📖 Syllabus

Table of Contents

1. Course Materials
2. Course Objectives
3. Grading & Attendance
   1. Grading
   2. Grades
   3. Regrade Policy
   4. Lecture Attendance
   5. Section Attendance
4. Course Assignments & Topics
   1. Assignments
   2. Final Project
   3. Exam
   4. Readings & Quizzes
   5. Planned Readings
5. Other Good Stuff
   1. Teamwork Expectations
   2. Class/Web Conduct
   3. Academic Integrity
   4. Disability​ ​Access
   5. Questions & Feedback

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Course Materials

• There is no textbook
• All course materials are provided through this website
• Reading quizzes and the exam are taken through Gradescope
• Assignments and the final are submitted through Gradescope

Course Objectives

• Comprehend core data science concepts and examine their applications
• Discuss data privacy and ethical concerns with real-world examples
• Identify data science questions and the appropriate analytic approach to answering those questions
• Communicate data-related topics and projects
• Demonstrate how to think critically about data, and how to approach problems with a “data-first” mindset
• Describe potential pitfalls of data analyses, how to identify them, and how to avoid them

Grading & Attendance

Grading

• Final exam date: No final exam, only a final group project.
• Your letter grade will be determined using the standard grading scale. Grades are not rounded up, that’s why we have included 5 bonus points.

Grades

Grades are released on Gradescope often a week after the submission date, typically sooner. Ultimately it is your responsibility to check your final grade and get in touch if you believe there is a problem.

Regrade Policy

The regrade policy is here to protect students from serious issues in grading, not to provide students with a platform to argue about, or plead for an extra point. A grader may incorrectly take off 1-2 points, but they are as likely to give students 1-2 points. In our experience less than 3% of the time a regrade results in a change. When we regrade, we closely go through the entire assignment again and reevaluate it as a whole. This means your grade can either stay the same, go up, or go down. This is not to discourage students from requesting legitimate regrades, but to discourage students from arguing about 1 point (which is worth 0.05% of your grade). These discussions require a serious investment of time. We want to spend that time on regrades where a serious issue has occurred, or with helping students learn the material outside of class.

If you think a grading error has occurred please follow these steps:

• You have 72 hours to request a regrade
• Initiate the regrade through Gradescope (if it is a group project, confer w/ your team first and submit one regrade after your team comes to a consensus)
• Provide evidence for why your answer is correct and merits a regrade (i.e. a specific reference to something said in a lecture, the readings, or office hours)
• We will get back to you within 48 hours with our final decision.

Lecture Attendance

Our goal is to make lectures and office hours worth your while to attend, e.g. we do in-class exercises. However, lecture attendance is not required. All lectures will be recorded. These will be made available to you (UCSD podcast).

Section Attendance

Section attendance is not mandatory, however, groups will be created within sections (usually week 3). Readings as well as lecture material will be reviewed in the section and it is to your benefit to go and ask questions.

Course Assignments & Topics

This class is a survey course intended to get you all excited about becoming data scientists! Data are everywhere and they’re being used in tried-and-true, new, and creative ways. This course will introduce you to the broad topics in data science, discuss what it means to be a data scientist, and get you on your way to thinking like a data scientist. To see what topics will be introduced in this course, see the side nav menu and click on topics.

Assignments

Assignments will focus on applying the concepts covered in lectures and readings. See the assignments tab for individual instructions and the home page course calander for due dates.

Final Project

The final project is a two-part report and video on how you would handle a complicated data science project. It’s a culmination of what you learned from the assignments and lectures.

Your final will include your data science question as well as all the nitty gritty, whys, and hows of the data science project you have chosen. You’ll write about your data science question, find some example data, summarize the data, explain how you would wrangle the data to answer your data science question, and describe the types of analysis you would carry out to answer your question of interest. You WILL NOT have to actually perform the analysis to answer the question, nor wrangle data, you only write about how you would perform the analysis and what you expect the outcomes will be.

See the final project tab for further instructions.

Exam

There is no comprehensive exam in COGS9, there is however a substantial group project due around week 10.

Readings & Quizzes

Quizzes cover the reading material assigned, e.g. Quiz 1 only covers material from reading 1 (R1).

• Five multiple choice (10 questions) quizzes
• Available for 48 hours
• One attempt
• Open notes, but you must work alone.
• Taken and submitted through Gradescope

Your lowest quiz score will be dropped when calculating your final grade. Late reading quizzes will be accepted up to 48 hours, however, they will receive ½ credit.

Planned Readings

Readings will cover many of the broad topics found within Data Science, both from an academic and industry perspective.

• R1: Donoho D, 50 Years of Data Science
• R2: Loukides M, Mason, H & Patil DJ, Ethics and Data Science
• R2: Privacy & Security Myths & Fallacies of “PII”, Narayanan and Shmatikov
• R3: Wickham H, Tidy Data (Sections 1 -3)
• R3: Woo K & Broman K, Data in Spreadsheets
• R4: Wickham H, Cook Di, Hoffman H, & Buja A, Graphical Inference for Infovis
• R4: Peck, E, Ayuso S, & El-Etr O, Data Is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania
• R5: Diakopoulos N, Accountability in Algorithmic Decision Making
• R5: Angwin J, Larson J, Mattu S & Kirchner L, Machine Bias

Other Good Stuff

Teamwork Expectations

Your team will be working on the final together. We expect all students to, more or less, be equal contributors to the final project. No one person should be doing a project, they are meant to be collaborative and give you experience working with people you probably do not know. One successful approach is to first agree on a communication tool/protocol and a schedule. Next, discuss each person’s strengths and divide up responsibilities. Develop a schedule for completing tasks, who is responsible, and a backup person in case an emergency occurs. Finally, check in regularly to ensure progress is being made and leave some time to check and proofread each other’s work. Especially because some students in your group may be remote for part of the class.

Dealing with non-cooperative team members – If an issue occurs first try to work the issue out within your group. Save all documentation, emails, and chats as a record in case you need to contact the course staff. We will step in and try to communicate with the student(s) to resolve this. If no resolution can be made, or the problem resurfaces, we reserve the right to move the student to a new group or grade that student separately from the group, or any other action to resolve the issue.

Group work is never easy – Teamwork, while difficult (especially during potentially remote interaction), is one of the most important skills you should learn and practice during college. To succeed, communication is critical. You need to be in contact with your group regularly. This will help you keep on top of deliverables and make adjustments if problems should arise. We are always here to help and make use of our experience working on real engineering/science projects.

Class/Web Conduct

In all interactions in this class, you are expected to be respectful. This includes following the UC San Diego principles of the community.

This class will be a welcoming, inclusive, and harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity, religion (or lack thereof), political beliefs/leanings, or technology choices.

At all times, you should be considerate and respectful. Always refrain from demeaning, discriminatory, or harassing behavior and speech. Last of all, take care of each other.

If you have a concern, please speak with Kyle or your TAs. If you are uncomfortable doing so, that’s ok! The OPHD (Office for the Prevention of Sexual Harassment and Discrimination) and CARE (confidential advocacy and education office for sexual violence and gender-based violence) are wonderful resources on campus.

Academic Integrity

Don’t cheat.

You are encouraged to (and at times will have to) work together and help one another. However, you are personally responsible for the work you submit (quizzes/exams). For assignments, it is also your responsibility to ensure you understand everything your group has submitted and to make sure the correct file has been uploaded, that the upload is uncorrupted and that it renders correctly. Projects may include ideas and code from other sources—but these other sources must be documented with clear attribution. Please review academic integrity policies here.

We anticipate you all doing well in this course; however, if you are feeling lost or overwhelmed, that’s ok! Should that occur, we recommend: (i.) asking questions in/after class, (ii.) attending office hours, and/or (iii.) reaching out to course staff.

Cheating and plagiarism have been and will be strongly penalized. If, for whatever reason, this website, or Gradescope is down, or something else prohibits you from being able to turn in an assignment on time, immediately contact course staff via email (attach your assignment/quiz/exam answers) or else it will be graded as late.

Disability​ ​Access

Students requesting accommodations due to a disability must provide a current Authorization for Accommodation (AFA) letter. These letters are issued by the Office for Students with Disabilities (OSD), which is located in University Center 202 behind Center Hall. To arrange accommodations please contact Kyle kshannon@ucsd.edu privately, I will loop in the appropriate course staff to act as a liaison.

Contacting the OSD can help you further: 858.534.4382 (phone) osd@ucsd.edu (email) http://disabilities.ucsd.edu

Questions & Feedback

How to Get Your Question(s) Answered and/or Provide Feedback It’s great that we have many ways to communicate, but it can get tricky to figure out who to contact or where your question belongs, or when to expect a response. These guidelines are to help you get your question answered as quickly as possible and to ensure that we’re able to get to everyone’s questions.

That said, to ensure that we’re respecting their time, TAs and IAs have been instructed they’re only obligated to answer questions between normal working hours (M-F 9am-5pm). However, I know that’s not when you may be doing your work. So, please feel free to reach out whenever is best for you while knowing that, you may not get a response until the next day. As such, do your best not to wait until the last minute to ask a question. If there is an emergency and you need to contact staff immediately, email Kyle and put “EMERGENCY-COGS9” in the subject line. I will get back to you ASAP.

If you have…

• questions about course content - these are awesome! We want everyone to see them and have their questions answered, please post questions, or ask during class/office hours.
• questions about course logistics - first, check the syllabus. If the answer is not there, ask in Section, ask a classmate, or ask during class/office hours.
• something super cool to share related to class - feel free to email Kyle kshannon@ucsd.edu) or come to office hours. Be sure to include COGS9 in the email subject line and your full name in your message.
• something you want to talk about in-depth - meet in person/digitally during office hours or schedule a time to meet 1:1 by email. Be sure to include COGS9 in the email subject line. Or it may be missed. kshannon@ucsd.edu.