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Data Science

Physics + Data Science: 3+2

Physics already trains you to model systems and compare predictions with measurements. The 3+2 pathway extends that preparation to statistical learning, data engineering and applied projects, combining your Physics BA with an MS in Applied Data Science in a planned five years.

How the pathway fits your degree

Calculus and Linear Algebra are already required. Add Python and Probability I.

Calculus I
Part of the Physics mathematics preparation.
Introduction to Programming in Python
Use the listed introductory Python course or confirm prior preparation.
Probability I (one module)
Plan the probability module before Year 3.
Linear Algebra
Part of the Physics mathematics requirements.

Applications in Physics

Signals and sensors

Clean time-series measurements, detect events and compare signal-processing approaches.

Simulation and inference

Use simulated and observed data to estimate parameters and test model predictions.

Experimental analysis

Track calibration, uncertainty and reproducibility across an instrument-based study.

Plan your undergraduate work

Coursework

Coordinate mechanics, electromagnetism, quantum, thermal physics and laboratory offerings with the graduate fall courses.

ISPs

The live 2026-27 Physics AOC requires two Physics ISPs. Place them within your three undergraduate ISPs.

Thesis

If your project needs equipment setup or a long measurement run, begin before the final writing term. Agree on the experimental endpoint with your sponsor.

These are sample schedules. Work with your area-of-concentration sponsor to confirm course prerequisites, actual offerings, remaining BA requirements and the fall thesis deadline. Graduate courses do not automatically replace undergraduate requirements.

Sample course schedule: Years 1–5

Each course or thesis activity appears on its own line. General-education choices are examples to review with your sponsor.

HUM 1020 and ISC 2076 carry two credits each. Probability and seminar entries are half-course modules. This example includes three courses toward the BA Exploring the Human Experience requirement. Plan the fourth with your sponsor before graduation.

Year 1 fall

Undergraduate

  • PHY 2048 Physics I (Calculus-Based)
  • PHY 2071 Physics I Laboratory
  • MAC 2311 Calculus I
  • COP 2047 Introduction to Programming in Python
  • ISC 2076 Introduction to Techne (2-credit BA choice)

Year 1 spring

Undergraduate

  • PHY 2049 Physics II (Calculus-Based)
  • PHY 3803 Physics II Laboratory
  • MAC 2312 Calculus II
  • MAS 2103 Linear Algebra
  • HUM 1020 Homer's Odyssey - Introduction to Humanities (2-credit BA choice)

Year 2 fall

Undergraduate

  • PHY 2105 Modern Physics
  • PHY 3822 Modern Physics Laboratory
  • MAC 2313 Calculus III
  • STA 2442 Probability I
  • ENC 1101 English Composition (BA/general education choice)

Year 2 spring

Undergraduate

  • PHY 4220 Classical Mechanics
  • MAP 3302 Ordinary Differential Equations
  • PHY 3513 Thermal Physics
  • POS 2041 American Government and Civics (BA/general education choice)

Year 3 fall

Undergraduate

  • PHYS 4250 Electricity and Magnetism
  • ECO 2023 Principles of Microeconomics (BA/EHQ choice)

Graduate

  • IDC 5204 Applied Statistics I
  • IDC 5110 Data Munging and Exploratory Data Analysis

Year 3 spring

Undergraduate

  • PHY 4610 Quantum Mechanics
  • PHYS 4500 Physical Optics
  • Physics thesis research and analysis
  • CLA 2103 Greek Civilization (BA/EHQ choice)

Year 4 fall

Undergraduate

  • Physics thesis writing and BACC
  • PHI 2603 Ethics and Values: Human Flourishing (BA/EHQ choice)

Graduate

  • IDC 5120 Programming for Data Science
  • IDC 5130 Databases for Data Science

Year 4 January

Graduate

  • IDC 5295 Industrial Workshops (non-credit)

Year 4 spring

Graduate

  • IDC 5205 Applied Statistics II
  • IDC 5112 Communications and Applied Domains in Data Science
  • IDC 5210 Applied Machine Learning
  • IDC 5131 Distributed Computing

Year 5 fall

Graduate

  • IDC 6200 Advanced Statistical Modeling
  • IDC 6215 Deep Learning and AI
  • IDC 6250 Practical Data Science

Year 5 spring

Graduate

  • IDC 6294 Industrial Practicum II

The MS requires 36 graduate credits. Complete the BA credit, general-education and area-of-concentration requirements as well.

Thesis, BACC and independent study

Plan three January Independent Study Projects (ISPs) during Years 1–3, including the projects required by your area of concentration. Use this work to prepare for your senior thesis.

Develop the thesis with your sponsor during Year 3. The original 3+2 plan calls for completing the thesis, Baccalaureate Exam (BACC) and all remaining BA requirements in Year 4 fall, before formal graduate entry in spring.

If thesis timing changes, discuss a revised course and graduation plan with the program director. The original plan places formal graduate admission in the first spring after all BA requirements are complete.

Sample January projects

  1. Year 1: Physics measurement/computation project
  2. Year 2: advanced Physics research project
  3. Year 3: Physics thesis pilot study and research preparation

Plan your experiments with your thesis sponsor so you have time to collect results, write and defend in Year 4 fall.

Advising and references

For help planning your pathway, contact graduateDS@ncf.edu.

References: NCF 3+2 pathway; original program proposal (PDF).