under construction! will be updated
56:219:601,
56:824:719: directed (independent) study special problems / colloquium [aka "data (science) project"]

56:219:603, 56:219:701: data science capstone/thesis

https://theaok.github.io/dirStu current outline/syllabus
Fall 2026; Wed or Thu time TBA, 321 Cooper St, computer lab in the back of the first fl [note, you can also use the lab outside of the class time--just stop by my office and ask me for the key]

instructor
  • Adam Okulicz-Kozaryn adam.okulicz.kozaryn@gmail.com
  • office: 321 Cooper St, room 302; office hours: Thu 1-2, and by appointment
  • this semester always at school on Wed and Thu; usually whole day; stop by

    • prerequisites

  • Data Science students: You need to be comfortable using a computer. Some minimum knowledge of (Python, R, or Stata) and (data-management or computer science) is necessary, such as my data management, visualization, or GIS courses: https://theaok.github.io/teach
  • Public Policy/Administration and Prevention Science students: no prerequisites

    • course description and learning objectives/outcomes

  • This is not a regular course, rather a series of seminars.
  • Likewise, this is not a regular syllabus, rather an outline with basics only.
  • Essentially, this is a seminar series, where we are working on a "publishable paper."
  • "Publishable paper" is a paper of high quality that is almost ready to be submitted to a journal for publication, and only less amount of work than already done remains to be completed over next few months after the end of a semester.
  • (And ideally we should complete that work and actually submit to journal at a later date, but this is not a requirement in this course and is not graded).
  • You may co-write the paper (upto 2 people) but then the paper must be 2 times better than a single-authored paper.

    • required textbooks and materials

  • There are no required textbooks. All required materials (code, readings) will be provided.
  • In our meetings, especially at the beginning and end we will be going over https://theaok.github.io/generic/howToPaper.html, my loose notes on paper writing.

    • requirements

  • Consistency is the key. Sure, some flexibility is there, you can miss a week or maybe even 2. Indeed working hard continuously for some time and then taking time off may be the way to go. But if you do not do anything for couple weeks, you will be in trouble. Definitely, if you do not do much throughout the semester and try to make it up in the final few weeks, it will not work.
  • Strictly speaking an advice, rather than a requirement, but in practice really a requirement, as it is virtually impossible to succeed otherwise: ask often many questions and meet with me one-on-one
  • You will write an empirical paper/report (or GitHub page) on any topic using one or more of the techniques covered in this course. A typical paper will be 5 to 20 double spaced pages. I will give you comments and help with the paper, and it is a good opportunity to produce a paper that will be later published in a journal.
  • I will also grade the code that you wrote to produce the results in your paper. You will submit not only paper, but also code that produced results in the paper; in fact, in some cases, you can just submit the code.
  • Lets plan around mid-semester to have a solid draft to know where we stand.
  • We will try to meet as a group in the beginning to go over basics, brainstorm and get going; and towards the end to present and learn from each other; in the middle we probably would work more one on one.

    • make no mistake, this is not walk in the park, the bar is high: to get an A it has to be "publishable" at the end of semester or at least "publishable" after 1 set of easily doable revisions as per my final comments

    56:219:603 / 56:219:701 data science capstone/thesis

    lets go over the course description (this is from sp2025, and will be updated (aug18 2026 update: probably will be updated in s27, do not see anyone registered this fall f26):

    The Data Science Master's project is a capstone project: it is the culmination of all the coursework that has brought to this point. You will want to make sure that this project is something that you can highlight prominently on your resume!
    Here are the expectations in brief (and some of these points will be addressed in more detail in forthcoming modules):
    The project will be a substantial software application involving data science techniques and tools. This means that at a minimum, it must involve substantial data volume, appropriate data-cleaning, exploratory data analysis, visualization, and domain-specific data analysis that involves data mining, machine-learning or AI techniques. It can be a group project but with no more than two students per group. Complexity of group projects is expected to scale commensurately with group size. The project deliverables will include a class demonstration, a comprehensive written report, and a code archive evaluated by the advisor. This is important: the project must be substantially different from any previous projects, independent study or coursework projects that you have already completed. I intend to be very strict about this.
    Overall, this course will be run in hybrid format:
    There will be two initial in-person class meetings from 12:30pm to 1:45pm in Armitage 105 on Monday, January 27 and Wednesday, January 29. The Monday meeting will provide additional details about required elements: code organization, documentation, and report format. In the Wednesday meeting each student (or group of two) will present a flash talk (a 5-minute presentation) on their project idea. After next week, students will maintain a regular cadence of meetings with their project advisors by mutual agreement. Presentations of projects will be on April 30 and May 5. Details will be provided sometime in the middle of the semester.
    Please let me know immediately by email if (a) you are among the students working with Dr. Dehzangi, Dr. Sanchirico or Dr. Okulicz-Kozaryn, or (b) you will be working with me on the Master's capstone. If you are in category (b), then also indicate in your email any specific areas or data sources you have in mind for your project: I am available today from 12:30pm to 2pm and later from 3pm to 4:30pm in my office if you want to meet me in connection with this.
    Finally, you must create a repository for your project work: I will require access to the repository to check progress, regularity of commits, and addressing of issues or pull requests from me or your project advisor



  • https://rutgers.instructure.com/courses/345052/ datasets; and tools and guidelines
  • https://rutgers.instructure.com/courses/345052/discussion_topics/4095502 info on capstone projects


    • (note: will be adding more) examples from the past:
  • https://huggingface.co/spaces/Krish264/NutriWeb
  • https://github.com/pavansatya/NutriWeb

    • data science independent study examples

    [note: will be adding more]
  • time-series / arima https://colab.research.google.com/github/chetan-957/Independent-Study/blob/main/Bitcoin.ipynb
  • time-series / sentiment analysis https://colab.research.google.com/github/sg2083/independent_study/blob/main/Sentiment_analysis_29_04_wip.ipynb
  • machine learning and happiness: README: https://github.com/MAhshidfs1367/Moonlight/blob/main/ReadMe.pdf; and code: https://github.com/MAhshidfs1367/Moonlight/blob/main/BRFSS_Model_11_August_2026.ipynb

    • rough schedule/topics

    meet and greet discuss research ideas; importance of literature review

    focus on lit rev https://theaok.github.io/generic/howToGoogSch

    everyone hopefully has better crystallized research idea: mini presentations

    a basic structure of the paper: intro, lit rev, your study, discussion and conclusion incl limitations/future research; ; and skim through TOC https://theaok.github.io/generic/howToPaper.html

    following weeks meet more loosely one on one or in small groups

    final 2 weeks or so meet back in a group; final presentations

    just to be safe, delete the data you have posted online, you never know: someone may be picky about it

    rules

    do not share or link to class videos! These videocasts and podcasts are the exclusive copyrighted property of Rutgers University and the Professor teaching the course. Rutgers University and the Professor grant you a license only to replay them for your own personal use during the course. Sharing them with others (including other students), reproducing, distributing, or posting any part of them elsewhere -- including but not limited to any internet site -- will be treated as a copyright violation and an offense against the honesty provisions of the Code of Student Conduct. Furthermore, for Law Students, this will be reported by the Law School to the licensing authorities in any jurisdiction in which you may apply to the bar. attendance Attendance is recommended. Be advised that you are responsible for any material covered in the class, whether or not it was in the readings or lecture notes. You are also responsible for any announcements made in class. For most students, attendance is simply essential to learning the material. If you do need to miss a class, be sure to consult with a fellow student to learn what transpired.

    incompletes: Generally speaking, the material in this course is best learned as a single unit. I will grant incompletes only in cases where a substantial change in life circumstances occurs that is beyond the control of the student, and only with appropriate documentation.

    study groups. You are encouraged to form a regular study group. Many students over the years have found the study groups to be very helpful. Study groups are permitted and encouraged to work on the problem sets together. However, each individual student should write up his or her own answer to hand in, based on his or her own understanding of the material. Do not hand in a copy of another person’s problem set, even a member of your own group. Writing up your own answer helps you to internalize the group discussions and is a crucial step in the learning process.

    Academic Integrity. I am very serious about this. Make no mistake--I may appear accommodating and informal--but I am extremely strict about academic integrity. Violations of academic integrity include cheating on tests or handing in assignments that do not reflect your own work and/or the work of a study group in which you actively participated. Handing in your own work that was performed not for this class (e.g. other class, any other project) is cheating, too. I have a policy of zero tolerance for cheating. Violations will be referred to the appropriate university authorities.

    For more information see http://fas.camden.rutgers.edu/student-experience/academic-integrity-policy

    Accommodating Students with Disabilities. Any student with a disability affecting performance in the class should contact the disability office ASAP: https://ods.rutgers.edu/