BIS5XX: Introduction to Health Data Science

Overview

About This Course

Description

A comprehensive introduction to data science with a focus on public health and healthcare applications. Covers data structures and algorithms, machine learning, and causal inference — equipping students to decompose complex problems, select appropriate methods, and communicate results to diverse audiences.

Who Should Enroll

MS / PhD in Biostatistics Required

MPH students (quantitative focus)

Advanced undergrads (with permission)

Stats & DS or CS grad students

Harsh Parikh, Ph.D.

Assistant Professor, Biostatistics

harsh.parikh@yale.edu

Office Hours

TBD — 1 hour / week

60 College St, Rm 200

Teaching Fellows

3–4 TFs (TBD)

1 OH each / week

Course Website

harsh-parikh.github.io/intro_ds_2026

Canvas

canvas.yale.edu

Prerequisites

Foundational knowledge in the following areas, or concurrent enrollment in courses covering them.

Linear Algebra

Vectors, matrices, basic operations

Set Theory

Notation, union, intersection, complement

Probability

Random variables, distributions, Bayes' rule

Programming

Any language; Python helpful but not required

Learning

Learning Objectives

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

Formulate

precise mathematical or statistical questions from natural language problems, identifying meaningful estimands

Decompose

complex problems into tractable sub-problems and recognize appropriate tools for each component

Implement

data science methods using Python (NumPy, Pandas, Scikit-learn) to analyze real-world datasets

Interpret

results within the appropriate problem context, understanding capabilities and limitations

Communicate

findings effectively to academic and non-technical audiences through writing and visualization

Evaluate

the appropriateness of analytical approaches, making informed trade-offs between methods

12 Weeks

Course Schedule

M1: Data Structures & Algorithms

M2: Machine Learning & AI

M3: Causal Inference

Course Introduction

Overview of data science; problem formulation; translating questions to estimands

Syllabus; Turing (1950)

Self-assessment quiz

1–2

Lists, Arrays & Sorting

Fundamental data structures; sorting algorithms; computational complexity

CLRS Ch. 2, 7, 8

HW 1 released (Wk 2)

Trees & Graph Traversal

Tree structures; BFS; DFS; applications in healthcare networks

CLRS Ch. 10, 22

Team formation due

Dynamic Programming

Optimization principles; memoization; tabulation; sequence alignment

CLRS Ch. 15

HW 1 due · Proposal due

Statistical Learning

Maximum likelihood estimation; uncertainty quantification

ISLR Ch. 1–2; IAML Ch. 1.1

HW 2 released

Linear Methods

Linear & logistic regression; regularization (Ridge, LASSO); evaluation metrics

ISLR Ch. 3, 4, 6; IAML Ch. 4

—

134

Nonparametric Methods

Decision trees; random forests; boosting & bagging

ISLR Ch. 8; IAML Ch. 2–3

HW 2 due

346

Neural Networks

Perceptrons; multi-layer networks; backpropagation; deep learning intro

ISLR Ch. 10; IAML Ch. 12.1

Progress report due · HW 3 released

Clustering

K-means; EM algorithm; hierarchical clustering

ISLR Ch. 12.4; IAML Ch. 10

—

346

Potential Outcomes & DAGs

Rubin Causal Model; causal graphs; confounding; identification strategies

Mixtape Ch. 3–4; FCCI Ch. 1–2

HW 3 due · HW 4 released

124

Matching & Weighting

Matching and weighting estimators; difference-in-differences

Mixtape Ch. 5; FCCI Ch. 10–11

—

346

Project Presentations

Student poster session; data equity discussion

Data Equity in Data Science

HW 4 due · Final project (Finals Wk)

Evaluation

Assessments & Grading

Grade Components

36%

Homework Assignments (4)

Conceptual, programming, and applied problems every ~3 weeks

30%

Course Project

Team-based (3–4) applied project with proposal, report, and poster

18%

Scribe Notes (2)

Detailed LaTeX lecture notes with extensions beyond class content

11%

In-Class Quizzes

Short 5–10 min quizzes on readings and lecture material

Participation

Class engagement, discussions, and forum contributions

Grading Scale (YSPH)

≥ 90%Honors (H)

80–89%High Pass (HP)

65–79%Pass (P)

< 65%Fail (F)

Late Policy

Late submissions are not accepted. Contact the instructor before the deadline for documented emergencies.

Grading Turnaround

Homework & Scribe Notes1 week

ParticipationMidterm & End of Semester

Participation grades are released twice — at midterm and at the end of the semester — so you can align your expectations with ours early on.

Key Dates

Homework 1Week 2 → Week 4

Homework 2Week 5 → Week 7

Homework 3Week 8 → Week 10

Homework 4Week 10 → Week 12

Project ProposalWeek 2 → Week 4

Progress ReportWeek 6 → Week 8

Final ReportWeek 10 → Finals Week

Poster SessionWeek 12 (in class)

Readings & Software

Materials

No single required textbook. All texts are freely available online.

CLRS

Introduction to Algorithms

Cormen, Leiserson, Rivest, Stein

IAML

Intuition for the Algorithms of ML

Cynthia Rudin

ISLR

An Introduction to Statistical Learning

James, Witten, Hastie, Tibshirani

FCCI

A First Course in Causal Inference

Peng Ding

Mix

Causal Inference: The Mixtape

Scott Cunningham

PyDS

Python Data Science Handbook

Jake VanderPlas

Software Stack

Python 3.x NumPy Pandas PyTorch Scikit-learn Matplotlib Seaborn Jupyter Overleaf (LaTeX)

Expectations

Course Policies

AI Policy

AI assistants (ChatGPT, Claude, Copilot) are permitted as learning aids for homework and scribe notes:

You must understand everything you submit

Disclose all AI use at the top of submissions

No acknowledgment → submission not accepted

You own all accuracy — AI hallucinations are on you

Quizzes: no AI allowed

Collaboration

Discussion with classmates is encouraged, but:

All written solutions must be your own

All code must be written individually

Acknowledge all collaborators and resources

Do not share answers, code, or LaTeX files

Electronic Devices

Mobile phones, iPads, and laptops are not permitted during lectures. Handwritten notes promote deeper processing. Exceptions for documented accommodations. No devices during quizzes under any circumstances.

Academic Integrity

All students must adhere to the YSPH Code of Academic and Professional Integrity (CAPI). Violations — plagiarism, unauthorized collaboration, undisclosed AI use, fabrication — will be referred to the CAPI Committee. Penalties can include expulsion.

Support

Resources

Accessibility

Yale Student Accessibility Services (SAS). Email sas@yale.edu or call 203-432-2324.

Mental Health

YSPH Wellness Counselor: Diane Frankel-Gramelis. 988 Lifeline: dial 988 (24/7).

Writing Support

Graduate Writing Lab — free consultations. Book at yale.mywconline.net.

Inclusivity

Office of Community & Practice. Contact Mayur Desai or Randi McCray.

Title IX

Deputy coordinator: Kelly Shay. OIEA.

Classroom Safety

emergency.yale.edu · Preparedness