Data Analysis for Physics#

Physics 398 DAP   Fall 2024

  • Instructors:

  • Class Meetings:

    • Monday and Wednesday from 10:30 am to 11:50 am

    • Room: 276 Loomis Laboratory

  • 3 credit hours


Note: This schedule will evolve throughout the semseter





Aug 26

Course Introduction

HW 01

Sep 02

Probability Theory

HW 02

Sep 09

Important Probability Distributions

HW 03

Sep 16

Theory of Estimators

HW 04

Sep 23

Estimating Probability Density from Data

HW 05

Sep 30

Statistics and Sources of Uncertainty

HW 06

Oct 07

Frequentist and Bayesian Methods

HW 07

Project 01

Oct 14

Stochastic Processes, Markov Chains & Variational Inference

HW 08

Oct 21

Confidence Intervals and Hypothesis Testing

HW 09

Oct 28

Response, Convolution and Unfolding

HW 10

Nov 04

Fourier Methods

HW 11

Nov 11

Time Series

HW 12

Project 02

Nov 18

Monte Carlo and Sampling Methods

HW 13

Nov 25


Dec 02

Bias and Blind Analysis

Dec 09

Machine Learning Methods


Welcome! Data is everywhere. Efficient data analysis leading to solid conclusions requires performant tools and rigorous mathematical techniques tethered by sound scientific methods.

Maybe you have data and not sure how to analyze it. Maybe you are looking to make the most of your precious scientific data and derive sound conclusions not sure how. If so, this course is for you!

This course is designed to provide students with an introduction to data analysis from a physics perspective. Upon completion of this course, students will learn to:

  1. Understand common probability distributions and identify examples of where these distributions occur in physics

  2. Identify sources of statistical & systematic uncertainties and bias, and properly handle them to interpret scientific data

  3. Implement key analysis tasks such as parameter estimation, unfolding, auto/cross-correlation, confidence intervals, hypothesis testing, Monte Carlo simulation, and much more!

This course material is largely independent of material in other physics classes. Students should have taken Phys 211, 212 and 213. There is no programming experience required.

You can find more detail in the Calendar section on the specific topics that will be covered in this course.

Course Logistics#


  • This course will consist of two meetings per week: one lecture period and one in-class practical session.

  • Lecture: Monday from 10:30 am - 11:50 am in 276 Loomis

  • Practical Session: Wednesday from 10:30 am - 11:50 am in 276 Loomis


Online Tools#

There are several online tools you will need to use as part of this course.


We will use Campuswire as a class forum, a way to message the course staff and each other, and a means to submit your attendance question.

Google Colab#

Using Google Colab, you will be able to program your code in a Jupyter notebook and submit it for us to grade. Please sign in to your Illinois account. While working on the assignment, you will share each of your colab assignments with the professor and the TA (but no one else).


On Gradescope, you will submit your assignments and find your graded assignments.


Homework Assignments#

You will be assigned weekly homework assignments that will put into practice what you learned in lecture for the week.

  • You will work on the assignments both during the in-class session on Thursdays and as homework.

  • You will submit your executed (i.e. with “RunAll”) homework notebook via Gradescope.

  • Each assignment is due at the beginning of the next class unless otherwise noted. You may turn assignment in up to one week late for 50% credit (except that all assignments are strictly due the day before Reading Day).

  • Solutions to the homeworks will not be given.

  • You may collaborate on assignments but must submit your own work.

  • Graded homework will be available through Gradescope.


At appropriate times throughout the course, you will select from a list of projects that involve demonstrating and extending your work in class by doing something cool and interesting in data analysys. You must work alone on this (i.e. without collaboration).

For projects you will put together a Jupyter notebook that demonstrates your project. The notebook should have code and demonstrate the task but also be written in an expository way that other students could, in principle, read and learn from. It is submitted in an analogous way as the regular course assignments.

Each project notebook must be submitted via Gradescope for grading.


  • Class attendence and participation: 5%

  • Homework: 70%

  • Projects: 25%

Letter grades will be assigned as follows:

  • A+   [97.0 - 100.0]

  • A     [93.0 - 96.9]

  • A-   [90.0 - 92.9]

  • B+   [87.0 - 89.9]

  • B     [83.0 - 86.9]

  • B-   [80.0 - 82.9]

  • C+   [77.0 - 79.9]

  • C     [73.0 - 76.9]

  • C-   [70.0 - 72.9]

  • D+   [67.0 - 69.9]

  • D     [63.0 - 66.9]

  • D-   [60.0 - 62.9]

  • F     [00.0 - 59.9]



  • Policies as it relates to COVID-19 can be found at

  • If you feel ill or are unable to come to class or complete class assignments due to issues related to COVID-19, including but not limited to testing positive yourself, feeling ill, caring for a family member with COVID-19, or having unexpected child-care obligations, you should contact your instructor immediately, and you are encouraged to copy your academic advisor.

About using code you find on the web or generative AI for homework and projects#

The quickest way to deal with the arcana of programing is to ask Google or ChatGPT for examples of what you are seeking to accomplish. But you will need to use your own judgment in terms of value added for your learning in using these techologies Your generation will need to how learn to work productively in-concert with AI. That - that’s a technological genie out of the bottle. Finding its way back into the bottle is as a likely as a broken glass spontaeously reassembling. As with any external resource, you must always credit the original source of code and other information that you paste into your own programs, notebooks, projects, etc in a comment that includes the original source. If an author says that his/her code is not to be copied or incorporated into your programs, then DON’T.

Students must cite all references, including any code they have used that they did not write themselves. Failure to cite references will be considered an academic integrity violation and be pursued according to University policy, which may include receiving a failing grade on an assignment or in the entire course. Citations do not need to follow any specific format (such as ACM style, etc.) but should mention the author’s name and where the cited work can be found (including a URL, if applicable). In code, a citation can be left in a comment.

Academic Integrity#

You must never submit the work of someone else as your own. We understand that many of you will find it helpful to work with other students to master the course. But when you collaborate with your study group on homework assignments, you must be a full, active participant in developing the solutions that you submit for credit.

It is cheating to receive answers from another student and then use them as your own. It is cheating to submit as your own work solutions that you find by searching on the worldwide web (though see “About using code you find on the web”) or using online tools such as ChatGPT, or by subscribing to an online service that suborns cheating. It is cheating—and a violation of U.S. copyright law—to give (or sell) course material to someone else who intends to redistribute and/or sell it.

All activities in this course, are subject to the Academic Integrity rules as described in Article 1, Part 4, Academic Integrity, of the Student Code.

Sexual Misconduct Reporting Obligation#

The University of Illinois is committed to combating sexual misconduct. Faculty and staff members are required to report any instances of sexual misconduct to the University’s Title IX Office. In turn, an individual with the Title IX Office will provide information about rights and options, including accommodations, support services, the campus disciplinary process, and law enforcement options.

A list of the designated University employees who, as counselors, confidential advisors, and medical professionals, do not have this reporting responsibility and can maintain confidentiality, can be found here:

Other information about resources and reporting is available here: and

Mental Health Services#

Significant stress, mood changes, excessive worry, substance/alcohol misuse or interferences in eating or sleep can have an impact on academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings which are covered through the Student Health Fee. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do for yourself and for those who care about you.

  • Counseling Center (217) 333-3704

  • McKinley Health Center (217) 333-2700

  • National Suicide Prevention Lifeline (800) 273-8255

  • Rosecrance Crisis Line (217) 359-4141 (available 24/7, 365 days a year)

If you are in immediate danger, call 911 *This statement is approved by the University of Illinois Counseling Center.

Students with Disabilities#

To obtain disability-related academic adjustments and/or auxiliary aids, students with disabilities must contact the course instructor and the Disability Resources and Educational Services (DRES) as soon as possible. To contact DRES, you may visit 1207 S. Oak St., Champaign, call 333-4603, e-mail or go to If you are concerned you have a disability-related condition that is impacting your academic progress, there are academic screening appointments available that can help diagnosis a previously undiagnosed disability. You may access these by visiting the DRES website and selecting “Request an Academic Screening” at the bottom of the page.


Useful references#

Quick guides#


Git and GitHub#

Project Jupyter#


This course was developed by Anne Sickles, Jeff Filippini, and Mark Neubauer. It was first taught by Anne Sickles and Mark Neubauer during the Fall 2023 semester.