Welcome to CMSACamp 2026

Department of Statistics & Data Science
Carnegie Mellon University

Introduction

Plan for today

Opening remarks + program overview

Visit from Jess Paschke

Academic advisor presentation (Glenn)

Lunch (Baker Hall 129Q)

Lab

Who are we?

Erin Franke (preferred form of address: Erin)

  • CMSACamp ’21
  • Intern for Minnesota Twins in Summer ’22
  • Macalester College ’23: Statistics major, CS & Econ minors
  • Statistical Programmer @ Mayo Clinic
  • Research at CMU: applications in wastewater-based epidemiology, education, psychology, sports analytics (marathon)

Who are we?

Sara Colando (preferred form of address: Sara)

  • CMSACamp ’23
  • Pomona College ’24: Math and Philosophy double major
  • Undergrad research: biostatistics (genomics)
  • Research at CMU: statistical network analysis, statistics and data science education, and measurement error in text analysis

Who are we?

  • TAs: Princess Allotey, Yuchen Chen, JungHo Lee

  • CMSACamp Director: Ron Yurko

  • CMSACamp Advisor: Glenn Clune

  • CMSACamp Coordinator: Jess Paschke

Meet the students!

Let’s first go around and share:

  • Name
  • Where you call home
  • College, year in school
  • Major(s)/minor(s)
  • Favorite sport
  • What you are most excited for about CMSACamp

About CMSACamp

Goals

  • Develop fundamentals research skills: data wrangling, visualization, modeling, communication

  • Become familiar with R, tidyverse, Quarto (Markdown syntax), Git/GitHub

  • Become familiar with cutting-edge statistical machine learning techniques

  • Create a portfolio of projects and practice reproducible research

  • Network with academic researchers and industry professionals

  • Help navigate your next steps—industry vs. graduate school

  • Optionally, present at CMSAConference in October!

Project advisors this year include

…and some more pending!

Resources to save

Check these frequently!

Schedule
(subject to change)

A typical day

  • Lectures

  • Labs

  • Speaker/webinar sessions

Optional:

  • Lunch: ~1x week, will be updated on calendar as we have more information.

  • Office Hours:

    • Erin: 11-12pm, Tuesdays
    • Glenn: 12-1pm, Tuesdays
    • Sara: 11-12pm, Thursdays

Lectures

Mon–Fri, 9:30–11:00am, Scaife 236

  • First ~2 weeks: EDA, clustering, basic data science tasks
  • Next ~4 weeks: statistical modeling, machine learning
  • After that: special topics, guest lectures

A few scheduling notes:

  • Holidays: Juneteenth (Friday June 19) and Independence Day break (July 2–3 + weekend)
  • Thursday July 9: Pirates vs. Braves game at PNC Park

Labs

Mon–Fri, 1:30–3pm, Scaife 236

  • Demo labs (Mon–Wed this week) + 1–2 later on

  • Project labs

    • will begin with a mini EDA project

    • then shift to focus on main capstone project

Speaker/webinar sessions

  • Check calendar! Either mid-day (in between lecture and lab) or after lab
  • Scaife 236 / Zoom depending on speaker
  • Lots of project pitches this week and next week, then guest lectures as summer goes on

EDA project

  • Practice understanding the structure of a dataset and perform basic EDA tasks (e.g., data wrangling, data visualization) in R, and using GitHub for collaboration
  • Work in groups of 2–3

  • Timeline

    • Thursday, June 4: Release date

    • Tuesday, June 16: 6-minute presentation (no notes/scripts) during lab

Capstone project

  • Work with a external advisor to analyze a dataset and answer a research question that is relevant in the respective sport
  • Work in groups of 2–3
  • Presentation checkpoint(s) (dates TBD, no notes/scripts)

  • Deliverables (more details will be provided later on)

    • Report
    • Poster
    • Presentation

Reminders

  • Fill out the survey forms (Communication and Data Science Background)
  • Reset CMU wifi password (for non-CMU students)
  • Check Calendar, Slack, email often

Tips

  • This is a research program. Feel free to go above and beyond & explore things that aren’t taught in lectures
  • Focus on principles, tools are incidental (i.e. it’s more important to get how things work first over just how to do things)
  • Don’t expect to get all the concepts in one go (instead, repetition… do more & read more)

Expectations

  • In-person attendance

    • Be on time. PLEASE.

    • This applies to lectures, labs, other sessions (e.g., webinars, guest speakers, other activities)

    • This is part of the Code of Conduct

  • Participate and ask questions
  • Work together. Help and support each other.
  • Enjoy, learn and grow!

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