EC005 Data Analysis

Class 1: Introduction

Dr. Colleen O’Briant

University of California, Riverside

Fall 2026

Today

  1. Who I am
  2. What this course is about
  3. How class and homework will run
  4. How you will be graded
  5. What to do before next time

About me

  • PhD in Economics, University of Oregon, 2024
  • Second year at UCR
  • Research: econometrics and machine learning, especially methods and models of individuals making choices over time and under uncertainty
  • Teaching: building classes with high standards and high support, and teaching with the Socratic method

Other courses I teach

Course Topic Prereqs When
EC102 Intermediate Microeconomics 002, 003, Calculus Fall
EC104A Intermediate Microeconomic Theory 002, 003, Calculus Fall
EC060 Engineering Economics None Winter
EC136 Empirical Financial Economics EC107 Spring
EC111 Machine Learning for Economics EC107 Summer, perhaps

If you like the way this class runs, you know where to find more of it.

Outside the classroom

  • Grew up right outside Washington, DC
  • Two dogs: Lila (golden retriever) and Roxy (australian shepherd)
  • Enjoying exploring Southern California with both of them

Where to find me

Office: 4106 Sproul Hall (the building closest to the parking lot, fourth floor)

Office hours: Mondays and Wednesdays, 3 to 4 pm, or by appointment

Email: colleen.obriant@ucr.edu

Come by. Office hours are for questions about the material, about the course, or just to say hi.

What this course is

How economists use data to answer “real-life” questions, in three movements:

The vocabulary of datasummary statistics, group means, visualization, randomness, covariance and correlation

Linear regressionderiving OLS, unbiasedness, standard errors, confidence intervals, hypothesis tests

Cause and effectmultiple regression, omitted variable bias, experiments, instrumental variables

Alongside all of it, you learn to program in R, starting from zero: vectors, tibbles, pipes, dplyr, ggplot2, lm(), custom functions, map(), and simulations of your own.

No prerequisites. You do not need to have written a line of code or taken a statistics course.

The kinds of questions we’ll work on

  • Is it always true that half the class scores below average?
  • What did the engineers’ O-ring data show the night before Challenger?
  • Can you solve a murder mystery with nothing but a database?
  • A herbalist claimed to have lived 196 years. Is that believable?
  • How does a frost in Brazil reveal the demand curve for coffee?

How class will run

I’ll use the Socratic method: all knowledge is already within you.

A course should simply be a series of questions to draw that knowledge out. I’ve written 57 for you: three Problems of the Day in every class.

I hope you’ll find: an idea you work out for yourself is understood far more deeply than one you are told.

This only works if you’re in the room! Skip class, and this course will be very challenging.

Thinking versus “studenting”

Most of what school rewards is not thinking. It is studenting: pleasing the teacher, gaming the system for grades, divining what will be on the test.

Those are skills, but they have little to do with thinking hard about the content.

In this class, we’ll practice thinking hard about data every day. This will be very different from most classes you’ve taken, where the emphasis is on studenting over thinking.

Thinking in the Age of AI

For the first time in history, machines can solve all kinds of thinking tasks for us. Completely amazing, but early research says:

  • Students who practice math with AI do worse once it is taken away (Bastani et al., PNAS, 2025)
  • People who write with AI engage their brains less, produce undistinctive work, and barely remember what they wrote (Kosmyna et al., 2025)

Relying on AI makes our thinking muscles atrophy, and eventually we lose confidence in our own ability to think. What’s the solution? Practice thinking hard, the way we’ll do in this course.

What you need

There is no textbook to buy. The workbook is the text.

  • A computer where you can install R and RStudio (both free); HW0 walks you through it
  • Paper, a calculator, and something to write with, every class. Graphing paper will sometimes be needed.
  • Canvas, for quizzes and homework submission

Class is technology free: no laptops, no tablets, no phones. A calculator is fine.

How you will be graded

Percent Category
70 Ten quizzes (7% each)
10 Ten homeworks (1% each)
10 Rotating front row
10 Attendance

Full details are on the Grading page of the workbook.

Quizzes

  • Ten quizzes, 25 multiple choice questions each, on Canvas with Browser Lockdown
  • A one week window for each quiz (Monday morning through Sunday night); you choose when to take it
  • 50 minutes per attempt, up to 3 attempts, 24 hours apart; highest score counts
  • Feedback comes by topic, not by question, so you know what to review before your next attempt

To study: the “check your understanding” questions at the end of every classwork and homework page.

Homework: koans

  • Koans are short guided R scripts with blanks to fill in and automated tests that tell you when you have it right
  • Assigned at the end of the week, due before Monday’s class, uploaded to Canvas
  • Designed to be very gentle and beginner friendly
  • Stuck? Come to office hours, ask one another, or email me

No credit for late homework. Submit what you have before the deadline, even if it is not finished.

Attendance

  • I collect your notes on paper at the end of each class and return them at the next one
  • I am looking for evidence that you worked through each problem: your reasoning and attempts, not just final answers
  • For each problem, one especially clear set of notes goes into the workbook as an example (name redacted; tell me if you want to opt out)

Rotating front row

  • On your two assigned days, sit in the front row and share a desk whiteboard with a partner
  • I pose a problem, you get ten minutes, and I look over your shoulder
  • You do not need to solve it; I am looking for honest effort and staying engaged
  • Engaged in the front row means full credit for the day, whether or not you are called up

Each front row day is 5% of your grade. Your dates are listed by last name on the Grading page. Emergencies: email me with documentation and we will reschedule.

This Week

  • Take the class survey today; your anonymous answers become the data set we use all term
  • Read the Introduction and Grading pages in the workbook
  • Look up your front row dates
  • HW0: install R and RStudio
  • Class 2 (Wednesday): Data Visualization. Graphing paper and a calculator will be useful.

Questions?

4106 Sproul Hall · Mondays and Wednesdays 3 to 4 pm · colleen.obriant@ucr.edu