Welcome to DSC 223

Tyler George

Cornell College
DSC 223 - Spring 2024 Block 7

Hello world!

Meet the prof

Dr. Tyler George

West 311

Meet each other!

03:00

Meet data science

• Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge.

• We’re going to learn to do this in a tidy way!

• This is a course on introduction to data science, with an emphasis on statistical thinking.

Software

Excel - not…

R

RStudio

Data science life cycle

Data science life cycle

Import

Tidy + transform

Visualize

Model

Understand

# A tibble: 5 × 2
  date             season
  <chr>            <chr> 
1 23 January 2017  winter
2 4 March 2017     spring
3 14 June 2017     summer
4 1 September 2017 fall  
5 ...              ...   

Communicate

Understand + communicate

Program

Let’s dive in!

Application exercise (AE)

Or more like demo for today…

If you have accepted the github organization invite you can clone it in…

📋 github.com/DSC223-SB7-2024/ae-00-unvotes

Course overview

Homepage

https://dsc223-sb7-2024.github.io/DSC223_S24_website/

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

Course toolkit

All linked from the course website:

• GitHub organization: github.com/DSC223-SB7-2024
• RStudio Server: http://turing.cornellcollege.edu:8787 (Only available on campus)
• Assignment submission and feedback: Git/Github & Moodle

Activities

• Introduce new content in lectures, by live coding with me, and completing the readings
• Attend and actively participate in lectures and labs, office hours, and team meetings
• Practice applying statistical concepts and computing with application exercises during lecture, graded for completion
• Put together what you’ve learned to analyze real-world data
  • Lab assignments (group), completion
  • Homeworks (individual), correctness
  • Exams (individual), correctness
  • Block project presented at end of block (group), details separate

Exams

• Two exams, each 20%

• Each exam comprised of two parts:

  • In class: Closed book

  • Take home: The take home portion and you will be able to use code from class, andy our notes. NO AI.

Project

• Dataset of your choice, method of your choice

• Teamwork

• Presentation and write-up

• Presentations in the last lab

• Interim deadlines, peer review on content, peer evaluation for team contribution

• Some lab sessions allocated to working on projects, doing peer review

Teams

• Assigned by me
• 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.
  • I will use Github commmits as one measure of contribution
  • You will evaluate your own and team members contributes at the end of the block via a survey

Syllabus

On Moodle and our website

Diversity + inclusion

It is my intent that students from all diverse backgrounds and perspectives be well-served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that the students bring to this class be viewed as a resource, strength and benefit.

• Please let me know your preferred name and pronouns on the Getting to know you survey.
• If you feel like your performance in the class is being impacted by your experiences outside of class, please don’t hesitate to come and talk with me. I want to be a resource for you.
• I (like many people) am still in the process of learning about diverse perspectives and identities. If something was said in class (by anyone) that made you feel uncomfortable, please talk to me about it.