Welcome to INFO 5001

Lecture 1

Dr. Benjamin Soltoff

Cornell University
INFO 5001 - Fall 2025

August 26, 2025

Agenda

Agenda

• Intros
• What is data science?
• Software
• Application exercise
• Course overview
• This week’s tasks

Learning objectives

• Introduce the course staff
• Define data science and how it will be taught in this course
• Review course policies

Staff intros

Meet the instructor

Dr. Benjamin Soltoff

Associate Teaching Professor in Information Science

284 CIS Building

Meet the course team

• Philan T.
• Paul V.
• Steven X.

Meet each other!

Physically interact with at least 2 people sitting around you. Introduce yourselves to each other and share:

• Your name - Prof/Dr. Soltoff
• Your major - Political science
• The last movie you saw - Star Wars: Episode VI – Return of the Jedi
• What you hope to get out of this class - A job?

02:00

What is data science?

What is data science?

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

  [A]n interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract or extrapolate knowledge and insights from noisy, structured and unstructured data, and apply knowledge from data across a broad range of application domains

• This is a course on computing applications for data science workflows

Data science life cycle

Import

Tidy + transform

Visualize

Model

Understand

Communicate

Understand + communicate

Program

How we will do this

Excel - not…

ChatGPT - no

Alteryx - closer…

R and Positron

• R is an open-source statistical programming language
• R is also an environment for statistical computing and graphics

• Positron is a free, data science IDE for R and Python
• “Soft-fork” of VS Code, with a focus on data science
• Other popular IDE is RStudio

Major differences between R and Python

	R	Python
Syntax	Functional language	Object-oriented language
Statistical learning	Developed by statisticians for statistical analysis	Meh
Machine learning	{tidymodels} {mlr3}	{scikit-learn}
Deep learning	{torch} {keras3} (via {reticulate})	{tensorflow} {pytorch}
Visualization	{ggplot2}	{matplotlib} + others
Package management	CRAN	pip/virtualenv/PyPI/Anaconda/uv
Speed	Somewhat slower	Somewhat faster
Community	Academia and industry	Larger (general-purpose programming language)

tidyverse

tidyverse.org

• The {tidyverse} is an opinionated collection of R packages designed for data science
• All packages share an underlying philosophy and a common grammar

Quarto

Quarto

• Fully reproducible documents – each time you render the analysis is run from the beginning
• Code goes in chunks – narrative goes outside of chunks
• A visual editor for a familiar / Google docs-like editing experience
• Similar (but IMO superior) to Jupyter Notebooks
• Supports Python, R, Julia, Observable natively

How will we use Quarto?

• Every assignment / report / project / etc. is a Quarto document
• You’ll always have a template Quarto document to start with
• The amount of scaffolding in the template will decrease over the semester

Version control with Git

Git and GitHub

• Git is a version control system – like “Track Changes” features from Microsoft Word, on steroids
• It’s not the only version control system, but it’s a very popular one

• GitHub is the home for your Git-based projects on the internet – like DropBox but much, much better

• We will use GitHub (Enterprise) as a platform for web hosting and collaboration

Versioning

Versioning

with human readable messages

How we use Git and GitHub

How we use Git and GitHub

How we use Git and GitHub

How we use Git and GitHub

Let’s dive in!

Application exercise

Or more like demo for today…

📋 info5001.infosci.cornell.edu/ae/ae-00-unvotes.html

Course overview

Homepage

https://info5001.infosci.cornell.edu/

• All course materials
• Links to Canvas, GitHub, Posit Workbench, etc.
• Let’s take a tour!

Course toolkit

All linked from the course website:

• GitHub organization: github.coecis.cornell.edu/info5001-fa25

• Positron

  • Use the Workbench: posit-workbench.infosci.cornell.edu

  • Or install it locally

• 🤖 Beebe

• Communication: GitHub Discussions

• Assignment submission and feedback: Gradescope

Important

Make sure you can access RStudio before class on Thursday.

Activities: Prepare, Participate, Practice, Perform

• Prepare: Introduce new content and prepare for lectures by completing the readings

• Participate: Attend and actively participate in lectures and labs, office hours, team meetings

• Practice: Practice applying computational techniques with application exercises during lecture, graded for completion

• Perform: Put together what you’ve learned to analyze real-world data

  • Homework assignments x 8-10(-ish) (individual)
  • Monthly quizzes
  • Final exam
  • Team project

Activities: Participate

Preparing for and participating in class

Not preparing for class, not actively participating

Cadence

• Application exercises: Complete by the end of the day
• HWs: Posted Friday morning, due following Wednesday 11:59pm
• Quizzes: Completed during Friday lab
• Exam: Written, in-person during finals week
• Project: Deadlines throughout the semester, with some lab time dedicated to working on them, and most work done in teams outside of class

Grading

Category	Percentage
Project	30%
Homework	25%
Exam	20%
Quizzes	15%
Application Exercises	10%

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

15 minute rule

Support

• Attend office hours
• Ask and answer questions on the discussion forum
• Use Beebe for generative AI assistance with course content
• Reserve email for questions on personal matters and/or grades
• Read the course support page

Diversity + inclusion

• I want you to feel like you belong in this class and are respected
• We are committed to full inclusion in education for all persons
• If you feel that we have failed these goals, please either let us know or report it, and we will address the issue

Accessibility

I want this course to be accessible to students with all abilities. Please feel free to let me know if there are circumstances affecting your ability to participate in class.

• Office of Student Disability Services
• Cornell Health CAPS (Counseling & Psychological Services)
• Undocumented/DACA Student support

Course policies

Late work, waivers, regrades policy

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

Collaboration policy

• Only work that is clearly assigned as team work should be completed collaboratively.

• Homeworks must be completed individually. You may not directly share answers / code with others, however you are welcome to discuss the problems in general and ask for advice.

• Quizzes and exams must be completed individually. You may not discuss any aspect of these assignments with peers until the grades are posted.

Sharing / reusing code policy

• We are aware that a huge volume of code is available on the web, and many tasks may have solutions posted

• Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism, regardless of source

• All code must be written by you, the human being

Generative AI

• Use generative AI to facilitate, rather than hinder, learning

• ✅ GAI tools for reference purposes

  How do I make a scatterplot using ggplot2 in R?

• 🤔 GAI tools for writing my code/analysis:

  Generate code in R to tidy the dadmom data frame

• ❌ GAI tools for narrative

• You are ultimately responsible for the work you turn in; it should reflect your understanding of the course content

Academic integrity

1. A student shall in no way misrepresent his or her work.
2. A student shall in no way fraudulently or unfairly advance his or her academic position.
3. A student shall refuse to be a party to another student’s failure to maintain academic integrity.
4. A student shall not in any other manner violate the principle of academic integrity.

Most importantly!

Ask if you’re not sure if something violates a policy!

Before Thursday

• Complete your preparation activities