Course Description
An increasing amount of data is now generated in a variety of disciplines,
ranging from finance and economics, to the natural and social sciences.
Making use of this information requires both statistical tools and an
understanding of how the substantive scientific questions should drive
the analysis. In this hands-on course, we learn to explore and analyze
real-world datasets. We cover techniques for summarizing and describing data,
methods for statistical inference, and principles for effectively communicating results.
Prerequisites:
MS&E 120 or equivalent,
and CS 106A or equivalent
Please take note of the following two course policies.
On-time attendance at lectures is required, and attendance at discussion sections is encouraged.
Our aim is to create a collaborative and supportive learning environment.
One of the best ways to learn the course material is to engage with the lectures by asking questions.
If you need to miss a class (e.g,. for an illness or sporting event) or will be late, please
email Sharad prior to the lecture.
In-class attendance checks will be periodically carried out throughout the quarter.
Please do not use electronics (laptops, tablets, phones) during lectures.
But please bring laptops to the Thursday discussion sections, as there will be in-class coding and analysis.
See here
and here on why we institute this policy.
(We're happy to make exceptions in special circumstances.)
We encourage you to attend our 2-part crash course on R.
The first part will be held during discussion section on Thursday, January 10.
The second part will be offered at 6-9pm on Monday, January 14, and
repeated at 6-9pm on Tuesday, January 15 in
200-034.
You can view the R course materials
here.
Instructors
Sharad Goel (
email)
Jongbin Jung (TA) (
email)
Jerry Lin (TA) (
email)
Camelia Simoiu (TA) (
email)
Schedule
Class: Tuesdays & Thursdays @ 1:30 PM - 2:50 PM in
STLC 114
Discussion Section: Thursdays @ 3:00 PM - 4:20 PM in
STLC 114
We use Piazza
to manage course questions and discussion. Please sign up
here.
Office Hours
Mondays @ 5 PM - 7 PM in Shriram 366 (Camelia)
Tuesdays @ 3 PM - 5 PM in Huang 251 (Sharad)
Wednesdays @ 10 AM - 12 PM in GESB 131 (Jongbin)
Thursdays @ 5 PM - 7 PM in Spilker 317 (Jerry)
There are no office hours during the first week of class.
Feel free to schedule an appointment if you would like to meet.
[ Optional ] Textbooks
All of Statistics by Larry Wasserman
(available
online)
R for Data Science by Garrett Grolemund and Hadley Wickham
Statistics by David Freedman, Robert Pisani, and Roger Purves
Natural Experiments in the Social Sciences by Thad Dunning
Computing Environment
We primarily use
R
(
RStudio is the recommended interface),
including the suite of
tidyverse packages.
Evaluation
8 homework assignments (50%)
Final project (20%)
Final exam (20%)
Attendance (10%)
Syllabus
Week 1: Data Exploration & Visualization
Summary statistics, data manipulation, group-wise operations, joins, principles of plotting
Week 2: Statistical Inference I
Chapter 6 of
All of Statistics
Sampling distributions, statistical estimators, confidence intervals
Week 3: Statistical Inference II
Selected topics from Chapters 7, 8 & 9 of
All of Statistics
Maximum likelihood estimation, method of moments, the bootstrap
Week 4: Linear Regression I
Part III of
Statistics, and selected topics from Chapter 13 of
All of Statistics
Correlation, simple linear regression, confidence & prediction intervals
Week 5: Linear Regression II
Selected topics from Chapter 13 of
All of Statistics
Multiple regression, feature generation, model evaluation, normal equations
Week 6: Logistic Regression
Selected topics from Chapter 13 of
All of Statistics
Logistic regression, multinomial logistic regression, model evaluation
Week 7: Bias-Variance Tradeoff
Overfitting, under-fitting, cross-validation, regularization
Week 8: Causal Inference
Rubin causal model, response surface modeling, instrumental variables, diff-in-diff
Week 9: Ethics, Privacy & Fairness
Institutional review boards, (de-)anonymization, online tracking, algorithmic fairness
Week 10: Project Presentations
Assignments
Unless otherwise stated, assignments are to be done individually.
You are welcome to work with others to master the principles and approaches used to
solve the homework problems, but the work you turn in should be your own.
Under no circumstance should you seek out or look at solutions to assignments given in previous years.
Late homework will not be accepted, but your lowest homework grade will be dropped.
Assignment 0:
Due Date: Thursday, January 10, 3pm PT
In preparation for the first discussion section,
install RStudio
(which in turn requires installing R).
Please also sign up for Piazza.
Assignment 1:
Assignment 2:
Due Date: Thursday, January 24, 11:59 pm PT
(
solutions)
Statistical estimators and confidence intervals.
Details
here.
Assignment 3:
Due Date: Tuesday, February 5, 11:59 pm PT
(
solutions)
The bootstrap, MLEs, and the method of moments.
Details
here.
Assignment 4: Project proposal
Due Date: Thursday, February 7, 11:59 pm PT
In teams of 2-5 people, please complete this
project proposal form.
You are free to pursue any topic related to applied statistics.
In previous years, teams have considered
athletic performance, gender inequality, farming practices, restaurant quality,
music success, gentrification, and standardized testing, just to name a few.
Any data-driven investigation is fair game.
At the end of the quarter, each team will prepare a written report
(approximately 10 single-spaced pages in length) detailing their work,
and will give a short in-class presentation.
To help assess the feasibility and suitability of your project,
please discuss your idea with Sharad before submitting your proposal.
Your group should
sign up for a 15-minute meeting slot. It is ideal if all members of your group can attend the meeting, but, at a minimum, two members should be present.
Upon completing the project, we'll ask each student to evaluate the contributions of their team members, and
we'll consider these peer-reviews when determining final grades.
Assignment 5:
Due Date: Thursday, February 14, 11:59 pm PT
(
solutions)
Linear Regression.
Details
here.
Assignment 6:
Due Date: Thursday, February 21, 11:59 pm PT
(
solutions)
Logistic regression.
Details
here.
Assignment 7:
Due Date: Thursday, February 28, 11:59 pm PT
(
solutions)
Bias-variance trade-offs, cross-validation, and regularization.
Details
here.
Assignment 8:
Due Date: March 7, 11:59 pm PT
(
solutions)
Causal inference.
Details
here.
Final Project
Presentation slides (in PDF format) due on Monday, March 11, 11:59 pm PT.
In-class presentations on Tuesday, March 12 and Thursday, March 14
(
sign up).
Paper (in PDF format) due on Wednesday, March 20, 11:59 pm PT.
Please submit your slides and paper on Canvas.
Peer evaluations
due on Wednesday, March 20, 11:59 pm PT.
In-class presentations are limited to 5 minutes, with an additional 1 minute for questions.
Your final paper should clearly state and motivate your research question, summarize
the related literature, describe your methods, detail your results
(and include the appropriate plots), and discuss the implications
of your findings. The paper should be approximately 10 single-spaced pages long.
Please include a title and abstract (up to 100 words);
your title and abstract may be distributed to future classes to help them generate
ideas for their own projects.
Final Exam
Sample questions
Monday, March 18, 12:15 - 3:15 pm
Last names starting with A-M: 420-041 (Jordan Hall)
Last names starting with N-Z: 380-380W (Math corner)
The final exam is open book/computer.
It consists of 10 true/false and 30 multiple-choice questions that
test the conceptual ideas presented in class.
Lectures
Lecture 1: Data exploration
Lecture 2: Visualization
Lecture 3: Intro to statistical inference
Lecture 4: Confidence intervals
Week 2 discussion section: Estimators
Lecture 5: The bootstrap
Lecture 6: Parametric inference
Week 3 discussion section: The bootstrap
Lecture 7: Correlation & regression
Lecture 8: Simple linear regression
Week 4 discussion section: MLE and method of
moments
Lecture 9: Uncertainty in regression
Lecture 10: Model evaluation & feature generation
Week 5 discussion section: Linear regression
Lecture 11: Logistic regression
Lecture 12: Multinomial logistic regression
Week 6 discussion section: Logistic regression
Lecture 13: Bias-variance tradeoff
Lecture 14: Regularization
Week 7 discussion section: Regularization
Lecture 15: Intro to causal inference
Lecture 16: Causal inference with observational data
Lecture 17: Ethics and privacy in data science
Lecture 18: Algorithmic fairness