Logistics:

* Lecture: Nau Hall 101, 2-3:15 pm Mon & Wed
* Lab: Data Science 206 or 306 @ 12, 1, 2, or 3 pm

Instructor Information:

* Professor Brian Wright ([email protected])
* Office Hours: Tuesdays @ 2:00 - 4:00 pm, Data Science 434
* TA Office Hours: Amelia and Anna on Monday 12:30-1:30pm. Aidan and Carissa on Friday 10:30am-11:30am

Miscellaneous:

* SIS Title: Foundation of Data Science
* Subject Area and Catalog Number: Data Science, DS 1001
* Year and Term: 2025 Spring
* Level and Credit Type: Undergraduate, Grade (A-F)

N.B. There is an associated lab class that must be enrolled simultaneously (50 minute Laboratory section, Friday @ 12, 1, 2, or 3 pm).

This course introduces students to a broad range of foundational topics and skills that form the data science pipeline. A central feature of the course is a guided, semester-long project that allows students to continuously apply what they learn in a practical context. Our guide will be the Virginia Model of Data Science. Students will engage with the material through lectures, small group discussions, hands-on lab activities, and guest presentations by industry experts, all designed to support and enrich their understanding and skills. Throughout the semester, students will learn to think like data scientists - approaching problems methodically, making evidence-based decisions, and applying analytical techniques to uncover insights. Students will develop a variety of competencies, including setting up a computing environment, creating data visualizations, developing research questions, building models, and examining biases throughout the data science process. In the final weeks, students will repeat this process independently by designing and executing their own project, reinforcing their skills and deepening their understanding through self-directed practice.

• Prime Learning Objective: You will be able to Think Like a Data Scientist (TLaDS). This starts with being able to define Data Science and explain it to friends and family, grows to being able to describe the field of Data Science and its emerging sub-fields, and finally approaching a problem as a data scientist in your final project.
• Secondary Objective: Identify how you see yourself in the field of Data Science. (Ranging from "I love it and want a career" to "I hate it, never again", our goal is for you to understand the mindset of Data Science and figure out if it is a path you want to follow).

In each of the areas we will specifically focus on:

• Systems

  • Students Will Be Able To explain the components of a computer system and how they interact to support data science work.
  • SWBAT compare hardware and software choices to choose the best tools for a problem.
  • SWBAT use an IDE (Integrated Development Environment) to solve simple problems.

• Design

  • SWBAT create and interpret data representations using models, databases, graphics, and visualizations.
  • SWBAT identify key experimental design choices that happen throughout the DS pipeline and how those choices impact the result.
  • SWBAT use data summary and visualization tools to understand and describe a problem.

• Analytics

  • SWBAT define and give examples of a Data Science model.
  • SWBAT describe the key types of Machine Learning and their uses.
  • SWBAT distinguish between target/feature variables, identify true/predicted values, and interpret common evaluation metrics in a model.

• Value

  • SWBAT describe the 5 Data & Society subfields and their relevance to data science practice.
  • SWBAT analyze how responsibility for decisions is distributed across stages of the ML pipeline.
  • SWBAT identify common sources of bias and error in data and models across the ML pipeline.

Every graded assignment in this class falls into one of 4 categories outlined below. Each assignment has a rubric to indicate the purpose, task, and criteria for the assignment. They are graded using the specifications grading system based on Specifications Grading By Linda Nilson. We will spend time in class to help you understand this system, especially if it is new for you.

Specifications Grading has been demonstrated to provide much greater equity in the classroom and as a result improves achievement of learning objectives. However this system may be new to you. It does take some time to understand and we are ready to help you with any questions you may have. Please take advantage of office hours. You can read more about the specifications grading policy here.

This course does not have exams or the concept of "points". Instead there are bundles of assignments, that when completed earn letter grades. Every individual assignment is marked as "meets spec" or "does not meet spec yet". In order to understand what "meets spec", every assignment is accompianed by a single-level rubric that outlines three things:

1. The purpose (Why am I doing this?)
2. The task (What am I going to do?)
3. The criteria (A detailed description of the necessary components of a "meets spec" submission)

The goal is to make the assignments as transparent as possible and not withold information from the students. For example in a traditional exam the students are told a list of subjects that may be on the test. The uncertainty causes anxiety and wastes an immense ammount of time. Instead of giving exams this class provides clear instruction on tasks to perform which yield the same result (mastery of learning objectives) without the anxiety and timewasting of studying for the unknown.

The following table summarizes the assignmenmts required to "meet spec" to earn certain letter grades.

The LABS and READ assignments for each week are due that Friday at 11:59pm on Canvas every week.

• LABS - There are 12 labs and all grade bundles require completion of all 12 labs. They are designed to be done in class and may require some additional polishing done outside of class time. There is a make up lab day and any student may make up a lab that day for any reason, no excuse is necessary.
• READ - The READ assignments are posted to Canvas a week before the due date and take less than two hours to complete. Late submissions are not accepted. Extenuating circumstances can be excused after discussion with Professor Wright during office hours. We suggest completion of these assignments well in advance of the deadline.

1. LABS - there is a lab section for this course and every student is expected to enroll and complete the lab assignments. The definition of "lab" is loose as the assignments performed in the lab sections vary. The goal is for the majority of the work to be done during the class lab period, but some final details like formatting and reflection may need to be done outside of class time.

2. "READ" and Review - every week supplemental material will be posted to enhance the in class activities. This is not just reading but can also include other forms of media. The deliverable for this assignment is a short review and reflection.

3. CASE Study Assignment - Each module has two case study assignments. These assignments are designed to dive deeper into material covered in class or to explore material not covered in class. They will require using external resources and some significant self-directed research. You’ll have some choice in the topics - so pick the ones that interest you most.

   For the A bundle you can do 4 cases but no more to work to get 3 above spec, for the B bundle you can do 2

4. Final Project - This assignment is the your opportunity to synthesize the semester and show mastery of the primary learning objective "thinking like a data scientist". This will reflect the project work done throughout the semester.

1. Labs: There is a make up lab day at the end of the term where students can complete a lab they were unable to complete. No excuse is necessary.
2. Resubmission: After grading, weekly assignments (LABS & READ) marked "does not meet spec, yet" can be revised and resubmitted once for full credit. For some submissions that are far off the mark an office hour visit may be necessary before resubmission is granted. CASE and FINL assignments can not be revised.

There are several technological tools used in this class:

• Email: Official communication from UVA is sent via UVA email
• Canvas: The official Learning Management System for this course is Canvas, all assignments are accessed, submitted, and graded through Canvas (including lab assignments). Course announcements will also be sent through Canvas, so make sure your notifications are turned on, or you are checking Canvas regularly. Assignments submitted outside of Canvas will not be accepted.
• Personal Computer: We will using online tools, compiling documents, and doing some introductory coding in this class. Your laptop does not need any special hardware or software for our work.

This is a tentative schedule, subject to change.

• 9/5 - This is the first lab day and the first READ assignment is due.
• 12/5 - Lab makeup day
• 12/9 - Final due date for all assignments

• Systems - Professor Karsten Siller
• Design - Carrie O'Brien: User Experience & Interface Designer at Capital One: Messaging, Web, and Human-Centered Design
  Professor Nur Yildirim
• Analytics - N/A
• Value - Professor Brent Kitchens
• Additional - TBD

Think Like a Data Scientist

• Chance, Logic, and Intuition by Tijms
• The 4+1 Model of Data Science by Alvarado
• [https://arxiv.org/abs/2311.03292](Data Science from 1963 to 2012)
• The Mathematical Theory of Communication by Shannon and Weaver
• Academic Bibliography: (https://github.com/UVADS/DS1001/blob/main/Datalogy.bib)

Systems

• Fundamentals of Data Engineering by Reis and Housley

Design

• The Design of Everyday Things by Norman
• How charts lie : getting smarter about visual information
• Observe, Collect, Draw! by Lupi and Posavec

Analytics

• R for Data Science
• Python for Data Analysis
• Probability: Basic Probabilty by Tijms

Value

• Weapons of Math Destruction

Attendance:

• Lecture: Every student is responsible for the material covered in lecture. Attendence during lecture is expected and the material is integrated with the READ/LABS assignments that week and CASE Study assignments for that module. On Mondays READ & LABS assignments will be reviewed. On Wednesdays we will preview Friday's lab to show the connection between lecture and lab. In the event of a missed class we strongly advise reviewing the material before the next lab period (a friend's notes, office hours, etc.).

• Lab assignments can only be completed in class on Friday (there is often special equipment and the need for a partner). There is a lab makeup day on the final Friday of classes during the normal lab times. No excuse is needed for a missed lab, you are already granted permission to make it up on makeup day.

Honor:

• University of Virginia Honor System: All work should be pledged in the spirit of the Honor System at the University of Virginia. The instructor will indicate which assignments and activities are to be done individually and which permit collaboration. For more information, visit www.virginia.edu/honor.

GenAI:

• Generative AI (GenAI) tools can be a helpful part of your workflow when used well. Think of them like a study partner: great for clarifying questions, polishing wording, or giving feedback. What they shouldn’t do is replace your own learning, so it is inappropriate to ask it assignment questions, copy large sections of output, or pass off GenAI ideas as your own. A good rule of thumb: if you wouldn’t do it with a friend, don’t do it with GenAI. When in doubt, check with a TA or instructor.

Course Evaluations:

• Student feedback is critical to the school, the instructor, and future students. Students are expected to complete anonymous and confidential course evaluations in a timely manner for each course at the end of each term.

Discrimination/Harassment/Retaliation:

• UVA prohibits discrimination and harassment based on age, color, disability, family medical or genetic information, gender identity or expression, marital status, military status (which includes active duty service members, reserve service members, and dependents), national or ethnic origin, political affiliation, pregnancy (including childbirth and related conditions), race, religion, sex, sexual orientation, veteran status. UVA policy also prohibits retaliation. All faculty and TAs are also responsible employees for disclosures or reports of potential discrimination, harassment, and retaliation.

Disability and Pregnancy Accommodations:

• If you anticipate or experience any barriers to learning in this course, please discuss your concerns with me. If you have a disability, or think you may have a disability, contact the Student Disability Access Center (“SDAC”) to request reasonable accommodation(s) for this course through their website. If you have accommodations through SDAC, send me your Faculty Notification Letter as soon as possible and meet with me so we can develop an implementation plan together.

• Students may be entitled to reasonable accommodations for pregnancy, childbirth, or related medical issues. Please contact SDAC for additional information. Pregnant and parenting students are encouraged to contact SDAC or EOCR to discuss plans and ensure ongoing access to their academic courses and program. Information for pregnant and parenting students is also available on EOCR’s Pregnancy and Parenting Resources webpage.

Religious Academic Accommodations:

• UVA also provides reasonable accommodations when a student’s sincerely held religious beliefs or observances conflict with academic requirements. Students who wish to request an academic accommodation for a religious observance should submit their request to me by email as far in advance as possible.

• If you have questions or concerns about your request, you may contact EOCR at [email protected] or (434) 924-3200 or visit their Religious Accommodations webpage for additional information. Please note that receiving an accommodation does not relieve you of your responsibility to complete any coursework you miss as a result of the accommodation.

Reporting an Incident:

• Just Report It (JRI) is the University's online system for reporting:

  • Sexual and Gender-Based Harassment and Violence
  • Discrimination, Harassment, and Retaliation
  • Hazing
  • Clery Act Compliance (by CSAs)
  • Interference with Speech Rights
  • Youth Protection
  • Preventing & Addressing Threats or Acts of Violence

• You may access Confidential Resources if you wish to discuss a concern or incident without reporting to the University. Information you share with Confidential Resources will not be disclosed to University officials or any other person except in extremely limited circumstances. For more information on reporting an incident, visit this website.

Student Mental Health and Wellbeing:

• The University of Virginia is committed to advancing the mental health and wellbeing of its students, while acknowledging that a variety of issues directly impacts students’ academic performance. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, contact the CAPS Care Managers at [email protected].
• For help finding a community therapist, visit the Community Referrals page through CAPS. Care and Support Services (CASS) is a non-clinical resource available to all students providing emergency resources and support (withdrawing/returning to UVA, housing resources, food insecurity, emergency funding, safety resources). The CASS team has a 24/7/365 phone line reserved for urgent non-clinical student needs that cannot wait until the next business day.
  • Monday-Friday, 8 a.m. to 5 p.m.: (434) 924-7133
  • After Hours: University Police Department: (434) 924-7166, ask for CASS on Call