MATH 637: Mathematical Techniques in Data Science

University of Delaware · Fall 2026

Lectures	MWF from 1:50pm–2:45pm in Purnell Hall 236
Office hours	MW from 2:45pm–3:45pm in Ewing Hall 509
Syllabus	Course syllabus (PDF)
Canvas	Canvas course site

Habit de Jardinier, an 18th-century engraving of a gardener dressed in garden tools — *Habit de Jardinier*, engraving by Nicolas de Larmessin (1640–1725)

This course will introduce fundamental techniques for data exploration, prediction, and inference. Our main objective will be to develop a mathematical understanding of data science techniques—their assumptions, properties, and limitations—alongside their practical implementation and use. Several of our meetings will be dedicated to problem-solving and programming sessions. Final projects will evaluate both an understanding of course material as well as the critical thinking skills at the core of effective data science.

Announcements

Aug 11 Welcome! The first lecture will be on Aug 26.

Lectures

#	Date	Topic	Materials
1	Aug 26	Course overview	Slides
2	Aug 28	Python basics in Colab	Code
3	Aug 31	Singular value decomposition

Resources

An Introduction to Statistical Learning, Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and (Python edition) Jonathan Taylor
Free PDFs of both the R edition (2nd ed., corrected 2023) and the Python edition.
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, and Jerome Friedman
Free PDF of the 2nd edition (12th corrected printing, 2017).
Mathematics for Machine Learning, Deisenroth, Faisal, and Ong
Free online textbook (2020).
Foundations of Data Science, Avrim Blum, John Hopcroft, and Ravindran Kannan
Free PDF of the pre-publication version of the Cambridge University Press book (2018).
Deep Learning: Foundations and Concepts, Christopher M. Bishop with Hugh Bishop
Free online version (2023), readable in the browser.
Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making, Bin Yu and Rebecca L. Barter
Open-access web version of the MIT Press book (2024).
Mathematical Methods in Data Science: Bridging Theory and Applications with Python, Sebastien Roch
Free online version of the Cambridge University Press book (2025).

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