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Syllabus 📖

Table of contents

  1. Course Description 🍎
  2. Logistics 👨‍🏫
    1. Class Meetings
    2. Readings
    3. Homeworks
  3. Technology 💻
  4. Grading 🧪
  5. Acknowledgements 🙏🏼

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. 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 are taking 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 we will not use the full timeslot for lecture. Instead, we will discuss the material from 6-7:30PM, and use the remaining time for office hours, where students can ask questions about the reading or homework.

For at least the first two weeks of the quarter, this course will meet remotely via Zoom (the link can be found on the course homepage). Once campus resumes in-person instruction, class will be held in-person in Center Hall 218 📍. Once the class moves in-person, remote attendance will remain an option.

Lectures will always be recorded and posted afterwards.


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).


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 can be found at the bottom of the corresponding lecture page, and will typically be due on Sundays at 11:59PM to Gradescope.

Technology 💻

In addition to the course website, there are two other sites you’ll need to access regularly; email the instructor if you haven’t been added to them.

  • Campuswire: for discussion of lecture content and homework problems.
  • Gradescope: for submitting homeworks.

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:

  1. Attend and participate in at least 7/8 class sessions. (Let the instructor know in advance if you can’t make a particular class.)
  2. Complete at least 7/8 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.