INFO 5001 - Computing for Information Science
Syllabus
Overview
Instructor
- Dr. Benjamin Soltoff
- Office: 284 CIS Building
- Email: soltoffbc@cornell.edu
- Office hours: TODO, or by appointment
Course logistics
- Meets TuTh from 10:10am - 11:25am for 28 sessions
- Discussion sections meet F 10:10am - 11:00am or F 11:15am - 12:05pm for 15 sessions
- 4 credits, offered for a letter grade
- Prerequisites: Introductory programming course equivalent to CS 1110 or CS 1112. Core undergraduate math/statistics expected. No R programming experience required.
Course description
This is an applied course for data scientists with general-purpose programming experience who wish to harness growing digital and computational resources for data scientific applications. The focus of the course is on generating reproducible tools and analysis using reproducible programmatic techniques. Students will leave the course with data analytic skills implemented through many computational methods and approaches to data science; while students will not become expert data scientists, they will be able to adapt and expand beyond these skills as they are presented with new questions, methods, and data.
Course learning objectives
By the end of the semester, you will…
- Construct and execute basic programs in R using elementary programming techniques and {tidyverse} packages (e.g. loops, conditional statements, user-defined functions).
- Implement data science workflows using common, reproducible methods and software tools.
- Responsibly utilize large language models (LLMs) and generative AI tools to assist with coding and data analysis.
- Apply stylistic principles of coding to generate reusable, interpretable code.
- Utilize reference documentation and debugging tools to troubleshoot problems.
- Apply Git and GitHub workflows for version control.
Office hours
Textbooks
All books are freely available online.
| R for Data Science, 2e | Wickham, Çetinkaya-Rundel, & Grolemund | O’Reilly, 2nd edition, 2023 | Hard copy only on Amazon |
Given the rapid innovation in the field, we will also make use of additional online resources such as tutorials, reference documentation, etc.
Course community
We want you to feel like you belong in this class and are respected. Cornell University (as an institution) and we (as human beings and the instructor of this course) are committed to full inclusion in education for all persons. If for any reason you feel that we have failed these goals, please either let us know or report it, and we will address the issue.
Services and reasonable accommodations are available to persons with temporary and permanent disabilities, to students with DACA or undocumented status, to students facing mental health or other personal challenges, and to students with other kinds of learning challenges. Please feel free to let me know if there are circumstances affecting your ability to participate in class. Some resources that might be of use include:
- Office of Student Disability Services: https://sds.cornell.edu
- Cornell Health CAPS (Counseling & Psychological Services): https://health.cornell.edu/services/counseling-psychiatry
- Undocumented/DACA Student support: https://dos.cornell.edu/undocumented-daca-support/undergraduate-admissions-financial-aid
Academic accommodations
We want all students to have the opportunity to be successful in this course. Accommodations can help provide some flexibility and equitable classroom access.
Per university policy, this course provides the following accommodations:
- Disability Accommodations
- Religious-Observance Accommodations
- Title IX Accommodations
- Varsity Athlete Accommodations
- Medical Accommodations
- Military Service
- Other Accommodations
Accessibility
If there is any portion of the course that is not accessible to you due to challenges with technology or the course format, please let me know so we can make appropriate accommodations.
Student Disability Services is available to ensure that students are able to engage with their courses and related assignments. Students should be in touch with Student Disability Services to request or update accommodations under these circumstances.
If you have an approved SDS accommodation, please send a copy of this letter to the instructors at soltoffbc@cornell.edu so we can ensure your accommodations are implemented in this course.
Communication
All lecture notes, assignment instructions, an up-to-date schedule, and other course materials may be found on the course website: info5001.infosci.cornell.edu.
Announcements will be posted through Canvas Announcements periodically. Please check Canvas (or ensure Canvas announcements are forwarded to your email) to ensure you have the latest announcements for the course.
Where to get help
- If you have a question during lecture or discussion, feel free to ask it! There are likely other students with the same question, so by asking you will create a learning opportunity for everyone.
- The course staff is here to help you be successful in the course. You are encouraged to attend office hours to ask questions about the course content and assignments. Many questions are most effectively answered as you discuss them with others, so office hours are a valuable resource. Please use them!
- Outside of class and office hours, any general questions about course content or assignments should be posted on the course discussion forum. There is a chance another student has already asked a similar question, so please check the other posts on GitHub Discussions before adding a new question. If you know the answer to a question posted on the discussion board, I encourage you to respond!
If there is a question that’s not appropriate for the public forum, please email us at soltoffbc@cornell.edu. Barring extenuating circumstances, we will respond to INFO 5001 emails within 48 hours Monday - Friday. Response time may be slower for emails sent Friday evening - Sunday.
Activities & Assessment
The activities and assessments in this course are designed to help you successfully achieve the course learning objectives. They are designed to follow the Prepare, Practice, Perform format.
Prepare: Includes reading assignments and lectures to introduce new concepts and ensure a basic comprehension of the material. The goal is to help you prepare for the in-class activities during lecture.
Practice: Includes in-class application exercises where you will begin to apply the concepts and methods introduced in the prepare assignment. The activities will graded for completion, as they are designed for you to gain experience with the statistical and computing techniques before working on graded assignments.
Perform: Includes homework, quizzes, exams, and the project. These assignments build upon the prepare and practice assignments and are the opportunity for you to demonstrate your understanding of the course material and how it is applied to analyze real-world data.
My role as the instructor is to provide you with the resources and support you need to succeed in this course. However, your success ultimately depends on your active participation and engagement in the course. You are expected to:
- Attend all lectures and discussion sections.
- Complete the assigned readings and lectures before each class.
- Actively participate in class.
- Complete all assignments on time and to the best of your ability.
- Seek help when needed, whether from the course staff, classmates, or other resources.
- Take responsibility for your own learning and development throughout the course.
Readings (Prepare)
Before each lecture, you are expected to complete the assigned readings. These readings will introduce you to the concepts that will be discussed in the lecture. The readings are designed to help you prepare for the in-class activities during lecture. I don’t assign an absurd volume of readings, nor do I expect you to fully and completely understand every single thing you’ve read. But you should have a familiarity with the concepts and techniques prior to attending class.
Lectures (Prepare)
Part of the class time will be lectures that introduce new concepts or review topics from the preparation materials. Lectures will not repeat everything in the readings, they will instead highlight important and known to be complex concepts and will be supplemented with live coding activities. You are expected to attend every lecture.
Application exercises (Practice)
A majority of the in-class lectures will be dedicated to working on Application Exercises (AEs). These exercises will give you an opportunity to apply the computational concepts and techniques introduced in the prepare assignment. These AEs are due by the end of the day of the corresponding lecture period. Specifically, AEs from Tuesday lectures are due Tuesday by 11:59 pm, and AEs from Thursday lectures are due Thursday by 11:59 pm.
Because these AEs are for practice, they will be graded based on completion, i.e., a good-faith effort has been made in attempting all parts.
The four lowest AE grades will be dropped at the end of the semester.
Homework (Perform)
In homework, you will apply what you’ve learned to complete data analysis tasks. You may discuss homework assignments with other students; however, homework should be completed and submitted individually.1 Homework must be rendered using Quarto, and submitted as a PDF in Gradescope.
1 One or two homeworks may be team assignments to be completed with your project team members.
Homework assignments are due 11:59 pm on the indicated due date.
The lowest homework grade will be dropped at the end of the semester.
Quizzes (Perform)
Approximately every four weeks, there will be a quiz to assess your understanding of the course material. Quizzes will be administered during the Friday discussion section meetings and will cover the material from the previous weeks. The quizzes are designed to help you review and reinforce your understanding of the concepts covered in the course.
All quizzes will be written in class and closed-note, closed-book.
Exams (Perform)
There will be one cumulative exam at the end of the course. Through this exam you have the opportunity to demonstrate what you’ve learned in the course. More details about the content and structure of the exam will be discussed near the end of the semester.
Project (Perform)
The purpose of the project is to apply what you’ve learned throughout the semester to solve some sort of real-world problem. The project will be completed in teams of 3-4 students, and each team will present their work during the final discussion section meeting.
More information about the project will be provided during the semester.
Grading
The final course grade will be calculated as follows:
| Category | Percentage |
|---|---|
| Project | 30% |
| Homework | 25% |
| Exam | 20% |
| Quizzes | 15% |
| Application Exercises | 10% |
The final letter grade will be determined based on the following thresholds:
| Letter Grade | Final Course Grade |
|---|---|
| A+ | >= 98 |
| A | 93 - 97.99 |
| A- | 90 - 92.99 |
| B+ | 87 - 89.99 |
| B | 83 - 86.99 |
| B- | 80 - 82.99 |
| C+ | 77 - 79.99 |
| C | 73 - 76.99 |
| C- | 70 - 72.99 |
| D+ | 67 - 69.99 |
| D | 63 - 66.99 |
| D- | 60 - 62.99 |
| F | < 60 |
Course policies
Academic honesty
TL;DR: Don’t cheat!
Please abide by the following as you work on assignments in this course:
You may discuss individual homework assignments with other students; however, you may not directly share (or copy) code or write up with other students. For team assignments, you may collaborate freely within your team. You may discuss the assignment with other teams; however, you may not directly share (or copy) code or write up with another team. Unauthorized sharing (or copying) of the code or write up will be considered a violation for all students involved.
You may not discuss or otherwise work with others on the quizzes and exam. Unauthorized collaboration or using unauthorized materials will be considered a violation for all students involved.
Reusing code
Unless explicitly stated otherwise, you may make use of online resources (e.g. StackOverflow) for coding examples on assignments. You may not directly copy and paste from these sources, but instead you need to adapt the code to fit your specific task. You must explicitly cite where you obtained the code using a code comment # immediately near the appearance of the reused code in the file. Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism.
Use of generative artificial intelligence (GAI)
Cornell’s report on Generative Artificial Intelligence for Education and Pedagogy outlines many of the potential benefits and drawbacks to using GAI in the classroom. In this course, we see the value of coding assistants such as GitHub Copilot and ChatGPT to generate code from text. However as an introductory course, we need to ensure that GAI is not used as a substitute or replacement for student learning. GAI should not be used by students to replace your ability to think clearly. Students who use GAI should use it to facilitate, rather than hinder, learning.
- ✅ GAI tools for reference purposes: You may make use of the technology as a reference tool, similar to looking up the documentation for a function or Googling your problem. For example, I hate writing regular expressions. Absolutely loathe it. Say I have a dataset where I need to clean a character column to remove all words that are within double asterisk symbols. I might ask ChatGPT
🤔 GAI tools for writing my code/analysis: You may use GAI tools to assist in writing code in this class. You are expected to understand how any/all submitted code works. Any assignment for which you use GAI as more than a reference tool will require a written self-reflection to consider how you used GAI tools, what skills you acquired through the assignment, and how you believe you demonstrate mastery of the learning objectives for the course.
You may not make use of the technology as a substitute for critical thinking. For example, you may not upload your data file to a GAI platform and ask it to create charts and statistical models for you. You are taking this course, not a GAI tool. I reserve the right to orally assess any student on their submissions to verify they meet the learning objectives for the assignment; students who fail to satisfactorily demonstrate they have met the learning objectives may receive a grade penalty of up to 100% on the assignment.
❌ GAI tools for narrative: unless instructed otherwise, you may not use GAI to write narrative on assignments. In general, you may use generative AI as a resource as you complete assignments but not to answer the exercises for you.
You are ultimately responsible for the work you turn in; it should reflect your understanding of the course content.
Any violations in academic honesty standards as outlined in the Cornell University Code of Academic Integrity and those specific to this course will result in a 0 for the assignment (or possibly more) and will be reported to the College of Engineering Academic Integrity Hearing Board.
Extra credit
Students can earn up to a maximum of 1 percentage point towards their final grade through the extra credit assignment. This is the only opportunity for extra credit in the course.
Late work & extensions
The due dates for assignments are there to help you keep up with the course material and to ensure the course staff can provide feedback within a timely manner. We understand that things come up periodically that could make it difficult to submit an assignment by the deadline. Note that the lowest homework assignment will be dropped to accommodate such circumstances.
Late work
A slip day allows you to submit an assignment 24 hours after the deadline and still receive credit without a late penalty. You are provided with a total of 4 slip days for the entire semester. Slip days may be used on homework assignments. You can use up to 1 slip day for a given homework assignment.
To use your slip days, just submit your assignment late. No need to email telling us you are submitting using your slip days. Check Canvas to see how many of your slip days you have used before submitting an assignment late.
If you use a slip day, do not submit anything to Gradescope before the submission deadline. We may begin grading before the slip day deadline and we will grade whatever submission we see in Gradescope.
If you run out of slip days or fail to submit your assignment prior to the slip day deadline without prior permission then your assignment will not be accepted.
There is no late work accepted for application exercises, since these are designed to help you prepare for homework, quizzes, and the project.
There is no late work accepted for project components, quizzes, or exams.
Waiver for extenuating circumstances
If you need a bit of extra time, please use your slip days. Slip days are specifically intended for legitimate reasons for needing an extension like disability, religious observance, Title IX, student athletics, medical problems, and military service.
If using your slip days for accommodations is not working for you or if you have an SDS accommodation which includes deadline flexibility, you may request a deadline extension in-advance of the deadline. We will work with you to develop reasonable accommodations that align with your individual situation.
To request a deadline extension:
- Commit and push the work you have completed up to this point on the assignment.
- Email soltoffbc@cornell.edu. In your email clearly state
- The assignment
- What you have already completed on the assignment.
- What you have left to complete.
- Your proposed deadline extension (e.g. Monday, February 8th at 11:59pm.)
Regrade requests
Regrade requests can be submitted beginning at noon the day after an assignment’s grade is posted, and must be submitted on Gradescope within a week of when an assignment is returned. Regrade requests will be considered if there was an error in the grade calculation or if you feel a correct answer was mistakenly marked as incorrect. Requests to dispute the number of points deducted for an incorrect response will not be considered. Note that by submitting a regrade request, the entire question will be graded which could potentially result in losing points.