About the Physics Cohort
In this cohort of The Data Mine, students will experience an active research environment targeted at the analysis of large data sets from particle physics and astrophysics experiments. Students will be introduced to and employ a variety of tools and programming languages including Python. Research topics revolve around data from the CMS experiment on the Large Hadron Collider in Switzerland, the search for dark matter particles using the XENON experiment in Italy, and the new Vera Rubin telescope facility.
Eligibility
Open to any undergraduate student with an interest in the Data Sciences (first-year students and continuing students are both welcome)
Residential Component
- New Incoming Students: Living in Data Mine residential space is required unless you are in the Honors College or have other special accommodations needed.
- The Housing Reapplication process – Residents can opt into the Housing Reapplication Lottery from October 14 at 8:30 a.m. EDT to October 22 at 5 p.m. EDT in the Housing Portal.
- Your roommate (in most cases) will be a member of The Data Mine.
- If you want to live on campus, it is necessary to complete a housing contract. (Completing a housing contract is a separate process from applying for a learning community.)
Duration
Fall and spring semesters
Required classes
The information below is subject to change.
Fall
- PHYS 32300 (3 credits: lecture and lab) Research in Big Data I
- TDM 10100, 20100, 30100, 40100 (1 credit; 5 sections available) The Data Mine I, III, V, VII
Spring
- PHYS 32400 (3 credits: lecture and lab) Research in Big Data II
- TDM 10200, 20200, 30200, 40200 (1 credit; 5 sections available) The Data Mine II, IV, VI, VIII
Events and Activities
- Weekly dinners with Data Mine participants
- Faculty and TA office hours onsite in Hillenbrand
- Seminars by visiting speakers, including practicing data scientists
- Social gatherings with Data Mine members
- Meals with campus and community leaders
- Game / recreation nights
- Career and graduate school panels
- Hackathons / data competitions
- Professional development activities
- Tour of Purdue’s computational facilities
faculty
