# Welcome Alex Cardazzi All materials can be found at alexcardazzi.github.io. ## Welcome to ECON 311 Hi, my name is Dr. Alex Cardazzi, and I will be your instructor for this course. I am an applied microeconomist studying urban, health, and sports economics, and I’ve taught this material for several years. This course has been rebuilt for an 8-week accelerated, fully online format, so read this page carefully even if you’ve taken a similar course before. ## How This Course Works This course is **asynchronous**: there are no live class meetings you are required to attend. Instead, each week you will work through a module made up of short lessons like this one, at your own pace, on your own schedule. Due dates still matter, but *when* during the week you do the work is up to you. The course runs 7 content weeks. Each week corresponds to one module, and each module is broken into several short lessons — you are reading the first one now. Lessons are meant to be worked through in order within a module, since later lessons usually build on earlier ones. ## Where to Find Things - **Course website**: [alexcardazzi.github.io/econ311](https://alexcardazzi.github.io/econ311.html) — nearly everything you need (all lessons, templates, and the syllabus) live here. - **Canvas**: this will be used only for submitting assignments and checking grades. ## What You’ll Be Doing Across the course, you will complete: - An **Onboarding HW** (this week) to confirm your setup works - Four **homework assignments**, each an open-ended data analysis you write up and render to HTML, with a Process Reflection on how you used Claude - A **recorded presentation** with each homework, where you present your report and then answer two questions about it Exact point values and due dates are in the [syllabus](../syllabus.html) — read that next if you have not already. ## About the Recorded Presentations The recorded presentations are probably the most unfamiliar part of this course. They are not a memorization test. With each homework, you will record your screen while you present your report, as if you were briefing a boss or client who has not seen it. Then you will answer two questions, out loud, from an AI playing that boss or client. The questions are friendly, but they are about your own work: what you did, why you made the choices you made, and what a result means. The goal is to confirm that the understanding behind your written work is actually yours. **You are responsible for everything that you submit.** Simple methods you can explain clearly score better than sophisticated methods you can’t. I also reserve the right to ask any student for a short meeting about something they submitted. Each homework explains exactly how the recording works. ## Required Technology: R, RStudio, and Claude Code This course requires three pieces of software: R, RStudio, and Claude Code. This module will walk you through installing all three. Claude Code is not optional — it is a required tool for this course, and you will use it throughout. A separate AI policy document (linked from the syllabus) explains exactly how you are expected to use it; the short version is that it should make you a better analyst, not replace your own understanding, since the recorded presentations will find that gap quickly if it exists. ## A Note on Learning R vs using Claude Learning R is a lot like learning a foreign language: immersion works better than translation. At points, you will find yourself wanting to do things in Excel (or with Claude Code) rather than in R because it will seem easier/faster. It is important that you fight that urge, because it will make you better off in the long run. Struggling through the error messages (that means reading them!) before asking for help (including from Claude Code) is the best way to get better. Also, as a general rule, change one thing at a time rather than rewriting everything at once so you can identify the problem(s).