working through one paper and will make use of reading guides and short discussions.

For their active and consistent participation in the Data Science TRAIn Lab, students can either earn work-study hours (up to 5 hours per week) or receive 1-credit hour. If a lab member is no longer actively and consistently participating in lab meetings, they will be excused from the lab for the remainder of the term. Previous lab members who have been excused once are welcome to reapply to the lab for following terms. Lab members who have been excused from lab more than one term are not eligible to reapply into the lab.

Frequently Asked Questions

  • I have never done anything with Data Science, Machine Learning, or Computer Science and the last math class I took was in high school. Can I apply to be part of the lab?

    Absolutely. We do not choose lab members based on what classes they have or have not taken. Instead, we choose lab members based on who would be the best fit for the lab and the lab’s purpose. Our lab seeks to have a lot of perspectives and levels of expertise. We need people who are more experienced with machine learning techniques as much as we need people who are passionate about a particular domain and have the expertise to help our lab address the right questions. This expertise may translate into a prospective lab members having taken courses in a variety of areas.

  • I do not know how to program. Is this something I can learn as part of my lab work?

    Yes! Learning to program falls directly under the “Try” part of the TRAIn acronym (and less directly under RAIn parts). There are many great resources for learning to program that the lab would be happy to introduce you to. However, learning to program as part of your lab work should not supercede preparing for lab discussions on weekly readings or other lab project work.

  • At this time, I do not think that I will choose a career in Data Science, but I am intrigued to learn more about the area. Should I apply to be part of this lab?

    You are the only person who can decide if you are interested enough to apply to the Data Science TRAIn Lab. Choosing a career in Data Science is not a requirement for applying to or joining this lab. However, you should be interested in the kinds of questions that data science can help address and be excited about the prospect of learning more about data science and machine learning.

    Being part of the Data Science TRAIn Lab will help you hone skills that will be useful in any profession or career you choose after Macalester. In the the Data Science TRAIn Lab, we are using the TRAIn philosophy to guide our lab work, meaning that our work will be approached by Trying new things, Reading papers in and beyond our areas of expertise, Asking questions with known and not yet known answers, and Incorporating our new knowledge into expertise to address the questions with not yet known answers. Additionally, by working in an interdisciplinary environment, you will also be honing your communication skills.

  • If I am accepted into the Data Science TRAIn Lab, do I have to participate?

    Not necessarily. You must commit to the lab at least one week before the start of term. By committing to the Data Science TRAIn Lab, you are committing to preparing for, attending, and actively participating in weekly lab meetings. You also will be expected to actively participate in other lab projects. Your commitment should be no more than 5 hours per week.

    If you are accepted into the lab and choose not to participate for that term, you will need to reapply to the lab. However, if you are accepted into the lab and participate for at least one full term, you can choose to become an inactive member for up to 2 terms without needing to reapply into the lab. Longer periods of inactivity will require reapplication.

  • If I do not have a work-study award, can I be paid to be part of the lab?

    No. This lab offers the option that you can receive academic credit or can earn work-study hours. Simply put, the goal of this lab to complement your curricular experience at Macalester. It should be treated as an additional curricular experience and should not detract from your primary reason for attending Macalester. Additionally, we feel that work-study supports curricular development and thus students with work-study awards should be given the choice to earn work-study hours through their lab membership as this lab teaches students a new way to work and study.

  • My friend is in the lab. If I choose to apply and use Option 2 for the references, can my friend be the person that I meet with about dsTRAIn?

    Possibly. dsTRAIn has a conflict of interest policy for the application process. This policy simply means that a lab member may recommend another lab member to serve as your reference. There are a number of reasons why a lab member may make this recommendation, from the closeness of your relationship to wanting to introduce you to another lab member with similar research interests to you.

  • I get why students would want to be part of this lab, but why are you interested in leading the Data Science TRAIn Lab?

    Being part of the Data Science TRAIn Lab allows me a venue to combine both my teaching and research interests. I love Data Science and the kinds of questions that it can answer. I find the prospect of uncovering trends in subject fields like art, music, and fashion exciting and I want to share my excitement with Macalester students. Additionally, I view the TRAIn philosophy as a great method to help Macalester students try out what being a graduate student might be like. Furthermore, I want to actively engage with the current research being done that is not directly related to my own research.