Course Syllabus Spring '14

Contact Information

Student Facilitator 1: Evan Ott

Student Facilitator 1 Email: evan.ott@utexas.edu

Student Facilitator 2: Will Beason

Student Facilitator 2 Email: beason@utexas.edu

Office Hours: W 2-4, BIO 301


Faculty Supervisor: Dr. Greg Sitz

Faculty Supervisor Email: gositz@physics.utexas.edu

Office Hours: W 3-4, H 10:30-11:30 in RLM 10.313


Class: H 8:30-9:30 in CPE 2.206

Class Homepage and Textbook: http://www.evanott.com/data-analysis/

Email for Assignment Submission: data.analysis.physics@gmail.com

Course Description

This course was devised by the UT-Austin chapter of the Society of Physics Students officers in order to introduce physics, math, astronomy, chemistry, and computer science students (and students interested in those and related subjects) to the LATEX and Mathematica programming languages for use in data analysis. While designed with preparation for PHY 353L in mind, the skills learned in this course apply to a broad array of courses and fields of study and are directly applicable to both academic and industrial applications. Students enrolling in this course would benefit from prior exposure to programming, but no experience is required. This course is being offered through the Physics Department at UT Austin as PHY 110C.

Learning Outcomes

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

  1. Use Mathematica to analyze scietific data and create graphical representations of that data
  2. Use LATEX to construct scientific documents including images, tables, and hyperlinks, completing assignments under different constraints to familiarize yourself with the language
  3. Combine the skills listed above in a final project

Course Structure

This course will largely follow the ‘flipped-classroom’ model, involving readings and homework assignments to be completed outside of class in an effort to make class-time as interactive and helpful as possible. Each week, students will complete a reading assignment from an online textbook created for this course by the Evan Ott and Will Beason and edited by the Society of Physics Students (see the link above). This textbook is largely technical in nature, presenting new features of LATEX and Mathematica each week. Students will read the sections of the textbook before class, then begin a programming assignment that will be due the following week over that material. First-drafts of the programming assignments will be due the day before class.

During class, students will first present solutions to the assignment they completed for that week and discuss the merits and failings of particular methodologies used, in a variant of the Moore model of teaching. Particularly toward the end of the semester, students will engage in a debate over technologies employed in solutions (is a 3D graph with one axis of time more relatable than multiple 2D graphs? are in-line equations better for space or too difficult to read? is using built-in distribution functions more enlightening than re-writing them in a more convenient form?).

The student facilitator will then review topics that proved most challenging for students based on the first-drafts submitted the previous day and help with troubleshooting errors. Then, each week, the student facilitator will review the week’s reading material. If time permits, students will begin work on the next week’s assignment, working collaboratively. An example class agenda can be found below.

Office hours will be held weekly (time and location to be provided on the first class day) for students to come ask questions or discuss the week’s reading.

Assignments

Each week, students will be expected to complete assignments related to the objectives outlined above. These will need to be completed largely outside of class, although students are encouraged to bring personal computers to class for help with technical issues or for referencing their own work when discussing solutions. Software to be used in the course will include traditional LATEX editors (use of Lyx is discouraged in favor of students working directly with code), and the latest edition of Mathematica. As needed, instructions will be given on how to download any software required to complete assignments, but all required software is available for use in the Physics Microcomputer Laboratory in RLM 7.306 (http://pmcl.ph.utexas.edu).

A final project will be given in lieu of the last two weekly assignments. This project will be cumulative, incorporating the data analysis skills learned in relation to the Mathematica portion of the course, and the typesetting skills learned in relation to the LATEX portion.

A representative assignment covering the cumulative knowledge gained toward the end of the semester may be found below.

Grading Policy

This is a strictly pass/fail class, so you must get credit for at least 60% of the points listed below in order to pass. Students are encouraged to work together on assignments, but are expected to list the students with whom they consulted on each assignment. The final project will be an individual project, although general discussion and help with programming errors is perfectly acceptable and encouraged.

Weekly Assignments (total): 50 pts

Final Project: 24 pts

Solution Presentation and Discussion: 26 pts

There will be no final exam for this course

Weekly assignments are due the day before class at 5pm. Students are encouraged to complete as much of the assignment as possible by this time so that class-time can be used to discuss solutions and technical problems. No penalty will be imposed on late assignments, and assignments may be resubmitted with no penalty until the final class day. Grades for these assignments will be based on completion of the tasks listed and use of newly-introduced topics.

The final project will be graded based on ability to show comprehension of skills learned throughout the semester, including aspects drawn from each lecture and assignment. The final project will be due at the end of the final class by email or other prescribed means to the student facilitator, with only emergency situations excepted.

Students are expected to present their solutions and actively engage in discussion about each week. Over the span of the semester, this means that each class will allow for 2 pts based on engagement, insight, and willingness to present solutions.

Tentative Schedule

This schedule is tentative, due to the potential for questions to dominate the conversation some days. However, it should serve as a guide to the topics to be discussed through the semester.

Week 1 - Introduction to Course; Review of End Goals; Introduction to Mathematica and brief diversion to PMCL

Week 2 - Introduction to Mathematica (continued): Simple programs, graphing

Week 3 - Introduction to Mathematica (continued): Simple programs

Week 4 - Mathematica: Reading in data, simple analysis

Week 5 - Mathematica: More advanced data analysis

Week 6 - Introduction to LATEX: The language, how to download

Week 7 - Introduction to LATEX (continued): My first document

Week 8 - LATEX: More complex documents

Week 9 - LATEX: Formatting and new concepts

Week 10 - LATEX, Mathematica: Make-up week for covering additional topics

Week 11 - LATEX, Mathematica: Combining data analysis and articles

Week 12 - LATEX, Mathematica: Combining data analysis and articles (continued)

Week 13 - LATEX, Mathematica: Pushing limits

Week 14 - LATEX, Mathematica: Wrap-up

Example Class Agenda - Week 4

  1. Students are to have read textbook information on ‘Mathematica: Reading in data, simple analysis’ before coming to class
  2. Rough drafts of “Mathematica: Simple programs’ assignment solutions due the day before class
  3. 1-2 Students present solutions to ‘Mathematica: Simple programs’ homework (10-15 min)
    1. Class discusses extent to which solutions address the tasks from the assignment
    2. Class discusses relative merits of technologies presenters employed versus other potential solutions
  4. Student facilitator leads discussion of ‘Mathematica: Simple programs’ solutions in terms of identified issues (10 min)
  5. Student facilitator presents over ‘Mathematica: Reading in data, simple analysis’ material (10-15 min)
    1. Presenter(s), class, and student facilitator help address questions from classmates
  6. Student facilitator distributes homework assignment over ‘Mathematica: Reading in data, simple analysis’ and answers questions about it. Student facilitator assigns the ‘Mathematica: More advanced data analysis’ reading (5 min)
  7. If time permits, students begin work on next week’s assignment, working collaboratively (5-15 min)

Students with Disabilities

Any student with a documented disability who requires academic accommodations should contact Services for Students with Disabilities at 471-6259 (voice) or 1-866-329-3986 (Video Phone) as soon as possible to request an official letter outlining authorized accommodations.

Academic Integrity

Students who violate University rules on academic dishonesty are subject to disciplinary penalties, including the possibility of failure in the course and/or dismissal from the University. Since such dishonesty harms the individual, all students, and the integrity of the University, policies on academic dishonesty will be strictly enforced. For further information please visit the Student Judicial Services Web site: http://deanofstudents.utexas.edu/sjs.

About Student Facilitator 1

Evan Ott is a physics and computer science major and the 2012-2014 president of UT’s chapter of the Society of Physics Students. He has previously team-taught a 4-week seminar on LATEX (materials available here) and coached his high school’s computer science club for two years in Java leading them to win several competitions. He has served as an undergraduate teaching assistant for Dr. Sacha Kopp’s UGS 303: Originality in the Arts and Sciences and PHY 110C: Science of the Times. In his time as SPS president, he has consistently worked to help provide resources to better connect students with technical skills applicable to both research and industry.

About Student Facilitator 2

Will Beason is a senior double majoring in Mathematics and Physics. His primary interests are cybernetics and information theory, and he hopes to either go to graduate school or get a job relating to these subjects after graduation. He has given several talks on data analysis, and is currently acting as a data analysis consultant for MeasureCP. He also runs a blog at will.ketobot.com where he attempts to apply the theories behind his interests to the world.

Example Assignment (near end of semester)

Your first goal this week is to read the data in single.txt which has points (x,y) in CSV format. Express this data graphically. From what type of distribution might this have arisen? (refer to the textbook’s appendix if you aren’t familiar with distribution functions)

Fit this distribution function to your data, and plot the data and function on the same graph. Do you think you were right? Use an appropriate statistical test for the function to determine if it is likely you have selected the correct distribution.

Once you are satisfied with the choice of distribution, read in timeseries.txt which has points (t,x,y) in CSV format. Can you find a way to (mostly) re-use your code to express this data? The graphs for single.txt should correspond exactly to those for the first timestamp in timeseries.txt, but future times seem to evolve the distribution.

Fit the distribution function to the data at each timestep, then plot each feature of your statistical model versus time. Are there patterns that emerge? Use the curve-fitting techniques to derive the evolution of the system over time. Can you think of a physical situation that might behave in this way?

Finally, combine the graphs, relevant equations, and discussion in a LATEX document.