INFO 5940 - Computing for Information Science

  • Instructor: Benjamin Soltoff
  • Teaching Assistants:
    • Catherine Yu
    • Andrew Liu
  • Meeting day/time: MW 12:25-2:20
  • Meeting location: Kimball Hall B11
  • Office hours:
    • Benjamin: Tu 9-11am (Gates Hall 216)
    • Catherine: M 5:30-7:30pm (Rhodes Hall 576)
    • Andrew: F 11-12pm (Rhodes Hall 406), 12-1pm (Rhodes Hall 400)
  • Prerequisites: None
  • Requirements: Internet connection and a computer

Course Description

This is an applied course for data scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on generating reproducible research through the use of programming languages and version control software. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. Students will leave the course with basic computational skills implemented through many computational methods and approaches to data science; while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data.

Course Objectives

By the end of the course, students will:

  • Construct and execute basic programs in R using elementary programming techniques and tidyverse packages (e.g. loops, conditional statements, user-defined functions)
  • Apply stylistic principles of coding to generate reusable, interpretable code
  • Debug programs for errors
  • Identify and use external libraries to expand on base functions
  • Apply Git and GitHub workflows for version control
  • Publish reproducible documents
  • Implement best practices for reproducible research
  • Implement machine learning algorithms
  • Visualize information and data using appropriate graphical techniques
  • Import data from files or the internet
  • Munge raw data into a tidy format
  • Scrape websites to collect data for analysis
  • Create visualizations using geospatial data
  • Parse and analyze text documents
  • Construct interactive web applications
Benjamin Soltoff

Benjamin Soltoff

Lecturer in Information Science

Cornell University


Benjamin Soltoff is Lecturer in Information Science at Cornell University. He is a political scientist with concentrations in American government, political methodology, and law and courts. Additionally, he has training and experience in data science, big data analytics, and policy evaluation. He currently teaches courses in data science, research design, data communication, and web design.

Upcoming Classes