Syllabus 📜
Table of contents
Course Description 🍎
While the terms “data analysis” and “data science” were only coined in the late 1900s, the foundations of data science were laid centuries, even millenia, ago. In this course, students will study the origins of some of the key ideas in the field, including calculus, probability, linear regression, and visualization. Students will learn more about early contributors – from Newton to Galton to Playfair to Lovelace – and their motivations. In doing so, students develop an appreciation for methods that are now ubiquitous in the field of data science.
We will only assume that students have taken DSC 10, and are familiar with basic derivatives and integrals (though this is not a firm requirement). Aside from that, we will make no assumptions regarding your background, and will introduce technical concepts at an introductory level when necessary.
Logistics 👨🏫
Class Meetings
This course will meet once a week on Mondays. The official schedule states that this course is from 6-8:50PM on Mondays, however lecture will only be from 6-7:30PM. The instructor will be available for questions after lecture as well as later in the week for office hours; see below for more details.
Class meetings will be in-person in Warren Lecture Hall 2209 📍. If you are unable to attend in-person, you may attend via Zoom using the link posted on the course website. We encourage you to attend in-person whenever possible as there will be many opportunities for discussion during class.
Lectures will always be recorded and posted afterwards.
Readings
There is no “textbook” for this course. We will link readings and videos from a variety of sources in the webpage for a given lecture. Students are expected to complete all of the readings provided in a given week; much of the insight in this class will come from reading (and subsequently, discussing).
Homeworks
Weekly homeworks in this course are designed to facilitate engagement with the material. They will consist of a mix of reading reflection problems and technical problems, and will align closely with the readings. Homeworks will be linked at the bottom of the corresponding lecture page, and will typically be due on Sundays at 11:59PM to Gradescope. No late submissions are allowed.
Office Hours
The instructor will also hold office hours. The exact time for these office hours can be found on the course homepage.
Office hours will also be held in a hybrid format, meaning that you can attend them either in-person or via Zoom. In-person office hours are held on the second floor of the east wing of the San Diego Supercomputer Center 📍 (map). Enter SDSC from the main entrance on Hopkins Drive, take the elevator up to the second floor, turn left at the kitchen, and meet in the common area. You will need a code to enter the building; the code is pinned in the #general channel of our course Slack. Make sure to hit the # key after typing in the code.
Technology 💻
In addition to the course website, there are two other sites you’ll need to access regularly. Invite links/codes can be found below.
- Slack: for discussion of lecture content and homework problems.
- By using Slack over Campuswire or Piazza, we hope to foster a more close-knit environment between the instructor and the students in the course.
- You should download the Slack app on your computer and on your phone and turn on notifications, as that’s where all announcements for the course will be made.
- Gradescope: for submitting homeworks. Use the entry code 3YEENX if you weren’t already added.
We will not be using Canvas at all.
Grading 🧪
This course is offered for 2 units, and is graded P/NP. In order to earn a P, you must:
- Attend and participate in at least 8/9 class sessions. (Let the instructor know in advance if you can’t make a particular class.)
- Complete at least 8/9 homework assignments satisfactorily.
In the event you aren’t able to meet these requirements, there may be a “make-up” assignment, but you should not rely on it.
Acknowledgements 🙏🏼
Many readings and topics are borrowed from Rohan Alexander’s History of Statistics and Data Sciences course at the University of Toronto.