• Identify instances of manipulated or misleading data

• Develop skills in data analysis and visualization using R

• Critically evaluate metrics and numbers used in everyday decision-making

• Collaborate effectively on data-driven projects

• Communicate data insights through various formats (presentations, visual displays, written reports)

Crystal Hall (she/her) chall@bowdoin.edu 

Office hours in person: Tuesdays 2:45-4:15, Fridays 1-2, and by appointment

Office hours by Zoom outside of the above: https://calendly.com/prof-chall 

Office: Mills Hall 110

Vianney Gomezgil Yaspik (she/her) v.gomezgilyaspik@bowdoin.edu 

Office hours: Tuesdays 3:00pm - 4:15pm & Wednesdays (time TBD) 

Office: Mills Hall 211

Hear my name 

Victoria Figueroa : vfigueroa@bowdoin.edu , LA hours Wednesdays: 7-9pm

Madina Sotvoldieva: msotvoldieva@bowdoin.edu , LA hours Mondays 6-8pm

Room: Mills 105

Assignments & Grading Structure

• • Number Presentation: 10%

  • Dear Data Activity (10 weeks): 20%

  • Problem Sets (4 total): 10%

  • Mid-term Exam (Week 6): 5%

  • Second Exam (Week 10): 5% 

  • Stakeholder Project Presentation (Week 11): 20%

  • Final Group Presentation (Week 14): 10%

  • Individual Final Submission (end of scheduled Final Exam Period): 10%

  • Participation and Engagement (includes occasional reading quizzes): 10%

• Course Deadlines  

In this list, you’ll find all the key deadlines for assignments and activities that every student in the course is expected to complete. This list does not include readings or “Behind the Number” presentations, as those vary by student. Use this as a quick reference to stay on track throughout the semester.

Tuesday, Sept 16 First Dear Data Postcard Due – Health/Movement 

Thursday, Sept 18 Problem Set 1 Due 

Tuesday, Sept 23 Second Dear Data Postcard Due – Assessment/Learning 

Tuesday, Sept 30 Third Dear Data Postcard Due – Age/Development 

Thursday, Oct 2 Problem Set 2 Due 

Tuesday, Oct 7 Fourth Dear Data Postcard Due – Assessment/Learning 

Thursday, Oct 9 Exam #1 in class 

Tuesday, Oct 21 Postcard #5 Due - Authority/Trust 

Thursday, Oct 23 Problem Set 3 Due 

Tuesday, Oct 28 Postcard #6 Due – Risk/Safety 

Tuesday, Nov 4 Postcard #7 Due – Community/Place 

Thursday, Nov 6 Problem Set 4 Due 

Tuesday, Nov 11 Postcard #8 Due – Global Theme 

Thursday, Nov 13 Exam #2 in class 

Tuesday, Nov 18 Postcard #9 Due – Happiness/Wellbeing 

Tuesday, Dec 2 Last Dear Data Postcard (#10) Due – Free Topic 

Tuesday, Dec 9 Stakeholder Presentations - Submission of slides for all groups before class

Thursday, Dec 11 Stakeholder Presentations

FINAL Exam Slot Stakeholder Project Due

You can request two excused absences due to health-related issues. If you need more accommodations, please, reach out to your academic dean, and they will work with you and your instructors on the best strategies to address your needs.

Since our in-person meetings are core components of the course pedagogical strategy, 2 percentage points will be deducted from your final grade for each unexcused absence. To be clear, absences beyond the first two can only be excused by the dean’s office. If, for example, you tell a professor that you will be away for the next class and they say “ok”, they are not excusing your absence, they are merely acknowledging it.

What counts as Generative AI?

Generative AI encompasses all of the artificial intelligence systems that can create new content like text, code, images, audio, and other types of media. This includes but is not limited to:

• Large language models (ChatGPT, Claude, Gemini, etc.), apps and agents derived from them, and aggregator interfaces (Amplify, LibreChat, etc.)

• Code generation tools (GitHub Copilot, Google Colab, Cursor, etc.)

• Image generators (DALL-E, Midjourney, etc.)

• AI-powered writing assistants and autocomplete features

• NotebookLM, Quizlet AI features, and other AI-enhanced study tools

Always acknowledge what generative AI tools were used to help you complete assignments.

How can generative AI help you to learn in this course?

• Converting slides and notes to study guides and podcasts (i.e. NotebookLM)

• Creating practice materials like flashcards (i.e. Quizlet) and mock quizzes or exams

• Proposing a study plan

• Providing alternative explanations or examples

• Suggesting refinements to grammar and corrections to spelling

• A guide to debugging code

How can generative AI impair your learning in this course?

• Reduces critical thinking and problem-solving abilities - overreliance on AI for analysis can prevent you from developing your own reasoning skills and prevent you to work through problems independently

• Can limit creativity

• Creates a sense of false confidence - AI-generated work almost always seems correct but can contain small errors or miss a deeper understanding

• Creates dependency 

• Reduces retention

• Limits research skills

• It denies you the possibility of self-expression in a welcoming and constructive learning environment.

• For coding-intensive projects, AI tools often miss the layers of design that go into solving a problem and instead produce a “one-shot” solution that is hard to understand and debug

Other costs of using AI

• Environmental impact - AI systems use very large amounts of energy to train the models and operate the apps and interfaces

• Labor exploitation - AI development more often than not relies on poorly paid workers in difficult work conditions for tasks such as content moderation, data labeling, etc. 

• Digital colonialism - most AI systems have been created by developed Western nations, which can potentially reinforce global inequalities and impose Western perspectives on other cultures/populations severely limiting your exposure to innovative and challenging ideas

• Digital divide - AI tools are widening the gaps between those with and those without access to advanced technologies

• Aggregation - AI is concentrated in the hands of a few large companies who are gaining outsized influence

• Privacy - Things you put into Generative AI systems often are fed into those systems. The history of AI companies suggests that despite their disclaimers your information may be kept private.

When should generative AI be used along with other tools?

• Finding resources to support your research 

Also use Bowdoin’s Compass and Google, for examples

• Testing discussion questions that you would like to pose to the class

Compare AI results to what you hear from classmates and the professor

• Checking your work and getting preliminary feedback

Use AI as a first pass, then seek “human” feedback from your classmates, LAs, or office hours

• Outlining

Create the initial “ingredient list” yourself, then generate initial structure with AI, then develop your own unique perspectives and arguments

Why do we give you assignments?

“We don’t assign essays because the world needs more student essays.” - Emily Bender

Assignments are designed to help you develop essential skills and ways of thinking that will serve you throughout your transformation as a student/learner. Think of each assignment as an opportunity to: practice critical thinking, develop your unique voice, learn from struggling, engage deeply with the course material (you might find your passion! or not…), demonstrate your learning, and more importantly prepare for future challenges, skills/tools/programs might change but developing deep critical skills will help you continue to adapt as tools change throughout time. 

When is generative AI not allowed?

• To write - Please avoid autocomplete as well. We want to hear your voice, not the most probable internet-based voice based on a temperature setting determined by programmers who have never taken our courses or studied at Bowdoin.

• To code - Unless stated otherwise, write the initial code yourself and use GenAI to help you debug.

• To brainstorm - Your perspectives are broader than this course, your background is richer than what LLMs can represent. Practice creating and evaluating new ideas.

• To solve problems - The problems presented in this class are designed to help you practice a process of critical thinking and evaluation. Taking a shortcut here only robs you of the opportunities to cultivate existing skills and learn new skills.

• As a search engine for “facts.” At the time of writing this document, generative AI tools have not demonstrated 100% reliability here. Always confirm.

• To analyze data or interpret results without your own critical thinking

• To generate ideas for creative projects or original research questions

• To complete any type of exams or quizzes unless explicitly permitted

• To translate assignments or course materials into other natural or formal languages without permission

What you can expect from your Professors

• We will design assignments and assessments that prompt you to assess and demonstrate your individual strengths

• We will read and evaluate your assignments using our own expertise and judgement

• We will clearly disclose any AI tools we use for course preparation (such as generating synthetic datasets)

• We will provide our personal guidance and support during office hours and email

Academic Integrity Expectations

• Be transparent about AI use 

  • When in doubt, mention its use

  • Submit your prompts with your assignments

• Understand that overreliance on AI will hinder your learning and skill development

• Violations of this policy will be treated as academic dishonesty

No screen use is permitted in class unless explicitly stated by the instructor or in cases of learning accommodations. This includes phones, tablets, and computers. Most courses will display a symbol during presentations that indicates when screen use is allowed. When screens are not permitted, all devices should be put away and out of sight. 

If you have a documented accommodation that allows the use of a laptop or tablet for note-taking, please make sure to submit the appropriate materials to the professor during the first 2 weeks. If you are interested in learning more about accommodations, please see Lesley Levy in the Office of Student Accessibility, https://www.bowdoin.edu/accessibility/. 

Similarly, reach out to your Professors about religious holidays so that alternative timelines for deadlines can be provided. 

Two Components:

1. Final Group Project (10%)

After Thanksgiving, students reform into mixed groups with representatives from each stakeholder team.

Week 13: Synthesis and Negotiation

• In-class guided activities for solution development

• Negotiation exercises to reconcile different perspectives

• Identification of missing datasets

• Dear Data-style visualization creation

• Documentation of abandoned alternatives

Week 14: Final Presentations

• 8-minute group presentations including:

  • Synthesized multi-stakeholder solution

  • Hand-drawn ideal visualization

  • Data-driven evidence supporting proposal

  • Identification of critical missing data

  • Explanation of one abandoned approach

• Audience participation through:

  • Structured note-taking worksheet

  • Peer recognition board contributions

  • Active engagement assessment

2. Individual Final Project (10%)

Individual Final Project (10%)

Due at end of scheduled exam period (exact time TBD)

Policy Recommendation for Bowdoin Course Reviews (1,500 words max):

• One executive summary (clear findings and recommendations)

Plus:

• Analysis of Bowdoin-specific contextual factors with public policy recommendation

• Data-driven proposal for Bowdoin course review system (why did you suggest these recommendations?)

• Citations from peer presentations (using class notes, as brief footnotes)

• Revised visualizations 

• Identification of one "lynchpin" artifact whose absence would fundamentally change recommendation

• In the Appendix R-script with all supporting code

Reflection Section Requirements (500 words max):

• Analysis of negotiation challenges and successes

• Identification of entrenched positions and potential solutions

• Advice for future students on managing human elements of data work

• Personal definition of "data-driven society"

• Connection to course readings and discussions

Submission Format:

• Online via Canvas

• 1500 word report (excluding code and visualizations)

• 500 word reflection

• Jupyter notebook with documented analysis code

• Short video showing the running of the code

• Portfolio of revised visualizations

• Executive summary (250 words)

• Bibliography of course materials and peer work