Wrap-up: Where to go from here

Lecture 23

Dr. Benjamin Soltoff

Cornell University
INFO 4940/5940 - Fall 2025

December 4, 2025

End-of-semester logistics

Remaining assignments

Project 02 due December 18th at noon
Complete confidential peer feedback survey by December 20th at 11:59pm

Build a simple data science stack

Posit Workbench

Access to Posit Workbench will end at some point after December 26th
All INFO 4940/5940 materials remain available in your repos on GitHub as long as you are an active student
Any other work you have done on the server will not be accessible after the end of the semester
Where will you go from here?

Software installation

Programming language

R
Python

IDE

Reproducibility

Maintaining reproducible computing environments

{renv}
uv

renv::snapshot(type = "all")  # save the state of your R packages
renv::restore()               # restore the state of your R packages

uv add <package>              # add a package to the environment
uv sync                       # synchronize the environment

Do I need reproducible environments?

For small, solo projects, maybe not
For larger, collaborative projects, probably yes

Create a GitHub account

Configure Git

usethis::use_git_config(
  user.name = "Your name", 
  user.email = "Email associated with your GitHub account"
  )

Painless authentication with PAT

Personal Access Token

Uses HTTPS protocol
Integrates with the {usethis} package to automate some Git workflows
Alternative: use SSH

Create PAT

usethis::create_github_token(
  scopes = c("repo", "user", "gist", "workflow"),
  description = "<DESCRIBE YOUR DEVICE>"
)

Store PAT

gitcreds::gitcreds_set()

#> ? Enter password or token: ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
#> -> Adding new credentials...
#> -> Removing credentials from cache...
#> -> Done.

Create PAT

usethis::create_github_token(
  scopes = c("repo", "user", "gist", "workflow"),
  description = "<DESCRIBE YOUR DEVICE>",
  host = "https://github.coecis.cornell.edu/"
)

Store PAT

gitcreds::gitcreds_set(url = "https://github.coecis.cornell.edu/")

#> ? Enter password or token: ghp_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
#> -> Adding new credentials...
#> -> Removing credentials from cache...
#> -> Done.

What have you learned?

Learning objectives for INFO 4940/5940

Train and evaluate predictive machine learning models
Collect and wrangle data for machine learning
Deploy machine learning models in a production environment
Utilize large language models (LLMs) to solve contemporary problems
Implement reproducible machine learning workflows using version control and literate programming

Where to go from here

Courses

Foundational (much more theory and math)

CS 3780: Machine Learning for Intelligent Systems
ECE 3200: Fundamentals of Machine Learning
ORIE 3741: Learning with Big Messy Data
STSCI 3740: Data Mining and Machine Learning
INFO 3950: Data Analytics for Information Science

Domain applications

INFO 1340: Books as Data
INFO 3350/6350: Text Mining History and Literature
INFO 4100/5101: Learning Analytics
INFO 4300: Language and Information
INFO 4940: Advanced NLP for Humanities Research
INFO 4940/6940: How LLMs work, their potential and limitations
INFO 5940: AI Chatbots, RAG, AI Agents
INFO 5940: Solving challenges at the enterprise with AI

AI minor

Find a community

Online communities

Keep your skills fresh

Integrate reproducible workflows into other courses/projects
Tidy Tuesday
Create a blog

Project 02 peer reviews

Instructions

Go to the project 02 peer review instructions and find your assigned teams.
Open the repos on GitHub and find the information on their projects and how to access the draft product
- docs/report.pdf
- README.md
Open a GitHub issue in each repo using the peer review template, test the product, and complete the issue.
Your team will be evaluated on the quality of feedback you provide to your peers.

Good luck on your projects!