Computing for Analytical Work

What this course is — and is not

• One credit. Three Mondays, 13:30 to 17:20 each, and one closed-book exam on Wednesday 7 October.
• Not a Python course. The layer beneath your other courses: the filesystem, the shell, processes, Python environments, Git — and the habit of diagnosing what actually went wrong.
• Three sessions, one question each: Where am I? · What is running? · What broke?
• Every lab is a broken project. You diagnose it, repair it, verify the repair, and explain it. The explanation is the skill.

Who I am, as your professor

• Eduardo Ariño de la Rubia — Professor of Practice in Data Science and AI, Department of Economics and Business. You may meet me again in Data Science 2 and in Deep Learning and Applied AI.
• Industry, before this: Meta, Senior Director of Data Science, Reality Labs · Domino Data Lab, Chief Data Scientist and VP of Product · Ingram Content Group, Principal Data Scientist and robotics lead.
• Elsewhere: co-founded DataScience.LA (1,000+ members) · talks at KDD, rstudio::conf, and Rev · host of CEU's Teaching Analytics in the Age of AI podcast.
• Also trained in negotiation and conflict resolution — which is why, in a lab, you get questions from me, not answers.
• RubiaE@ceu.edu · office hours by appointment · GitHub earino

Who else is here

• Boga (Boglarka Petruska) — teaching assistant and co-author of this course, in the room every session. During labs: questions, not answers.
• Nahian (Nahian Ibnat) — teaching assistant and co-author, remote. Reads every lab checkpoint after class and replies on Moodle to every need help line before the next session.
• Each of us has been stuck on exactly the kind of thing this course teaches. Thirty seconds each.

How a session runs — 13:30 to 17:20

13:30 – 15:10   Block 1:  45 lecture · 5 stand-and-stretch · 45 lab
15:10 – 15:40   break — thirty minutes, leave the room
15:40 – 17:20   Block 2:  45 lecture · 5 stand-and-stretch · 45 lab

• Sessions 2 and 3 open with a 10-minute closed-book knowledge check, out of that block's lecture, never the lab.
• The lab is the course. Every lab ends the same way: you explain it to a neighbour, hand up if unsure, and upload your diagnosis note to the lab's Moodle checkpoint before you leave. That is what complete means. Block 1 shows you the details.
• Labs carry no points. They cost you preparation if skipped, not marks.
• Bring the laptop every session. Windows: Git Bash, always.

Three Mondays, one arc

Final exam: closed-book (one handwritten A4 sheet allowed), in person, about 70 minutes, Wed 7 Oct, 10:50–12:30, room D-001. A mock exam with a key comes first.

Under all of it, one loop: observe → locate → hypothesize → inspect → change → verify → explain.

How you are graded

Homework = repair a broken repository + a written diagnosis note + a short video walkthrough in your own voice, graded against a rubric published with the assignment. The explanation carries more weight than the fix. A self-check key posts on Moodle after each late window closes.

The rules that will actually matter to you

• Late homework: one day late at −10%; nothing after Saturday 23:59, because the key posts Sunday.
• Extensions: email me before the deadline, with a reason. Documented illness or emergencies are always accommodated.
• Missed knowledge check: documented absence → that 5% moves to the final. Undocumented → 0. No make-ups.
• Missed lab: do it at home that week and submit the checkpoint; say in the note if you want someone to look.
• Regrading: within 7 days, in writing, naming the rubric criterion. The whole submission is re-read; the grade can move either way.
• Accommodations: the skill is diagnosis, not speech. Written explanations and written or audio walkthroughs exist — arrange them with me in advance.
• Moodle is authoritative for every date.

AI: use it to learn, not to avoid learning

• Yes: explain this error; suggest what to inspect; teach me the concept; improve my writing.
• No: submit code or explanations you cannot defend in person; run commands you cannot explain; describe work you did not do.
• The grading assumes you used AI. It checks whether you understood the result: the video, the neighbour, the closed-book exam.
• Labs 3 and 5 open with 15 minutes of no AI. Terminal and error text only. Announced now, so it is never a trap.
• You are responsible for everything you run, change, and submit.

Before Block 1

• Two places: the course site — syllabus, setup, glossary — at earino.github.io/ecbs5293, and Moodle — deadlines, checkpoints, submissions. Labs and homework are public GitHub repos you clone. You never push. No GitHub account needed.
• Setup check. If yours did not end in ALL CHECKS PASSED, or you have not run it: tell Boga or me now. We come to you during the lab, not the lecture.
• The glossary is for you. A word on the site that confuses you is a bug in the page, not in you. Tell a TA.
• Laptops closed for the first ten minutes of Block 1.