Syllabus

Introduction

Welcome to DSCI220! In this course we will use concepts and structures from math as a model for the way we think about data. Among other things, we will explore sets, regular expressions, graphs, and strings. Most importantly, we will learn to reason about these structures and the data they represent.

Instructor information

Course description and learning objectives

By the end of the course, students will be able to:

  • Characterize data using a wide variety of structural, logical, and mathematical representations.

  • Understand the importance of elegant and efficient data representation.

  • Clearly and precisely communicate data representations to data scientists.

  • Clearly and precisely communicate logical arguments to data scientists.

  • Identify alternate mechanisms for solving or simplifying problems by translating between 1) English statements, 2) formal symbolic representation, and 3) code (where appropriate).

  • Write proofs for simple theorems by translating the theorem into first-order logic, decomposing the statements into symbolic representations, selecting an appropriate strategy (for example, direct proof, or proof by induction), and applying a sequence of logical inferences to make a conclusion.

  • Prove the functional correctness of simple algorithms.

  • Prove the running times of simple algorithms.

Lecture material and Course Content

The topic list for this course parallels a classic course in discrete math, but with examples and applications from data science. We use the programming language python to connect ideas from the course to programming paradigms – especially those patterns and algorithms relevant to data scientists.

The course consists of 9 units of study, each approximately 1.5 weeks:

  1. Logic
  2. Language and Proof
  3. Numbers and Functions
  4. Representation
  5. Sets
  6. Strings
  7. Counting
  8. Probability
  9. Graphs

There is no required textbook for the course. We realize, however, that you may want reference material for some of the topics. I am using “Discrete Mathematics and its Applications,” by Kenneth Rosen. Any edition will do, and older editions are not very expensive. The material we study is classic, and all topics will have robust explanations online, including on Wikipedia.

Course requirements

Deliverables for the course include the following components:

  1. Classroom Meetings – Class meets M/W/F, 11a-12p, in Frank Forward 317. All sessions are recorded and posted, but we make no guarantees about the quality of the recording, nor do we try very hard to replicate the in-class experience for online participants.

  2. Tutorial – The material presented in the 2hr weekly tutorial is more detailed and more applied than the classroom narrative. It is an opportunity for you to make sure you can DO the things we talk about in class. Tutorial sessions will be led by course TAs. There are 10 over the term, beginning in week 3. Tutorial exercises may be completed collaboratively and with online resources.

  3. Examlets – Five self-scheduled 50 minute assessments to be completed on PrairieLearn in the Computer Based Testing Facility (CBTF). Rules for the CBTF can be found here: https://cbtf.ubc.ca/students/. You will be given ample practice materials to use in preparation for the examlets.

  4. Final Exam – Like the examlets, the final exam will be self-scheduled within the CBTF. The exam window – approximately 5 days – will be posted later in the term.

Grading and exam policies

Component Weight
🎉 Class Meetings 0%
🪄 Tutorials 15%
🤩 Examlets 55%
🤩 Final Exam 30%

You must pass the final exam (50%) to pass the course.

Accessibility

If you have a disability or ongoing medical condition that may require accommodations, please contact the UBC Centre for Accessibility accessibility@ubc.ca to determine your eligibility and receive an accommodation letter.

If you have a specific concern about accessing course materials or if you would like to suggest improvements to the accessibility of this course, please do not hesitate to speak with me.

Academic misconduct

All instances of academic misconduct will be handled in a manner consistent with UBC policies. This includes reporting to the Director of Undergraduate Data Science Programs, and Faculty of Science Associate Dean of Students. No credit will be given for any assignment which was completed with inappropriate collaboration.

Land Acknowledgement

UBC’s Point Grey Campus is located on the traditional, ancestral, and unceded territory of the xʷməθkʷəy̓əm (Musqueam) people. The land it is situated on has always been a place of learning for the Musqueam people, who for millennia have passed on their culture, history, and traditions from one generation to the next on this site.