Data Fluency — INFO-I 123 Fall 2026 — Indiana University Bloomington

Alexander Hayes is teaching Data Fluency (INFO-I 123) in the fall 2026 semester at Indiana University Bloomington.

Schedule

Week Date Topic Monday Topic Wednesday Topic
1 08/23 Foundations + Frontiers Welcome + Data Structured Data + GenAI
2 08/30 Computation + Hardware Hardware Hardware + Infrastructure
3 09/06 Data Acquisition + Software Labor Day Automatic Data Collection + Julia
4 09/13 Scientific Programming I Hardware → Software, Functions Functions + Problem Solving
5 09/20 Scientific Programming II Compound statements: if/for  

Syllabus

Course Description

Data is big. Data is everywhere. How can we possibly be expected to keep up in a world full of data, much of which is data about ourselves? This class provides fundamental skills for the 21st century: understanding data, extracting knowledge from data, generating predictions from data and presenting data.

Learning Outcomes

This course will prepare you to be an excellent producer and consumer of data. INFO-I 123 will introduce you to concepts across Informatics and Data Science. Students who successfully complete Data Fluency will be able to:

  • Learn how to read and interpret quantitative information, and be able to make decisions based on it
  • Learn how to identify problems/issues that can be examined and remedied with data-driven analysis
  • Learn how to understand, process, extract value from, visualize, and communicate data

INFO-I 123 also fulfills the General Education requirement for Natural and Mathematical Sciences. It fulfills the following General Education Learning Outcomes:

  • NM-1. Students demonstrate an understanding of scientific inquiry and the bases for technology
  • NM-4. Students demonstrate the ability to solve problems
  • NM-5. Students demonstrate analytical and/or quantitative skills

(Usage note: this section is quoted verbatim from material by Samantha W. M. Wood in course content discussions).

Course requirements

Required Text and Materials

There are no required texts for this course. Any readings that come up with be provided digitally on Canvas, or links posted to IU Library resources.

Technical Requirements

We strongly recommend having a laptop that you can bring to class regularly. Windows / macOS / Linux / ChromeOS systems should generally be fine, and we try to support workflows that work on the most-common platforms.

Apart from a laptop, there are no purchases or subscriptions required for this course. We’ll stick with free and open source tools, or with tools included through Indiana University.

  • Microsoft Office Suite (Excel / PowerPoint / Word), or similar tools from the Google Workspace Suite (Sheets / Slides / Docs)
  • Julia/Python
  • Generative AI tools (specific cases announced in class, again: we do not want you to have to purchase anything - “free” versions of proprietary tools, or open source approaches will be brought up as much as is possible)
  • Access to Canvas using a supported web browser

Please contact your instructor as soon as possible if/when you run into uncertainty with your computer. Indiana University has a loaner laptop program, and we’ll try to keep flexible options for a workflow like: “taking paper notes in class, then working on a desktop at home”, etc.

Right to Document

All work that is submitted can be used anonymously for future educational and research purposes (e.g., show exemplars to future students). If you would like to be excluded from this policy, please email your instructor.

Right to Revision

Your instructors reserve the right to update the syllabus if or when course needs change. In this event, an “Announcement” will be made on Canvas explaining the change.

Communication and Teaching Team

We try to emphasize in-person communication, a few minutes before class, during class, or during office hours. For everything else, we will use email (i.e., please do not use the “Canvas Messages”).

We’ll try to get back to messages within 24-48 hours during business days (i.e., we cannot promise to be available during weekends or holidays).

Contact info and availability will be on the home page for ease-of-access.

Technical Support

For additional help with technical issues, consult:

Course assessment & grades

Course assessment

Assessment falls into 4 categories, with short overviews below. (“Attendance” tends to be a common source of questions. In general: we try to follow a pattern of “drop low grades” in a consistent way, since we assume that almost everyone will have a weird week at some point during the semester).

  • Quizzes - 50%
    • Midterm + final style. Not strictly cumulative, but weighted toward recent material.
  • Lecture Checks - 15%
    • Typically a short reflection on what we’ve done in class, or submitting notes based on what we’ve talked about in class.
  • Skills Checks - 20%
    • Self-guided assessments incorporating concepts from class.
  • Attendance - 15%
    • Taken in-class, or as low-stakes in-class assignments
    • Several attendance points will be dropped automatically (later in the semester following university attendance verification)
    • You do not need to contact your instructor if you are sick/absent, absences are dropped automatically (with an exception for “Religious Observances”, see the university resources).
    • You should not contact your instructor with a “Doctor Note” or “personal health information”; neither are your instructor’s business. The Student Health Center has a “No medical excuses policy”. In the event of an attendance issue, please contact the Office of Student Life, which can send an attendance memo to your instructor(s) on your behalf. If you are absent for 3 weeks (approx. 6 days of class), the Office of Student Life recommends that “it may be best to consider withdrawing from all courses until you are able to return and focus on your academic goals”.

Submitting assignments

Submissions will be on Canvas, or in-class.

How will I know how I’m doing in this course?

Grades will be posted in Canvas within ~1 week from the assignment close date (depending a bit on the complexity of grading a particular assignment, etc.). The Canvas Grades tool shows the grades given to individual assignments as well as your calculated course grade based on the assignments that have been graded thus far. If you have questions or need assistance, please contact your instructor.

Grade Scale

Your assignment and course grades will be determined using the following scale:

Grade Range
A+ 100% to 97%
A < 97% to 93%
A- < 93% to 90%
B+ < 90% to 87%
B < 87% to 83%
B- < 83% to 80%
C+ < 80% to 77%
C < 77% to 73%
C- < 73% to 70%
D+ < 70% to 67%
D < 67% to 63%
D- < 63% to 60%
F < 60% to 0%

Late work

Due dates fall into two categories:

  • “Soft” due dates - when the assignment is listed as being due on Canvas
  • “Hard” due dates - when the assignment closes on Canvas, and submissions are no longer allowed

Soft due dates are set to help you keep pace with the course. There is no penalty for submitting between the “soft” due date and “hard” due date. But once an assignment closes: we will not reopen it.

University Policies and Resources

The following resources are available to you as a student. Most of these also overlap with more-general Indiana University / Bloomington policies, so in general I’ll try to link to the primary sources as places for information. “Religious Observances” and “Accessible Educational Services”.

On-campus resources recommend calling 911 if there is an emergency, and non-emergencies fall under specific areas:

Conduct and Misconduct

In brief:

  • Work that you submit must be your own, and authentic to you (not something your friend gave you, not something an LLM output)
  • Cheating, plagiarism, or unauthorized collaboration will be reported.

Misconduct cases are defined in Section II of the Code of Student Rights, Responsibilities, & Conduct.

We’ll try to provide as many resources as possible to you within the scope of the course (usage examples, specific sections of documentation, help pages, etc.). Work within the guidelines provided to you.

Generative AI/LLM Usage

This thing (generative artificial intelligence, large language models, ChatGPT, Claude, etc.) has a long history that we’ll try to discuss within this course. But the fact is: very few people were prepared for the “mainstreaming” of these techniques. All of us (you, your instructor, society) are figuring out what it means now that they are here.

We don’t have answers to some questions, because they are open or ongoing:

  • AI models are trained on data from the Internet, much of which was copyrighted and used without permission. The legality of this is in a word: thorny.
  • In the United States: Human inventions can be patented, and human creative work is automatically subject to copyright (it’s literally in Article 1 of the U.S. Constitution: “To promote the Progress of Science and useful Arts, by securing for limited Times to Authors and Inventors the exclusive Right to their respective Writings and Discoveries”). The U.S. Copyright Office’s guidance (c. January 2025) is that “material generated wholly by AI is not copyrightable”, and is contending with what AI-assisted authorship means.

Our overall goal is guided by “authenticity”, “transparency”, and “accessibility”. Your growth should be a function of what you know or what you can do. The point of this class isn’t to spend $200/month on the Claude Max plan or put $10,000 into computer parts in the middle of an ongoing RAM crisis.

By assignment group:

  • Quizzes: Not allowed.
  • Lecture checks: Not allowed. These are meant to be reflections on work we did together in-class, LLMs are prone to “hallucinating” or “making things up”. There may exist edge cases for “knowledge organization”: email your instructor before using in this case though.
  • Skills checks: Somewhat allowed, with transparency on usage.
  • Attendance: Not allowed. AI cannot attend your classes.

AI/LLM Transparency

Alexander Hayes will try to provide good examples, starting here. When using AI/LLMs/generative approaches, provide:

  • Tool Name - What did you use?
  • Contribution timeline - How did you use it? What did you do and how?
  • Full Trace - these can typically be exported as PDFs or Markdown, reviewing the session.

For example, your instructor did an intermediate “Tone Check” when writing the syllabus:

I adapted parts of an earlier syllabus by Samantha W. M. Wood (referenced in the above “Learning Outcomes”), and official course overviews from the university. When I had what I considered to be about 90% of the syllabus written (everything except the “Conduct and Misconduct” section + “Right to Document/Revision”), I used the google/gemma-4-26b-a4b model and LM Studio 0.4.21 with the following prompt:

Give me a quick tone and content check. I'm putting the final touches on reviewing my syllabus, and want to make sure it sounds okay and doesn't have anything majorly missing. Attached below:

<Syllabus>
...
</Syllabus>

Again, a quick tone and content check would be appreciated. Anything obvious I'm missing?

I incorporated 2 points from a “Content Check” section in the LLM output: mentioning “communications/office hours” were good points to include, but most of them would be on the Canvas home page (context I did not provide to the LLM). I tend to “jump around” when writing, so the “typos/formatting” caught were things I corrected (but the “Grading Scale” point in the LLM output was, once again, context that the LLM did not have — the IU course template 2.0 includes it).