Spring 2027

Introduction to Data Analyses in Drug Development

Introduction to Data Analyses in Drug Development covers data analysis techniques in drug discovery, focusing on project-based skills with industry-standard software and public databases.

offering recorded3 credits
Recorded instructors · Spring 2027 Sin Yin LimSpencer Ericksen

Summary

1 / 2

Recent recorded grades — Spring 2024: 3.86 GPA, 85.7% A/AB (n=7 letter grades); Spring 2025: 3.78 GPA, 88.9% A/AB (n=9 letter grades); Spring 2026: 3.89 GPA, 88.9% A/AB (n=9 letter grades).

What you’ll be able to do

  • Identify relevant software platforms, programming tools, and databases relevant to various drug development tasks.

  • Apply best practices, critical thinking skills, and troubleshooting techniques to data analysis projects.

  • Complete data analysis projects representative of those used in drug target characterization.

  • Analyze large high throughput screening (HTS) datasets to confirm positive hits and identify common issues that arise in these datasets.

  • Perform chemical similarity searches and hits triage by applying industry-standard tools and practices.

  • Conduct physiologically-based pharmacokinetic (PBPK) modeling and simulation projects using industry-standard software packages.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

Declared in MS Pharmaceutical Sciences: Applied Drug Development

  • Declared in MS Pharmaceutical Sciences: Applied Drug Development
PHMSCI 756

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Declared in MS Pharmaceutical Sciences: Applied Drug Development

Professors

Spring 2027

Recent recorded grades — Spring 2024: 3.86 GPA, 85.7% A/AB (n=7 letter grades); Spring 2025: 3.78 GPA, 88.9% A/AB (n=9 letter grades); Spring 2026: 3.89 GPA, 88.9% A/AB (n=9 letter grades). Includes jointly taught sections.

Recent recorded grades — Spring 2024: 3.86 GPA, 85.7% A/AB (n=7 letter grades); Spring 2025: 3.78 GPA, 88.9% A/AB (n=9 letter grades); Spring 2026: 3.89 GPA, 88.9% A/AB (n=9 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

SIN YIN LIM is recorded teaching in Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

SPENCER ERICKSEN is recorded teaching in Spring 2023, Spring 2024, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LAB 001Online Only0 / 400
LAB 002Online (some classroom)0 / 100

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.86 GPA, 85.7% A/AB (n=7 letter grades); Spring 2025: 3.78 GPA, 88.9% A/AB (n=9 letter grades); Spring 2026: 3.89 GPA, 88.9% A/AB (n=9 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Data analysis techniques in drug discovery and development
  • Application of industry-standard software and public databases
  • Best practices for data processing and management ensuring experimental reproducibility
  • Troubleshooting skills through critical evaluation and root cause analysis
  • High throughput screening (HTS) dataset analysis
  • Chemical similarity searches and hits triage
  • Physiologically-based pharmacokinetic (PBPK) modeling and simulation

Skills

  • Identifying relevant software platforms, programming tools, and databases for drug development tasks
  • Applying best practices, critical thinking skills, and troubleshooting techniques to data analysis projects
  • Completing data analysis projects representative of those used in drug target characterization
  • Analyzing large high throughput screening (HTS) datasets to confirm positive hits and identify common issues
  • Performing chemical similarity searches and hits triage using industry-standard tools and practices
  • Conducting physiologically-based pharmacokinetic (PBPK) modeling and simulation projects using industry-standard software

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
PHMSCI 756 · Spring 2027

Introduction to Data Analyses in Drug Development

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:718:026418",
    "course_id": "PHMSCI 756",
    "course_uid": "course_67978614b779bbb175342a70",
    "term_id": "1274",
    "source_course_id": "026418",
    "source_subject_id": "718",
    "title": "Introduction to Data Analyses in Drug Development",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "3f62686f3187a1b1f079b01a51bd273214d1d8704acbe9af42fda41a1605a230",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}