Welcome to STA 199!

Lecture 0

John Zito

Duke University
STA 199 Spring 2025

2025-01-09

Teaching team: a glamorous assemblage

Caitrin Murphy

Han Chen

Katie Solarz

Hyunjin Lee

Jasmine Wang

Liane Ma

Avery Hodges

Alexa Fahrer

Domenic Fenoglio

Julia Healey-Parera

David King

Lisa Zhang

Eduardo Vasquez

Arijit Dey

Federico Arboleda

Sarah Wu

Li Fan

Devarpita Bag

Netra Mittal

Natasha Harris

Sonya Eason

Noah Obuya

Mary Knox

John Zito

What are we studying?

Data science

Transforming messy, incomplete, imperfect data into knowledge.

Statistical thinking

Quantifying our uncertainty about that knowledge.

The data science life cycle

Syllabus highlights

Homepage

https://sta199-s25.github.io

  • All course materials
  • Links to Canvas, GitHub, RStudio containers, etc.

Course toolkit

All linked from the course website:

Activities

  • Introduce new content and prepare for lectures by watching the videos and completing the readings
  • Attend and actively participate in lectures and labs, office hours, team meetings
  • Practice applying statistical concepts and computing with application exercises during lecture, graded for attempting
  • Put together what you’ve learned to analyze real-world data
    • Lab assignments (7 or 8 throughout semester)
    • Exams (midterm x 2 + final)
    • Term project completed in teams

Application exercises

  • Daily-ish in lecture

  • “Graded” for attempt, not accuracy

  • Practice Weeks 1 + 2, graded thereafter

  • At least one commit by 2 pm of the day of lecture

  • Turn in at least 70% for full credit

Labs

  • Start in lab session

  • Complete at home

  • Due within a week

  • Discussion with classmates ok, copying not ok!

  • Lowest score dropped

Exams

  • Three exams, each 20%

  • Midterm comprised of two parts:

    • In-class: 75 minute in-class exam. Closed book, one sheet of notes (“cheat sheet”) – 70% of the grade.

    • Take-home: Follow from the in class exam and focus on the analysis of a dataset introduced in the take home exam – 30% of the grade.

  • Final in-class only (Apr 29, 9am - 12pm): Closed book, one sheet of notes (“cheat sheet”).

  • “Cheat sheet”: No larger than 8.5” x 11”, both sides, must be prepared by you.

Caution

Exam dates cannot be changed and no make-up exams will be given. If you can’t take the exams on these dates, you should drop this class.

Project

  • Dataset of your choice, method of your choice

  • Teamwork

  • Five milestones, interim deadline throughout semester

  • Final milestone: Presentation (video) and write-up

  • Presentations submitted as videos

  • Peer review between teams for content, peer evaluation within teams for contribution

  • Some lab sessions allocated to project progress

Caution

Project due date cannot be changed. You must complete the project to pass this class.

Teams of 4 - 5

  • Assigned by us within your lab section
  • Project
  • Peer evaluation during teamwork and after completion
  • Expectations and roles
    • Everyone is expected to contribute equal effort
    • Everyone is expected to understand all code turned in
    • Individual contribution evaluated by peer evaluation, commits, etc.

Grading

Category Percentage
Application Exercises 5%
Labs 15%
Midterm 1 20%
Midterm 2 20%
Final 20%
Project 20%

No specific points allocated to attendance, but the application exercise score is implicitly tied to attendance.

See course syllabus for how the final letter grade will be determined.

Wiggle room

  • You only have to complete 70% of the AEs to receive full credit;
  • We drop the lowest lab score;
  • We replace the lowest in-class midterm score with your final exam score (if it’s better).

Support

  • Help from humans:
    • Attend office hours
    • Ask and answer questions on the discussion forum
  • Reserve email for questions on personal matters and/or grades
  • Read the course support page

Announcements

  • Posted on Canvas (Announcements tool) and sent via email, be sure to check both regularly
  • I’ll assume that you’ve read an announcement by the next “business” day
  • I’ll (try my best to) send a weekly update announcement each Friday, outlining the plan for the following week and reminding you what you need to do to prepare, practice, and perform

Accessibility

  • The Student Disability Access Office (SDAO) is available to ensure that students are able to engage with their courses and related assignments.

  • I am committed to making all course materials accessible and I’m always learning how to do this better. If any course component is not accessible to you in any way, please don’t hesitate to let me know.

If you need testing accommodations

Make sure I get a letter, and make your appointments in the Testing Center now.

Late work, waivers, lecture recordings, regrades…

  • We have policies!
  • Read about them on the course syllabus and refer back to them when you need it

Collaboration

  • Labs: discussing and helping is fine. Sharing your solutions and copying others is not;

  • Exams: collaboration of any kind is completely forbidden on any part of any exam;

  • Projects: collaboration of any kind is enthusiastically encouraged within your team. Between teams, it’s the same as labs; do not directly share your stuff or copy off of others.

Use of AI tools

  • AI tools for code:

    • Sure, but be careful/critical! Working code != correct/good code.
    • Must explicitly cite with a direct url linking to the conversation you had.

  • AI tools for narrative: Absolutely not!

  • AI tools for learning: Sure, but be careful/critical!

Academic integrity

To uphold the Duke Community Standard:

  • I will not lie, cheat, or steal in my academic endeavors;

  • I will conduct myself honorably in all my endeavors; and

  • I will act if the Standard is compromised.

  • Zeros from conduct violations will not be dropped or replaced;

  • If people are copying, sharer and recipients penalized equally. It does not matter how well-intentioned everyone was;

  • If we discover violations, they go straight to the conduct office.

A game

How old is this person?

 

Ethel Merman

Ethel Merman

Born January 16, 1908
Died February 15, 1984
Age 76
Claim to fame JZ’s favorite singer

How old is this person?

 

Megan Pete

Megan Thee Stallion

Born February 15, 1995
Age 29
Claim to fame Rapper

How old is this person?

 

봉준호

Bong Joon-ho

Born September 14, 1969
Age 55
Claim to fame Directed Parasite, Snowpiercer, etc

Now do it with pictures…

When the picture was taken, how old was the person?

Managing expectations

The stakes are low today. We’re just getting our feet wet and working out the kinks:

  • It’s the first time all 300+ of us are attempting to access our containers simultaneously. It may choke;

  • If you get stuck on a loading screen of some kind, be patient and let it do its thing. Refreshing, reloading, etc will just amplify our collective problem;

  • If yours never loads, no big deal. Just sit back and watch me, or follow along on a neighbor’s screen;

  • Even if yours does load, you’re welcome to just watch.

Yuja Wang

Born 2/10/1987
Age in pic 36
Claim to fame Classical pianist

I’m sure she’d be pleased

Joan Crawford

A secret she took to her grave:

Born 3/23/(1904 - 1908)
Died 5/10/1977
Age in pic 38 - 42
Claim to fame Oscar-winning actor

We’ll never know…

Eubie Blake

His actual birthday was not known at the time:

Born 2/7/1887
Died 2/12/1983
Age in pic 82
Claim to fame Composer

lol that bar at 86

Watch out for data quality!

Raghuram Rajan

Born 2/3/1963
Age in pic 48
Claim to fame UChicago economist
RBI governor

You’re natural denoisers!

James Gandolfini

Born 9/18/1961
Died 6/19/2013
Age in pic 51
Claim to fame played Tony Soprano

The wisdom of crowds

Celia Cruz

Born 10/21/1925
Died 7/16/2003
Age in pic 76
Claim to fame Queen of Salsa

¡Azúcar!

Our best guessers

Truth No. 1 No. 2 No. 4 No. 4
Wang 36 34 36 27 34
Crawford 40? 38 42 33 40
Blake 82 77 74 80 72
Rajan 48 50 50 45 53
Gandolfini 51 58 48 50 46
Cruz 76 72 68 73 73
3.66 3.83 4.16 4.16

Whence ggplot?

Silly exercise, serious themes

Statistical lessons

  • Domain knowledge and modeling assumptions: data do not speak for themselves. You need some subject-matter expertise about what you’re studying, as well as an interpretive lens;
  • Are you asking questions the data can actually answer?
  • Uncertainty has many sources, and in some cases, it may be simply irreducible, no matter how hard you try;
  • Data quality and data cleaning: Data are not gospel. There could be noise and mistakes. Then what?
  • Wisdom of crowds: aggregating many imperfect guesses can do better than any one individual guess.

Hard skills

  • GitHub repositories: cloning and pulling;
  • Working with data in CSV format;
  • Rendering a Quarto document to get PDFs that seamlessly integrate written text, code, and output;
  • Using ggplot to build up visualizations in layers, like a cake.

Get ready

You will do all of these things on a weekly basis in this course.

Wrap up

This week’s tasks

  • Complete Lab 0
    • Computational setup
    • Getting to know you survey
  • Read the syllabus and ask questions on Ed
  • Complete readings and videos for next class
  • Accept your invitation to the GitHub organization pronto!