V415 Data Science in Public & Environmental Affairs

Fall 2026 | Section 14147

In-person Lecture

Course Goal

By the end of the semester, students will be able to use practical data analysis methods to investigate real-world problems, evaluate evidence, and communicate findings that support better decisions. This course will help you learn how data is stored, organized, prepared, analyzed, and communicated for use in professional and public decision-making.

Course Description

Official IU Bulletin description: Course provides a practical experience for students to acquire useful, marketable skills in Data Science. Students will learn to effectively manage and analyze data with the purpose of finding solutions to difficult or complex issues and communicate findings to a larger audience.

Course Statement

Data is generated continuously by governments, businesses, healthcare organizations, nonprofit organizations, environmental systems, digital platforms, sensors, and everyday transactions. The challenge is not simply having data, but knowing how to turn it into useful evidence for decisions.

This course provides a practical introduction to data science for students who expect to work with data in professional settings. You will learn how to frame real-world problems as data questions, evaluate and prepare data, analyze patterns and trends, and communicate findings to nontechnical audiences. Much of the course involves hands-on problem solving using commonly available analytical tools and realistic workplace scenarios.

You will also learn to evaluate the quality and limitations of data, make defensible analytical choices, and use generative AI and other analytical tools responsibly. Throughout the course, the goal is not simply to produce charts or calculations, but to use data thoughtfully to support better decisions.

Learning Outcomes

By the end of the semester, students should be able to:

• Frame a workplace or policy problem as a data question and identify the information needed to address it.
• Locate and evaluate data sources for relevance, credibility, completeness, and fitness for purpose.
• Prepare and organize data for analysis by identifying and correcting common data-quality problems and restructuring or combining data when necessary.
• Analyze data using appropriate tools and methods to identify patterns, relationships, trends, and evidence relevant to a decision.
• Evaluate analytical results critically, including limitations, uncertainty, ethical concerns, and situations requiring additional expertise or more advanced analysis.
• Communicate data-driven findings effectively through appropriate visualizations, dashboards, summaries, and recommendations for nontechnical audiences.
• Use generative AI and other analytical tools responsibly, independently evaluating, validating, and documenting the assistance they provide.

Time commitment: As a standard 3-credit semester-length class, you are expected to spend an average of 6 or more hours outside of class per week on reading, studying, assignments, project work, and related activities.

Prerequisites & Expectations of Student IT Background

There are no formal prerequisites for this course. However, prior completion of V261 or K201 is highly recommended because those courses provide useful experience with Microsoft Excel and basic data-management concepts. Students who have not completed one of these courses can still succeed, but they should expect to spend additional time developing foundational spreadsheet and data-handling skills.

The course will use Microsoft Excel, Tableau, and other analytical tools. Students should enter the course with basic familiarity with spreadsheets, including entering and organizing data, using simple formulas, and managing files. No prior experience with Tableau is expected.

Students from non-technical backgrounds are welcome and should be able to complete the course successfully. Basic file management, including downloading, uploading, organizing, and storing files using local or cloud-based systems, is also expected.

No textbook is required. Readings, tutorials, data files, and other resources will be provided through Canvas. Selected Google Cloud Skills Boost/Qwiklabs may also be assigned.

How the Course Works

This course is activity-based and uses minimal lecture. Students will work individually, with rotating classmates, and in teams. Most class meetings include hands-on work with data, short discussions, or a Daily Deliverable. The course follows a recurring learning pattern:

Learn -> Practice -> Apply -> Reflect -> Transfer

Major activities are organized around authentic workplace problems. Students are expected to identify stakeholders, make analytical choices, work with imperfect data, justify consequential decisions, and communicate results in professional formats.

Evaluation of Student Performance

This course uses Specifications Grading for most assignments, combined with weighted course categories. Specifications Grading means that an item is evaluated as Complete or Incomplete based on whether it meets clearly stated essential requirements (the specifications), rather than earning points for each individual criterion. The purpose is to make expectations transparent and put students in the driver's seat for the grade they want to earn: students can see the level of completion required for each grade bundle and make choices accordingly. Complete does not mean perfect; it means the essential specifications have been met. An assignment marked Incomplete in Canvas is an assignment status and is different from an official university course grade of Incomplete (I).

Category	Weight	Purpose
Final Team Project	30%	Four project milestones plus the Final Project Portfolio and executive check-in/defense.
Workplace Challenges & Hackathons	30%	Four team-based Hackathons that become progressively less scaffolded.
Applied Practice & Individual Work	15%	Guided Excel, Tableau, data preparation, and analytical skill-building assignments.
Attendance & Participation	15%	Attendance and responsible participation in an activity-based course.
Engagement & Learning Checks	10%	Daily Deliverables, warm-ups, brief quizzes, reflections, and peer/team feedback.

Specifications Grading Statuses

• Complete - the submission meets the essential specifications and demonstrates a reasonable, good-faith effort. Complete does not mean perfect.
• Incomplete - the submission does not yet meet one or more essential specifications, is substantially unfinished, or was not submitted.
• Excused - the item is removed from the completion-rate calculation because of an approved circumstance or eligible TOKEN use.

Routine assignments do not include an automatic revision/regrading cycle. Students should make a valid effort to meet the published specifications on the first submission.

Grade Bundles

Your final course grade is the highest grade bundle for which you meet all requirements. Canvas provides a running weighted grade, but the bundle requirements below also apply.

Requirement	A Range	B Range	C Range	D Range
Final Team Project	All 4 milestones Complete + Final Project Portfolio Complete	At least 3 of 4 milestones Complete + Final Project Portfolio Complete	At least 2 of 4 milestones Complete + Final Project Portfolio Complete	At least 2 of 4 milestones submitted; Final Portfolio submitted
Hackathons	4 of 4 Complete	3 of 4 Complete	2 of 4 Complete	2 of 4 Complete
Applied Practice & Individual Work	85% Complete	75% Complete	65% Complete	55% Complete
Engagement & Learning Checks	85% Complete	75% Complete	65% Complete	55% Complete
Attendance	85% or higher	75% or higher	65% or higher	55% or higher

Within the highest eligible grade range, the Canvas grading scale will be used to determine plus/minus grades. The instructor may apply reasonable judgment when a student falls short of a bundle requirement by a single minor item or one class meeting.

TOKEN System

TOKENS provide a limited way to recover from an eligible Incomplete item without creating routine resubmission and regrading cycles.

• Students begin the semester with 0 TOKENS and may earn up to 5 TOKENS during the semester through announced enrichment or course activities.
• Using a TOKEN changes one eligible Incomplete item to Excused in Canvas. No resubmission is required unless an activity explicitly states otherwise.
• No more than one TOKEN may be used for a Hackathon.
• TOKENS may not be used to erase an absence, excuse Final Project milestones, or excuse the Final Project Portfolio.
• TOKEN-earning opportunities are intended to recognize additional effort and will be designed to require minimal additional grading.

Major Types of Student Work

Workplace Challenges & Hackathons

Four team Hackathons provide the major transfer assessments in the course and become progressively less scaffolded over the semester. Topics, datasets, and workplace scenarios are subject to change at the instructor's discretion to support course learning goals, data availability, or emerging opportunities.

• Hackathon 1 - NHTSA Fatal Crashes: Excel analysis, hypotheses, visual evidence, and an evidence-based recommendation.
• Hackathon 2 - IU Health Data Prep & Dashboard: data quality decisions, cleaning/preparation, exploratory analysis, and an interactive Tableau dashboard for administrators.
• Hackathon 3 - Global Terrorism Database: large-data exploration, student-generated analytical questions, visual storytelling, an infographic, recommendations, and limitations.
• Hackathon 4 - IU Health Predictive Analytics: forecasting, validation, uncertainty, decision support, and communication to leadership.

Exceptional Hackathon work that substantially exceeds the minimum specifications may earn each contributing team member a TOKEN. Meeting the normal Complete specifications does not depend on outperforming other teams.

Final Team Project

The Final Project is a workplace-style data consulting project. Teams select a meaningful problem, identify a stakeholder and decision, obtain and evaluate relevant data, prepare and analyze the data, communicate evidence, and make a defensible recommendation. Technical sophistication is not the goal; the quality of the question, evidence, judgment, and communication matters most.

• FP1 - Problem, Stakeholder & Data Proposal
• FP2 - Data Readiness & Analysis Plan
• FP3 - Preliminary Findings & Evidence
• FP4 - Final Readiness Review
• Final Project Portfolio - analytical work files, stakeholder-facing dashboard/visual product, infographic, one-page recommendation memo, recorded executive briefing, and AI use documentation.

During the final week of regular classes, teams will complete a brief executive check-in/defense with the instructor while other teams use class time as a Final Project Studio. The defense is not a second formal presentation; it is a short professional conversation used to verify that the team understands and can defend its work. The Final Project Portfolio is due on the last Saturday of the semester before final exams begin.

Applied Practice & Individual Work

Guided practice builds the technical foundation needed for the Hackathons and Final Project. Core assignments include Excel Skill Builder, Cleaning a Data Set Using Excel, Intro to Tableau: Food Environment Atlas, Tableau Prep Builder, Tableau Interactive Dashboard, and Tableau Data Set Analysis. Some assignments are intentionally procedural because novices need guided practice before they are asked to make independent analytical choices.

Engagement & Learning Checks

Daily Deliverables, warm-ups, brief quizzes, reflections, and peer/team feedback are low-stakes checks for participation and understanding. Daily Deliverables may change as the semester progresses so that class activities can respond to student needs. Quizzes and warm-ups are intended to introduce or reinforce concepts and may allow multiple attempts when appropriate. 

Daily Deliverables: Daily Deliverable deadlines will vary depending on the activity. Some activities, especially those involving teamwork or in-class participation, must be completed during class. Others may be submitted later the same day, and some warm-up activities may be due before class. The due date and time listed in Canvas will indicate the expectation for each activity.

Attendance and Participation

This course uses active learning strategies and techniques, with much of the learning occurring through in-class problem solving, discussion, teamwork, hands-on practice, and workplace-style activities. Because these experiences cannot always be fully recreated outside of class, regular attendance and active participation are important parts of the course.

Attendance will be recorded using Canvas Roll Call. Attendance standing is based on the percentage of scheduled class meetings attended:

A-level: 85% or higher
B-level: 75% or higher
C-level: 65% or higher
D-level: 55% or higher

There is no separate system for tracking excused absences. The attendance thresholds provide reasonable flexibility for occasional absences. Instructor discretion may be used in exceptional circumstances.

Late Submissions

Unless an assignment states otherwise, assignments and projects may be submitted up to one day after the posted due date without an additional grading penalty. Daily Deliverables are normally due by 11:59 p.m. on the day assigned. After the normal late window, an eligible TOKEN may be used according to the TOKEN policy. Requests for other extensions should be made before the due date whenever possible. No regular course work will be accepted after the official end of the semester unless required by an approved accommodation or other university policy.

Use of Generative AI

Generative AI tools (ChatGPT, Copilot, Gemini, and Claude) are encouraged in this course when used to support learning and analytical work. AI may be used to brainstorm questions, troubleshoot Excel or Tableau work, explain concepts, suggest possible approaches, improve communication, and assist with other tasks permitted by an assignment. The goal is to learn how to use AI effectively without delegating responsibility for the work to the tool.

• Document AI use when an assignment requires it, including prompts, links, screenshots, or a brief explanation of how the output was used.
• Independently verify AI-assisted formulas, calculations, data-cleaning steps, interpretations, and recommendations before submitting them.
• Be prepared to identify AI suggestions that were modified, rejected, or found to be unreliable.
• You remain responsible for the accuracy, quality, and ethical implications of all submitted work, including AI-assisted work.
• Submitting AI-generated work that you cannot explain, validate, or defend may result in an Incomplete and may constitute academic misconduct when it misrepresents authorship or violates assignment rules.

Students should not copy or upload assignment instructions, prompts, rubrics, or other Canvas course materials verbatim into a generative AI tool unless the instructor has explicitly permitted that use. Students may instead summarize the task in their own words or ask AI for general guidance, brainstorming, troubleshooting, or feedback consistent with the assignment requirements. When an assignment asks or permits you to use AI with its instructions or data, that permission applies only to the materials and purpose identified in the assignment.

Technology Use in Class

Laptops are required for many class activities. Students should use electronic devices for class-related purposes during class time. Non-class texting, messaging, video viewing, web browsing, or other activities that distract from learning or teamwork may affect participation. At least one phone should remain available for emergency campus alerts.

Course Schedule

A detailed course schedule with meeting dates, topics, Hackathons, major assignments, project milestones, holidays, and due dates will be provided as a separate document in Canvas. Daily Deliverables and some class activities may be adjusted during the semester to respond to student progress and current needs. The posted schedule is subject to limited change in the event of extenuating circumstances.

O'Neill School Student Honor Code

Introduction: The O’Neill School takes matters of honesty and integrity seriously. We expect O’Neill students will, at all times, maintain academic integrity and civil and professional standards of conduct.

Scope: O’Neill’s Student Honor Code applies to all courses and programs offered by O’Neill, and to all students who participate in O’Neill classes and programs, regardless of whether students are admitted to O’Neill. Students studying at O’Neill are expected to continue their commitment to ethical behavior in all their endeavors, including, but not limited to, coursework inside and outside of O’Neill, internships, community service, and overseas study. O’Neill’s Student Honor Code incorporates IU’s Code of Student Rights, Responsibilities, & Conduct and extends to matters of civility and professional conduct, including expectations about class attendance, punctuality, participation in class, and decorum in class and at other school functions.

Academic Conduct Requirements: All students enrolled in O’Neill courses are subject to the academic misconduct provisions found in the IU Code of Student Rights, Responsibilities, & Conduct. Academic misconduct is defined as any activity that tends to undermine the academic integrity of the institution. The IU Code lists and defines many types of academic misconduct, including cheating, fabrication, plagiarism, interference, violation of course rules, and facilitating academic dishonesty. Academic misconduct includes the use of any technology or assistance, including AI, that is not permitted by the instructor.

Civility and Professional Conduct Requirements: O’Neill requires that all students treat others with mutual respect and behave professionally in the classroom and during all academic and non-academic activities.

• Students must act in accordance with the O’Neill School mission and values.
• Students must regularly attend and be fully prepared for class, including completing readings.
• Students must not behave in a way that interrupts or distracts from teaching or learning. Students should be punctual, present, and attentive for the duration of class unless an emergency exists.
• Students must abide by the course policy regarding use of electronic devices.
• Students must responsibly participate in class activities and during team meetings for course assignments.
• Students must address faculty members, other students, and others appropriately and with respect, whether in person, in writing, or in electronic communications.
• Students must show tolerance and respect for others.
• Students must practice respect and responsible stewardship of school resources and the physical environment.
• Students must comply with the personal misconduct responsibilities within the IU Code of Student Rights, Responsibilities, & Conduct.

Reporting: Students must abide by the O’Neill Student Honor Code themselves and hold their peers to the same standards. Students should report suspected instances of academic, civility, or professional misconduct to the instructor of the course. Students should report suspected instances of personal misconduct to the IU Office of Student Conduct.

Academic Integrity and Course Materials

Students are responsible for the academic misconduct standards in the IU Code of Student Rights, Responsibilities, & Conduct (studentcode.iu.edu). The course AI policy above defines permitted AI use for this class.

Course Materials - Instructor’s Exclusive Rights. The faculty member teaching this course holds the exclusive right to distribute, modify, post, and reproduce course materials, including all written materials, study guides, lectures, assignments, exercises, and exams. While you are permitted to take notes on course materials and lectures for your personal use, you are not permitted to re-post in another forum, upload to AI except as explicitly permitted for a course activity, distribute, reproduce, or sell the instructor’s course materials without express written permission. Violations may be reported as academic misconduct and may result in course and/or university sanctions.

University and Course Policies

Late Withdrawal

After the auto-W deadline, withdrawal will be significantly limited, and you will need permission to withdraw and must meet requirements established by O’Neill. See the official IU academic calendar for current dates.

Counseling and Psychological Services

For information about services offered to students by CAPS: http://healthcenter.indiana.edu/counseling/index.shtml

TimelyCare

Indiana students have free, 24/7 access to virtual mental health care services with TimelyCare. Students do not need insurance to access TimelyCare services. https://www.iu.edu/mental-health/find-resources/timely-care.html#0

Religious Accommodation Requests

The “Request for Accommodation for Religious Observances” form must be submitted by the student to the professor no later than two weeks prior to the anticipated absence. A separate form must be submitted for each absence and for each course. Faculty do not have to consider accommodations for the purpose of allowing students to travel away from Bloomington for religious observance. Information: https://vpfaa.indiana.edu/resources/religious-observances-information.html

Accessible Educational Services (AES)

Securing accommodations for a student with disabilities is a responsibility shared by the student, the instructor, and the AES Office. For information about support services or accommodations and required procedures: https://studentlife.indiana.edu/care-advocacy/iub-aes/index.html

Sexual Harassment / Sexual Misconduct Resources

As your instructor, one of my responsibilities is to help create a safe learning environment on our campus. Title IX and IU Sexual Misconduct policy prohibit sexual misconduct. If you have experienced sexual misconduct, or know someone who has, the University can help. Confidential resources include the Sexual Assault Crisis Service (812-855-8900), Counseling and Psychological Services (812-855-5711), Confidential Victim Advocates (812-856-2469), and IU Health Center (812-855-4011). More information: http://stopsexualviolence.iu.edu/help/index.html. Federal regulations and University policy may require the instructor to convey information about potential sexual misconduct to appropriate Title IX officials, who will work to provide support while protecting privacy to the extent possible.

IU Culture Centers

Information about IU culture centers and communities is available at: https://studentlife.indiana.edu/communities/culture-centers/index.html