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COGS 9

📖 Syllabus

Table of Contents

Course Materials

  • There is no textbook
  • All course materials are provided through this website
  • Reading quizzes 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

  % of Total Grade 200 Total Points
3 Assignments 30 60 (20 each)
5 Reading Quizzes (lowest quiz score dropped) 20 40 (10 each)
Final Project pt. 1 10 20
Final Project pt. 2 20 40
Final Project video 20 40
  • 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.

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

Lectures are pre-recorded and posted on the course website (and via UCSD podcast), so you can watch on your own schedule. Office hours and the Project Studio are live on Zoom. We’d love to see you there to work through questions together.

Project Studio (Discussion Sections)

Discussion sections run as a live Project Studio on Zoom. This is dedicated working time for your group project. You’ll form teams in Week 1, and studios run Weeks 2 through 5 with five sessions each week. Drop into whichever fits your schedule. See the home page for the schedule and Zoom links. Your group must attend at least one studio per week. Check in with your team name when you join, and staff will mark your group present. For each week your group does not attend, you lose up to 2% off your final grade.

Late Policy

  • Reading quizzes: must be submitted on time; no late submissions accepted.
  • Assignments: a 12-hour grace period after the deadline (no penalty). After that, late work is not accepted.
  • Final project (all parts): must be submitted on time. Late work is not accepted except for documented emergencies outside your control. Email the course staff before the deadline.

Joining the Class Late

Summer sessions move fast. If you add the course late and want full credit for early work, you must: (1) email Kyle and your TA that you joined late, before the end of Week 2, so we don’t count that work as late; (2) read this syllabus and catch up on the recorded lectures; and (3) submit any missed Week 1 assignments/quizzes by 11:59pm Friday of Week 2. Work from later weeks is not eligible for this catch-up.

See the Joining Late page for a step-by-step checklist to get set up fast.

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. Each assignment is worth 20 points (60 points total, 30% of your grade). See the assignments tab for individual instructions and the home page course calendar 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 exam in COGS 9. In its place, there is a substantial final group project due at the end of the session (Week 5).

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
  • Released all at once at the start of the term; complete at your own pace by each due date
  • 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 are not accepted.

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

Policies and Resources

Check out these pages for more: