Coordinates:
* Lecture: Nau Hall 101, 14:00-15:15 Tu & Tr
* Lab: Data Science 206 or 306 @ 10:00, 11:00, or 12:00
Instructor Information:
* Professor Peter Alonzi (lpa2a)
* Office Hours: 14:00-16:00 on Wednesdays in Data Science 344 (Modified times after 4/30)
* Cursor Club: 16:00-17:00 location TBD in Data Science based on attendance
Miscellaneous:
* SIS Title: Foundation of Data Science
* Subject Area and Catalog Number: Data Science, DS 1001
* Year and Term: 2025 Spring
* Course Title: Think Like a Data Scientist (TLaDS)
* Level and Credit Type: Undergraduate, Grade (A-F)
N.B. There is an associated lab class that must be enrolled simultaneously (1 hour Laboratory section, Friday @ 10,11,12).
This is a survey course about the emerging field of Data Science. Our goal is to teach you how to Think Like a Data Scientist (TLaDS). In this class we will go beyond the Data Science analytics. We will dig into the human and ethical aspects necessary for sucess in today's data-driven world. Our guide will be the Virginia Model of Data Science described in this article (link). On a weekly basis we will explore ideas in lecture and in lab as well as with supplementary readings. Larger projects take the place of traditional mid-terms and allow you to dive deep into the particular areas of Data Science that most appeal to you. The final work in the class focuses on the future. You will discuss how you see Data Science today, where you think it is going, and what role you could imagine playing in the years to come.
- 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 defining problems the way a data scientist would.
- 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:
-
Design
- Theme: Problem Solving and Storytelling -- What's the goal? What do I leave in and what do I leave out?
- LO: Observe the world around you and record your observations in a systematic way to solve a problem (aka how to create a data set)
- LO: Demonstrate the principles of establishing a dataset (aka how to evaluate the quality of a dataset)
- LO: Reflect on a data set and transform it for efficient communication to humans (aka how to communicate a data set)
-
Value
- Theme: With great power comes great responsibility
- LO: Be able to reflect on and articulate the benefits and concerns of data-driven decisions
- LO: Describe scenarios that allow for human-centered data science
-
Systems
- Theme: Scaling -- What scale is necessary?
- LO: Identify the hardware and software components of a computer and describe their function
- LO: Describe the different scales of computer operation
-
Analytics
- Theme: Analyzing -- Garbage in, Garbage out
- LO: Describe the ecosystem of data models
- LO: Articulate a typical algorithm life cycle
Every graded assignment in this class falls into one of 5 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.
This course uses the grading policy known as specifications grading. It has been demonstrated to provide much greater equity in the classroom and as a result improves achivement 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. For additional reading on this policy see link.
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:
- The purpose (Why am I doing this?)
- The task (What am I going to do?)
- 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.
| Code | Assignment Type | # | Avg. Time | C | B | A |
|---|---|---|---|---|---|---|
| LABS | Labs | 12* | In class | 12 | 12 | 12 |
| READ | "Read" & Review | 12 | 2 hours | 10 | 11 | 12 |
| CASE | Case Study Extension | 4 | 8 hours | -- | 1 | 2 |
| LOOK | Look Ahead | 4 | 8 hours | -- | 1 | 2 |
| ESSY | Final Essay Parts | 3 | 4 hours/part | 1 | 2 | 3 |
Weekly Assignments
- LABS - There are 12 labs and all grade bundles require completion of all 12 labs. They are designed to be completed in class. 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 Alonzi during office hours. We suggest completion of these assignments well in advance of the deadline.
- 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 all of this work to be completable within the class lab period.
- "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.
- Case Study Extensions - During the semesters case studies will be presented. This assignment type involves going beyond the initial parameter of the case study and exploring some of the more advanced points.
- "Look ahead" - These assignments are designed to allow students to advance their mastery of topics further. They incorporate material from courses typically taken by 2nd and 3rd year students.
- Final Essay - This assignment is the students' opportunity to synthesize the semester and show mastery of the primary learning objective "thinking like a data scientist". The higher grade bundles require a more complex essay which includes a discussion of personal career paths in data science and ultimately discussing next steps and the future of data science.
- Labs: There is a make up lab week at the end of the term where students can complete a lab they were unable to complete. No excuse is necessary.
- Resubmission: After grading assignments marked "does not meet spec, yet" can be revised and resubmitted for full credit. For some submissions that are far off the mark an office hour visit may be necessary before resubmission is granted. A second revision is granted rarely and only after an office hour conversation.
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 managed through canvas (including lab assignments).
- Personal Computer: We will be coding in this class, your laptop does not need any special hardware or software for our work.
| Week | Section | Dates | Lecture | Lab |
|---|---|---|---|---|
| 1 | Intro | T 1/14 R 1/16 F 1/17 |
Think Like a Data Scientist What is Data? NO LABS |
X |
| 2 | Design | T 1/21 R 1/23 F 1/24 |
The Data Science Pipeline Establishing Data LABS-1 |
LABS-1: LUPI |
| 3 | Design | T 1/28 R 1/30 F 1/31 |
Communicating with Data Guest Lecture LABS-2 |
LABS-2: SET |
| 4 | Design | T 2/4 R 2/6 F 2/7 |
Data Communication Google Maps Case Study LABS-3 |
LABS-3: Projection! |
| 5 | Value | T 2/11 R 2/13 F 2/14 |
Professor Wright Professor Wright LABS-4 |
LABS-4: Guess Who!? |
| 6 | Value | T 2/18 R 2/20 F 2/21 |
Guest Lecture Guest Lecture LABS-5 |
LABS-5: Value Case Study |
| 7 | Value | T 2/25 R 2/27 F 2/28 |
Case Study (Professor Wright) Guest Lecture LABS-6 |
LABS-6: GIGO |
| 8 | Systems | T 3/4 R 3/6 F 3/7 |
Hardware Software LABS-7 |
LABS-7: Hardware & Software |
| 9 | Spring Break | T X Spring R X Break F X No Class |
X | |
| 10 | Systems | T 3/18 R 3/20 F 3/21 |
Data Structures Algorithms LABS-8 |
LABS-8: Battleship |
| 11 | Systems | T 3/25 R 3/27 F 3/28 |
Guest Lecture GPUs & NVIDIA Case Study LABS-9 |
LABS-9: GPU |
| 12 | Analytics | T 4/1 R 4/3 F 4/4 |
LABS-10 |
LABS-10: |
| 13 | Analytics | T 4/8 R 4/10 F 4/11 |
LABS-11 |
LABS-11: |
| 14 | Analytics | T 4/15 R 4/17 F 4/18 |
Case Study LABS-12 |
LABS-12: |
| 15 | Outro | T 4/22 R 4/24 F 4/25 |
Career/Life/Essay Week Career/Life/Essay Week LABS Makeup Day |
Lab Makeup Day |
| 16 | Outro | T 4/29 R X F X |
Final Lecture & All assignments Due |
X |
- 1/24 - This is the first lab day and also the first READ assignment is due.
- 4/25 - Lab makeup day
- 4/29 - Final due date for all assignments (If a revision is necessary submission details will be provided with grading comments)
- Design - Carrie O'Brien: User Interface Designer at Capital One: Messaging, Web, and Human-Centered Design (Jan 30)
- Value - Professor MC Forelle -
- Systems - Professor Neal Magee
- Analytics -
- Job Week -
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)
Design
- The Design of Everyday Things by Norman
- How charts lie : getting smarter about visual information
- Observe, Collect, Draw! by Lupi and Posavec
Value
- << should >>
- Weapons of Math Destruction
Systems
- Fundamentals of Data Engineering by Reis and Housley
Analytics
- R for Data Science
- Python for Data Analysis
- Probability: Basic Probabilty by Tijms
- << ml >>
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/lab assignments that week. On Tuesdays READ assignments will be reviewed. On Thursdays 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.
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. The following pledge should be written out at the end of all quizzes, examinations, individual assignments, and papers: “I pledge that I have neither given nor received help on this examination (quiz, assignment, etc.)”. The pledge must be signed by the student. For more information, visit www.virginia.edu/honor.
Special Needs: The University of Virginia accommodates students with disabilities. Any SCPS student with a disability who needs accommodation (e.g., in arrangements for seating, extended time for examinations, or note-taking, etc.), should contact the Student Disability Access Center (SDAC) and provide them with appropriate medical or psychological documentation of his/her condition. Once accommodations are approved, just follow up with me concerning any logistics and implementation of accommodations. Please try to make accommodations for test-taking at least 14 business days in advance of the date of the test(s). Students with disabilities are encouraged to contact the SDAC: 434-243-5180/Voice, 434-465-6579/Video Phone, 434-243-5188/Fax. Further policies and statements are available at www.virginia.edu/studenthealth/sdac/sdac.html