Welcome to INFO 5001

Lecture 1

Dr. Benjamin Soltoff

Cornell University
INFO 5001 - Fall 2024

August 27, 2024

Agenda

Agenda

• What is data science?
• Software
• Application exercise
• What is INFO 5001?
• This week’s tasks

Staff intros

Meet the instructor

Dr. Benjamin Soltoff

Lecturer in Information Science

Gates Hall 216

Meet the course team

TAs

• Hyunchul L
• Kevin C
• Melissa E
• Siyuan T
• Tung W
• Yan T

Meet each other!

• Name
• Undergrad major
• A book you’ve read recently

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 domains¹

• We’re going to learn to do this in a tidy way – more on that later!

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

1. Source: Wikipedia

Software

Excel - not…

ChatGPT - no.

Alteryx - closer…

R

RStudio

Data science life cycle

Data science life cycle

Import

Tidy + transform

Visualize

Model

Understand

Communicate

Understand + communicate

Program

How we will do this

R and RStudio

• R is an open-source statistical programming language
• R is also an environment for statistical computing and graphics
• It’s easily extensible with packages

• RStudio is a convenient interface for R called an IDE (integrated development environment), e.g. “I write R code in the RStudio IDE”
• RStudio is not a requirement for programming with R,¹ but it’s very commonly used by R programmers and data scientists

1. See VS Code or Positron

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 caret mlr3	scikit-learn
Deep learning	torch keras3 (via reticulate)	tensorflow pytorch
Visualization	ggplot2	matplotlib + others
Package management	CRAN	pip/virtualenv/PyPI/Anaconda
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

Source: Quarto.org

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

Source: STA 199

Versioning

with human readable messages

Source: STA 199

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

Who is this class for?

Jeri

• PhD student in information science
• Feels comfortable with regression and econometric methods in Stata
• Will be analyzing a large-scale dataset for her dissertation
• Seeks a reproducible workflow to manage all her exploratory and confirmatory analysis

Fernando

• Entering the MPS program
• Has a degree in computer science but has never taken courses in data science
• Expects to analyze a collection of published news articles
• Wants to understand code samples he finds online so he can repurpose them for his own work

Ryan

• Fourth-year undergraduate student in information science who is beginning early work towards the MPS degree
• Has taken core classes in information science (including CS 1110) with a concentration in user experience/user design
• Expects to use data science methods in industry for user testing and data analysis

Fang

• Born and raised in Shenzhen, China
• Background in psychology, plans to apply for doctoral programs in marketing
• Is going to run 300 experiments on Amazon MTurk in the next six months
• Expects to take courses in machine learning and Bayesian statistics which require a background in R

Course overview

Homepage

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

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

Course toolkit

All linked from the course website:

• GitHub organization: github.coecis.cornell.edu/info5001-fa24

• RStudio

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

  • Or install it locally

• 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 statistical concepts and computing with application exercises during lecture, graded for completion

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

  • Lab assignments x 6(-ish) (first individual, later team-based)
  • Homework assignments x 6(-ish) (individual)
  • Mid-semester take-home 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
• Labs: Start and make large progress on Friday in lab section, finish up by Monday 11:59pm the following week
• HWs: Posted Friday morning, due following Wednesday 11:59pm
• Exam: Exam released Friday morning, no lab on Friday of that week, due by Monday 11:59pm
• 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
Homework	30%
Project	30%
Labs	15%
Exam	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
• Reserve email for questions on personal matters and/or grades
• Read the course support page

Announcements

• Posted on Canvas (Announcements tool), be sure to check regularly (or forward announcements to your email)
• I’ll assume that you’ve read an announcement by the next “business” day

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.

• Exams must be completed individually. You may not discuss any aspect of the exam with peers.

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 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

Additional resources: CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy

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.

Source: Cornell University Code of Academic Integrity

Most importantly!

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

Before Thursday

• Complete your preparation activities

A film recommendation (nine years late…)