DATA 133 is an interdisciplinary introduction to thinking and working with data in an AI-enabled world. Students learn how to ask meaningful questions, evaluate evidence, work with real datasets using Python, create and critique visualizations, identify bias and limitations, use generative AI responsibly, and communicate defensible conclusions. Examples and projects draw from business, natural and social sciences, humanities, and other disciplines. This course is intended for students with NO prior programming experience.
Understand and critically evaluate data, measurement, evidence, visualization, claims, bias, and uncertainty.
Use Python, notebooks, and pandas to inspect, manipulate, summarize, explore, and visualize real data.
Use generative AI productively without blindly trusting it: inspect, test, verify, explain, and appropriately disclose AI assistance.
Reason about provenance, privacy, consent, representation, fairness, ethics, limitations, and communication.
There is no required paid textbook for this course. Lecture slides, notebooks, datasets, selected open readings, and other materials will be provided by the instructor. These materials are an important part of the course and students are expected to review them as assigned.
Section 1: Tuesday, Thursday 10:00-11:30, Morken #203
By the end of DATA 133, students will be able to:
Previous computer programming experience is NOT a prerequisite for this course!
The official prerequisite for this course is four years of high school math or MATH 140 or equivalent.
DATA 133 is designed both for students considering Data Science and for students who want to use data and AI thoughtfully in another field. Students from business, natural sciences, social sciences, humanities, and other disciplines are welcome. Prior Python experience can be helpful but is not assumed.
We will primarily use the following tools:
R may be introduced briefly as part of the broader data-science ecosystem, but it is not a primary programming language in DATA 133. The goal is not to become a Python syntax expert; the goal is to learn how computational tools can help us investigate data questions.
DATA 133 meets for approximately 90 minutes per class. Most meetings combine a motivating question or case, focused instruction, guided data/code work, student analysis, discussion, and synthesis. Students should expect to work with data during class rather than only listen to lectures.
A typical class may move through: Big Question → Prediction/Discussion → Concept → Data/Code → Student Challenge → Interpretation → Takeaway.
You are expected to attend and participate in class. Many meetings include collaborative data investigations, discussions, coding activities, and short in-class exercises that are difficult to reproduce by simply reading slides afterward. You are responsible for material and activities covered during class. If you must miss class, please communicate with the instructor and make reasonable arrangements to catch up.
Course materials, assignments, announcements, and other helpful information will be made available through the course website and/or the university-supported course learning system. I may also contact you through your PLU email address. Please check course announcements and email regularly. You are encouraged to contact me with questions or to arrange a meeting outside regular office hours.
Generative AI is part of the subject matter of DATA 133. The goal is neither to ban AI nor to accept its output uncritically. You will learn to use AI as a tool while remaining responsible for the quality and integrity of your work.
Assessments may use one of three modes:
As members of the PLU community, we share responsibility for creating a safe, inclusive classroom environment that encourages exploration, disagreement with ideas rather than people, and meaningful participation. Technology, including AI, should be used in ways that support rather than distract from learning.
Your grade will be based on the following:
| Component | Weight | Purpose |
|---|---|---|
| Labs and Data Challenges | 25% | Hands-on practice with Python, data, visualization, evidence, and AI/data-literacy challenges. |
| Short Quizzes | 15% | Individual checks of conceptual understanding, code reading, interpretation, and data literacy. |
| Mid-term / Individual Data Literacy Assessment | 15% | Individual assessment of foundational data reasoning, Python/pandas understanding, interpretation, and verification skills. |
| Mini Data Investigation | 10% | A small end-to-end investigation emphasizing a good question, data quality, exploration, and defensible findings. |
| Final Data Story Project | 25% | An interdisciplinary project applying the complete DATA 133 workflow to a question and dataset of interest. |
| Final Presentation | 5% | Communication of the final data story to an audience. |
| Participation / In-class Activities | 5% | Engagement in data investigations, discussion, peer feedback, and class activities. |
The culminating project asks you to investigate a meaningful question using data. You may choose a domain such as business, biology/health, psychology/social science, environment, humanities, sports, or another approved area. Machine learning is not required.
The project will include a short AI Use & Verification Statement describing where AI was used, what it produced, how the output was checked, and what you changed or rejected.
The schedule below is organized as 28 approximately 90-minute class meetings. Exact dates, deadlines, and minor topic adjustments may be announced during the semester.
| # | Topic / Big Question | Key Ideas | Activity / Connection |
|---|---|---|---|
| 1 | What Is Data Science? How do data become evidence and decisions? | Data, observations, variables, data-science lifecycle | Where is the data? challenge |
| 2 | Can We Trust Numbers? When is a true number misleading? | Mean/median, percentages, rates, denominators, context | Data Detective |
| 3 | What Makes a Good Data Question? | Descriptive, comparison, relationship, predictive, causal questions | Rewrite vague questions |
| 4 | Can Data Be Neutral? | Measurement, representation, missingness, selection | What should we measure? |
| Module 2 — Humans + Python + AI | |||
| 5 | What Can AI Actually Do? | LLM literacy, hallucination, strengths and limitations | Compare and audit AI answers |
| 6 | How Do We Talk to a Computer? | Jupyter, variables, types, expressions | Python Data Playground |
| 7 | Can You Read Python? | Lists, indexing, methods, functions, conditions | Read → predict → modify |
| 8 | AI Wrote the Code—Should You Trust It? | Debugging, testing, verification, simple iteration | AI Code Clinic |
| Module 3 — Working with Real Data | |||
| 9 | How Do We Look Inside a Dataset? | Rows, columns, DataFrames | read_csv, head, shape, info |
| 10 | How Do We Find What Matters? | Selection, filtering, sorting | Dataset query challenge |
| 11 | What Does "Typical" Mean? | Count, mean, median, grouping, aggregation | Compare groups |
| 12 | This Dataset Is Lying to You | Missingness, duplicates, types, invalid values | Data Crime Scene / Mini Investigation |
| Module 4 — Seeing Patterns | |||
| 13 | What Does a Distribution Tell Us? | Shape, center, spread, outliers | Distribution investigation |
| 14 | Are These Groups Really Different? | Comparison, aggregation, variation | Cross-domain group comparison |
| 15 | Which Visualization Should I Use? | Bar, histogram, boxplot, scatter, line | Chart selection studio |
| 16 | Can a True Graph Still Lie? | Axes, scales, cherry-picking, aggregation | Visualization Makeover |
| Module 5 — Evidence & Uncertainty | |||
| 17 | Where Did This Data Come From? | Population, sample, surveys, selection bias | Poll/survey investigation |
| 18 | Does A Move With B? | Association, correlation, scatterplots | Relationship investigation |
| 19 | Did A Cause B? | Confounding, observational studies, experiments | Causality Detective |
| 20 | How Certain Are We? | Variation, uncertainty, sampling intuition | Simulation / bootstrap intuition |
| Module 6 — Data, AI & Society | |||
| 21 | Who Is Missing From the Data? | Representation, historical bias | Dataset audit |
| 22 | Who Owns Your Data? | Privacy, consent, PII, tracking | Privacy case |
| 23 | Can an Algorithm Be Fair? | Fairness, competing goals and tradeoffs | Hiring/lending case |
| 24 | When AI Analyzes Your Data | Hallucination, fabricated evidence, leakage, validation | AI Analysis Audit |
| Module 7 — From Data to Knowledge | |||
| 25 | Where Does Data Actually Come From? | CSV, Excel, JSON, APIs, databases, sensors, web | JSON/API → DataFrame experience |
| 26 | How Do We Make a Defensible Claim? | Evidence, uncertainty, limitations, overclaiming | Claim–Evidence–Limitation workshop |
| 27 | How Do We Tell a Data Story? | Audience, narrative, visualization, recommendation | Final-project peer review |
| 28 | What Did We Learn From Data? | Synthesis, communication, reflection | Final Data Story presentations |
All submitted work must represent your own understanding and contribution. Collaboration is encouraged when an activity explicitly permits it, but students may not submit another person's work as their own. The same principle applies to AI-generated material: permitted AI assistance does not transfer responsibility for the work from the student to the tool. Assignment-specific collaboration and AI rules take precedence when stated.
We adhere to PLU's Academic Integrity policy as stated in the student handbook: Academic Integrity. If you are uncertain whether a form of collaboration, reuse, or AI assistance is permitted, ask before submitting.
Our goal is to challenge you while providing resources that help you succeed. Your instructor is a primary resource: use office hours, email, or arrange an appointment when you need help. University academic-support resources are also available to students. Current support information will be provided through PLU resources and the course site.
If weather or other conditions affect university operations, follow current PLU announcements and course communications. Use appropriate caution and personal judgment when travel conditions are unsafe.
Students who need disability-related accommodations should work with the appropriate PLU accessibility/disability support office and communicate approved accommodations to the instructor. Please consult current PLU resources for procedures and contact information.
Add/drop, withdrawal, pass/fail, and other registrar deadlines vary by semester. Please consult the current PLU academic calendar and registrar information for official deadlines.