Computing in Context, Fall 2026¶

Course Description¶

This introductory course will explore computing concepts and coding in the context of solving policy problems. Such problems might include troubleshooting sources of environmental pollution, evaluating the effectiveness of public housing policy or determining the impact that local financial markets have on international healthcare or education. Using policy scenarios as examples, students will be exposed to topics including: requirements gathering, data collection, data cleansing, writing pseudocode and code, using Python packages to help solve policy problems, presenting technical solutions and the constraints of computing. The hands-on nature of the class will help students to develop a strong, transferable skill-set that could be applied to both current coursework and future employment. Between the computer science and policy context lectures, students will see how computer science will become a fundamental component of their policy analysis education.

Course Information¶

• Course Number: DSPC6000IA, formerly INAFU6006

• Credits: 3

• Prerequisites: none

Instructors¶

• First half: Adam Cannon, cannon@cs.columbia.edu

• Second half: Aidan Feldman, alf2215@columbia.edu

Teaching Assistants (TAs)¶

Section 1:

• Mery Situmorang - mhs2231@columbia.edu

• Sophia Cain - sac2318@columbia.edu

Section 2:

• Aria Kovalovich - aak2257@columbia.edu

• Sam Fu - sf3318@columbia.edu

Meeting Information¶

• All class sessions take place in IAB.

• For DSPC6000IA, you need to register for the lectures but don’t need to register for the labs separately.

  • This isn’t clear in Vergil, at time of writing.

• Students should bring a laptop to all labs.

• See Contexts and Structure for more details on how the course is organized.

• Office hours

Section 1¶

Section 2¶

*Note that the lecture time is different for the second half of the semester.

Course Overview¶

Relationship to Other Courses¶

• Comparison with Python for Public Policy

• This is a prerequisite for some Data Science for Policy (DSP) courses, see this diagram.

Contexts and Structure¶

Computing in Context has multiple sections, each of which correspond to a different “context”. Most are listed under the course number COMS1002W, SIPA’s are DSPC6000IA.

The lectures in the first half of the semester have students from multiple contexts, and are taught by Adam Cannon. The lectures in the second half of the semester are context-specific; SIPA’s are taught by Aidan Feldman. The labs (a.k.a. recitations) are context-specific are context-specific the entire semester.

For example, let’s say a student is in SIPA (DSPC6000IA) section 1. The first half of the semester, they will attend combined lectures with students from Biology, Linguistics, etc., while doing labs with just their SIPA section. The second half of the semester, they will have both lectures and labs with their SIPA section only.

See also:

• Meeting Information

• Background on the course

Testing out¶

Computing in Context is a required course for the Data Science for Policy (DSP) concentration and the Data Science for Public Policy minor, and a prerequisite for various courses. If you have Python+pandas experience, you can test out. The test will:

• Cover the same topics as the course

• Be done in person

• Take 90 minutes (or less)

• Be closed-book

If you pass, you’ll be exempt from Computing in Context as a requirement and prerequisite. You’ll choose three credits of other DSP course(s) in its place, and be able to go straight into more advanced classes.

If you don’t pass, you’ll need to take Computing in Context as normal. Your grade will be unaffected.

To sign up for the test, contact Aidan.

Grading¶

See the combined syllabus for details. See Gradescope for the assignment rubrics. The final grades for each SIPA section will be curved.

Even if you use a freebie for a lab, working through the material is recommended.

Schedule¶

For weeks 1-7, see the combined syllabus.

See also: Academic Calendar

Generative AI overlap¶

The Generative AI course is four Fridays, 10am-5pm. This overlaps with the Computing in Context labs. See Attendance. For the November dates, you’ll need to use your freebies.

Communications¶

Assignments, due dates, and other aspects of the course may be modified mid-course. As much advance notice will be given as possible.

Getting help¶

• Troubleshooting and other communications between class sessions will be through Ed Discussion.

  • This way, other students can respond and/or benefit from the answers.

  • Don’t just say “I got an error.” Include the relevant line(s) of code and the full error, copied-and-pasted or as a screenshot.

    • See Academic Integrity policy for guidance on sharing solutions.

• The instructor/TAs will try to respond within 24 hours, 48 hours max, if someone else hasn’t already.

• Office hours are also available.

• Email is also an option, though please only use for questions that aren’t appropriate for others to see.

Course Policies¶

Wait List¶

If there ends up being a wait list:

• We expect churn in enrollment, so please be patient.

• One reason for the class size limit is ensuring everyone has a chance to participate. Therefore, it will not be exceeded to accommodate individual students.

• To be fair to everyone, enrollment is first come first served.

• How the wait list works

• Worst case, if you don’t get in:

  • See the other Python course options

  • There are a lot of other ways to learn Python

Auditing¶

See the school policies. Students must be officially registered. If there’s a wait list, priority for spots in the class will be given to students taking it for credit. Once registered: To receive R-credit, every assignment should at least be attempted and submitted. At the end of the course, please remind the instructor that you were auditing.

Attendance¶

See also: The combined syllabus

Attending class is mandatory, but most importantly, important. Learning programming requires commitment from the part of the student and the skills are built out of practice. If you miss an experience in class, you miss an important learning moment and the class misses your contribution.

Attendance is only taken for the lab sessions; see below and grading for details. Classes will not be live-streamed or recorded. If you are absent, we trust that it’s for a good reason: scheduling conflicts, religious observance, etc. We don’t excuse absences beyond that, except in exceptional circumstances. If you’re sick, please stay home and rest.

You are responsible for getting caught up on what was covered, without asking the instructors/TAs to re-teach the material to you. You may want to ask a classmate for notes.

Labs¶

See the combined syllabus.

Students are generally asked to attend the lab for their section. In the first half, all contexts do the same material. If you have a conflict, you can attend one of the a lab from any context. That said, SIPA’s labs are longer than the other contexts’ — they include more introduction and walkthrough.

For the second half, the labs are SIPA-specific. If you have a conflict, the only other lab it would make sense to attend is the other SIPA section.

Devices¶

To encourage focus and participation, students are asked to keep laptops closed and phones away in lecture by default. (Accommodations for students with disabilities are an exception.) You’ll be asked to open the laptops for in-class exercises.

Academic Integrity¶

If you are copying and pasting from a source (see below), it must be cited. This doesn’t need to be in a formal style like APA - a comment (and link, where possible) is fine.

If you did most of the work yourself, it’s ok. If most of the work was copied from elsewhere, it’s plagiarism, and will be reported.

Sources¶

Anything outside of the provided course materials is considered a “source”. This includes:

• Other students

• Online resources

• Books

• Generative AI (ChatGPT, Claude, Copilot, Gemini, etc.)

Other notes¶

• Students are welcome to work with one another, as long as:

  • You indicate who you worked with

  • The submissions are different

• Students are more than welcome to share approaches and code snippets in the Discussions, so long as they aren’t giving the full solution away.

• Students will post the Projects and Lab 8 publicly as part of their portfolio site, since they’re open-ended. Other assignments (with “correct answers”) cannot be posted publicly, to avoid cheating in future semesters. You are more than welcome to share any of your notebooks with specific people, such as future employers.

• Tips for working with generative AI

SIPA Academic Integrity Statement¶

The School of International & Public Affairs does not tolerate cheating or plagiarism in any form. Students who violate the Code of Academic & Professional Conduct will be subject to the Dean’s Disciplinary Procedures.

Please familiarize yourself with the proper methods of citation and attribution. The School provides some valuable resources online; we strongly encourage you to familiarize yourself with these various styles before conducting research. Cut and paste the following link into your browser to view the Code of Academic & Professional Conduct and to access useful resources on citation and attribution: https://bulletin.columbia.edu/sipa/academic-policies/

Violations of the Code of Academic & Professional Conduct should be reported to the Associate Dean for Student Affairs.

SIPA Disability Statement¶

SIPA is committed to ensuring that students registered with Columbia University’s Disability Services (DS) receive the reasonable accommodations necessary to participate fully in their academic programs. If you are a student with a disability and have a DS-certified accommodation letter, you may wish to make an appointment with your course instructor to discuss your accommodations. Faculty provide disability accommodations to students with DS-certified accommodation letters, and they provide the accommodations specified in such letters. If you have any additional questions, please contact SIPA’s DS liaison at disability@sipa.columbia.edu.