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COGS9 Introduction to Data Science Fall 2021 

Hybrid lecture: Catalyst 0125 + Zoom + Podcast




CTL 0125

Zoom ID: 982 6976 7118


Discussion sections START Friday Oct 1



CSB 004 [Zoom]
Zoom ID: 99577270837




CSB 004 [hybrid]

Zoom ID: 946 3643 6425




CSB 004 Zoom only!

Zoom ID: 975 6398 5690




CSB 004 Zoom only!

Zoom ID: 939 6336 6305




PETER 103 [hybrid]

Zoom ID: 925 7155 5412



Jason Fleischer - Course Instructor

Pronounce: JAY-sun FLY-shure

Pronouns: he/him

Office hours: sign up here  M-F 11:30-12:40 and by appointment

Corey Zhou - TA

Pronouns: they/them

Office hours: Mon 3-5pm

Matthew Feigelis - TA

Pronouns: he/him

OH: Tues 10-11am,

OH: Tues 2:30-3:30pm

Payal Bhandari  - TA
Pronouns: she/her
Office Hours: Wed 11am - 1 pm

Luning Yang - IA

Pronouns: he/him

Office hours: Thursday: 8pm - 9pm

Yunyi Huang - IA

Pronouns: He/Him

Office hours: Weds 11am-12pm

Zhigang Lin - IA

Pronouns: he/him

Office hours: Fri 5pm-6pm

Important links:

Canvas webpage: 

Campuswire* discussion board: use code 5202 to join

Anonymous course feedback:

*You will be able to post anonymously on Campuswire; however, you will only be anonymous to your classmates. Your Instructor and TAs will be able to see who you are.

Course objectives

Course materials

There is no textbook. All materials will be provided through Canvas.


Lectures are MWF  2 - 2:50pm. The Catalyst 0125 auditorium is located on the northern end of the North Torrey Pines Living + Learning Neighborhood. It's part of the new Sixth College.

Lectures will be offered as a hybrid class: they will be in-person, available synchronously online via zoom, and recorded for your later viewing. At some point in the quarter we may switch to Friday being online only; I will make multiple clear announcements if/when that happens.

THERE IS NO REQUIREMENT THAT YOU COME TO IN-PERSON LECTURE. There is no component of your grade that is based on attendance or live participation. DO NOT COME TO IN-PERSON CLASS IF YOU FEEL SNIFFLY OR SICK!!! I really don't want our very large lecture hall to become a super-spreader event for any illnesses.

For in-person lectures a negative symptom check from that day and a properly worn mask of the correct type are required to attend! For more information please see Return to Learn requirements for on-campus activities.

Those concerns aside, don't let me scare you away from coming to lecture in-person: it is the most engaging way you can absorb the material and it gives you the best opportunity to be an active participant. Students who actively engage in the learning process do better than those who passively absorb material given to them. I hope that my strict illness policies encourage students who wish to attend in-person to do so.

In-person lectures will be broadcast simultaneously on this Zoom link Click here to join Zoom Meeting: 982 6976 7118 the password is cogs9.

Every lecture will also be recorded and shared. Lecture recordings will be available on and on Canvas in the Media Gallery no later than 5PM the day the live lecture is delivered.

Lecture quizzes / reflections

Either a quiz with ~10 easy multiple choice questions OR a prompt to write a short reflection will be released each Friday starting Oct 1, covering the material from lectures that week. This quiz will be due the following Friday at 11:59 PM (i.e., Oct 1 quiz is due Oct 7). For a quiz you will have a single, timed (15 min) attempt to complete but you can start it at your leisure in that week it’s available. For a reflection piece, there is no time limit but try not to spend more than 15 min on it. There are no late extensions for these, but your lowest score will be dropped.


Three assignments will focus on applying the concepts covered in lecture and ensuring you’re on the right track for your final project.

Assignments will be released on Canvas and submitted on Gradescope. Assignments will always be due Fridays at 11:59 PM. You will receive feedback along with a grade a week from submission.

Final Project

The final project is a report on how you would handle a complicated data science project. This report will include all the nitty gritty, whys, and hows of the analysis you have chosen. You’ll specify your data science question, find data that could be used to answer the question, and describe the analysis you would carry out to answer your question of interest. You WON’T have to actually perform the analysis to answer the question; you’ll just write about it. But, if you do carry out the analysis and can present results, that’s great and it will earn you extra credit.

You are able to choose your final project groups of 4-5 people. If you do not have a group, the instructor will assign one. There will be time to work on and discuss your projects in section, so we recommend (but do not require) you form groups within the section you plan to attend. You can also coordinate groups virtually. Asynchronous group communication is a skill that you will definitely need in your professional life! There are many tools that let you coordinate a team across time zones and schedules (e.g., Slack, Discord, etc)

A project proposal will be due in Week 5. The proposal must clearly define your data science question and at least a rough sketch of how you propose to answer it. We will give you feedback from your proposal which you will incorporate into your final project. The projects are due at midnight on Dec 8th. For both proposal and final project a single group member will submit on Gradescope, but they must be sure to tag every group member during the submission process.


There will be two exams covering material in lecture (including guest lectures!) and the readings discussed in section. They will be administered on Canvas during class time.  Exams are timed and are closed notes (also no talking with others, using other windows on a web browser, etc). Exams will be primarily multiple choice with a few short answer questions. See schedule below for exam dates.

If you know you cannot attend an exam in advance contact me ASAP about your situation. If you miss an exam without telling me in advance I will generally not allow a late exam; certain exceptions may be made on a case by case basis but they will be rare.

Discussion Sections & Readings

The first discussion sections run in Week 1 on Friday Oct 1.

Discussion section covers a set of 5 readings, questions on assignments & lecture, and the final project. Usually we will spend about half the time discussing the reading/assignment and answering any questions you have about the lecture. The other half of the time is usually working on projects in your groups. Readings will be posted one week before their accompanying quiz is due, and they will be discussed in section the week they are due.

Each reading quiz will become visible on Canvas as 12:01AM on Thursday and you will have until 11:59 PM on Friday to complete the quiz. You are not timed. You must click submit to submit your reading quizzes. Your most recent submission will be graded. If you fail to do so before the deadline, it will not be graded. No late credit will be given if reading quiz assignments are submitted after deadline.

●      R1: Donoho D, 50 Years of Data Science

●      R2: Keyes O, Hutson J, & Durbin M, A Mulching Proposal

●      R3: Wickham H, Tidy Data

●      R3: Woo K & Broman K, Data in Spreadsheets

●      R4: Peck, E, Ayuso S, & El-Etr O, Data Is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania

●      R5: Angwin J, Larson J, Mattu S & Kirchner L, Machine Bias



How many to submit


% of Total Grade



20 pts each; 60 total




50 pts each; 100 total


Reading quizzes


16 pts each; 80 total


Project proposal

1 per 4-5 person group

30 pts


Final project

1 per 4-5 person group

70 pts


Guest lectures


10 pts each; 20 total


Lecture quizzes / reflections

9 (lowest score dropped)

40 pts




      Final exam date: No final exam, only final project deadline.

      There are 400 possible points to be earned in this course. To determine your final grade, you will add up all of the points for the above categories and divide your grade by 4. Grades are not rounded up.

Our grading scale is


≥ 97%


< 90 % to 87%


< 80 % to 77%


< 70 % to 60%


< 97% to 94%


< 87 % to 84%


< 77 % to 74%


< 60 %


< 94% to 90%


< 84 % to 80%


< 74 % to 70%


I continue to believe that attending in-person is the best way to learn for most but not all people. Our goal is to make the lecture and discussion section worth your while to attend. But no one will be forced to, there is a pandemic and many extenuating circumstances. You're adults, you will know what fits your constraints and needs.

When will grades be released

Grades for assignments, quizzes, and exams will be released on Canvas approximately a week after the submission date. It is your responsibility to ensure your assignments are submitted on time and to check your grades and get in touch if any are missing or you think there is a problem.


We will work hard to grade everyone fairly and return assignments quickly. But, we know you also work hard and want you to receive the grade you’ve earned. Occasionally, grading mistakes do happen, and it's important to us to correct them.

If you think there is a mistake in your grade for an assignment, submit a regrade request on Gradescope within 72 hours of receipt of the grade. This request should include evidence of why you think your answer was correct (i.e. a specific reference to something said in lecture) and should point to the specific part of the assignment for us to reconsider.


 Class Conduct

In all interactions in this class, you are expected to be respectful. This includes following the UC San Diego principles of 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 anyone on the instruction team (professor, TAs, or IAs). 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. 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 the academic integrity policies 

Know that a third of the class typically feels overwhelmed at the start of the quarter. That said, the average is quite high in this course typically (A-). So, while we anticipate you all doing well in this course, if you are feeling lost or overwhelmed, that’s ok! Should that occur, we recommend: (1) asking questions in class, (2) attending office hours and/or (3) asking for help on Campuswire.

Cheating and plagiarism have been and will be strongly penalized. If, for whatever reason, datahub is down or something else prohibits you from being able to turn in an assignment on time, immediately contact me by emailing your assignment by email, 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). Disabilities can occur in many areas, such as: psychological, psychiatric, learning, attention, chronic health, physical, vision, hearing, and acquired brain injuries. Please contact the instructor privately to arrange accommodations once you have an AFA. If you are struggling to get a meeting with OSD, you can let the instructor know and they will likely be able to help accommodate you while you work to get official documentation.

OSD is located in University Center 202 behind Center Hall


Difficult life situations

Sometimes life outside of academia can be difficult. Please email me  or come to office hours if stuff outside the classroom prevents you from doing well inside it. I can often refer you on to the help you need.

If you don't have the most essential resources required to thrive as a student, please contact UCSD Basic Needs who can help you access nutritious food and stable housing, and help you seek the means to reach financial wellness.

If you need emergency food, finances, and/or academic and social support you can also contact UCSD Mutual Aid They provide mentoring and aid that comes from volunteers among your peers.  If you don't need that kind of support, consider joining them in helping your fellow classmates who do.

If you need counseling or if you are in a mental crisis you can contact CAPS  They provide psychiatric services, workshops, and counseling; they also operate a 24/7 crisis hotline at 858.534.3755. The pandemic has taken a heavy toll on all of us, there is no shame in seeking help.


How to Get Your Question(s) Answered and/or Provide Feedback

It’s great that we have so 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 post whenever is best for you while knowing that if you post late at night or on a weekend, you may not get a response until the next weekday. As such, do your best not to wait until the last minute to ask a question.


If you have…

-       questions about course content - these are awesome! We want everyone to see them and have their questions answered too….so post these to CampusWire!

-       questions about course logistics - first, check the syllabus. If you can’t find the answer, ask a classmate. If still unsure, post on CampusWire.

-       questions about a grade - If for an assignment, submit a regrade request on Gradescope. For anything else, post as a question on CampusWire, address it to “Instructors,” and select the folder “regrades”

-       something super cool to share related to class - feel free to use CampusWire,  email the instructor (, 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 during office hours or schedule a time to meet by email. Be sure to include COGS9 in the email subject line.

-       some feedback about the course you want to share anonymously - If you been offended by an example in class, really liked or really disliked a lesson, or wish there were something covered in class that wasn’t but would rather not share this publicly, etc., please fill out the anonymous form on this webpage









(Friday 11:59 PM)

Covered in Section on Friday




(9/24) What is data science?




(9/27) Ethics & Privacy

(9/29) Data Science Questions

(10/1) Reproducibility

R1: Data Science + Course survey (extra credit)

Finding teammates for the project


(10/4) Programming

(10/6) File Management

(10/8) Data & Data Types

R2: Data Ethics

DS questions

+ ethics


(10/11) Getting Data

(10/13) Data Wrangling

(10/15) Data Visualization I

R3: Tidy Data

project proposals + data handling


(10/18)  Data Visualization II

(10/20) Descriptive Analysis

(10/22) Exam I

A1: Data Visualization

exam review


(10/25) Exploratory Data Analysis

(10/27) Prediction + modeling

(10/29) Inference

Project Proposal*

project proposals + EDA


(11/1) A|B Testing


Guest lecture

or catch up day


(11/5) Machine Learning I

A2: p-values

project + prediction, modeling, inference


(11/8) Machine Learning II

(11/10)  Algorithms & Computability

(11/12)  Text Analysis I

R4: Data Viz

project + machine learning


(11/15) Text Analysis II

(11/17) Geospatial Analysis


How to be wrong


A3: Machine Learning

project + text / geospatial



How to be wrong



Guest lecture

or catch up day





NONE Thanksgiving



(11/29) Guest lecture

or catch up day


(12/1) Future of Data Science

(12/3) Exam II

R5: Algorithms + Post course survey (extra credit)

Exam review

Final Project*: due Wednesday Dec 8th by 11:59 PM. THERE IS NO FINAL EXAM.


*indicates group submission. All other assignments are completed & submitted individually.