Teaching and Tutoring

My General Resources for Students

1. How to read an academic paper? Link to my notes here.

2. Worksheet on coming up with a good research question here.

Good Resources Written by Others

Here is a set of resources related either to learning particular skills or more broadly to the science and craft of learning overall. Some have benefited me personally. 

• Arthur Spirling's tips on how to be a grad student: https://github.com/ArthurSpirling/BeingAGradStudent

• Nick HK's videos on learning R, more or less from scratch: https://www.youtube.com/playlist?list=PLcTBLulJV_AIuXCxr__V8XAzWZosMQIfW. Pitched for economists, but remains super useful for folks interested in social science applicants generally--and even those that are interested in learning R specifically.

Current course(s):
On leave (AY 2024-25) from UCLA—no current teaching responsibilities.

Archived courses taught recently

• PS140: Congress (TA) [winter '23]

• PS40: Intro to American Politics (TA) [fall '23]

• PS40: Intro to American Politics (instructor of record) [summer session A '23]. This course will be taught to high school students as part of UCLA's Summer Institute. [syllabus]

• Cluster 60: Social Science in the 1960s: Learning by Doing (Teaching Fellow; instructor of record) [spring '23] [syllabus]

• Cluster 60B: America in the Sixties: Politics, Society, and Culture, 1954 to 1974 (Teaching Fellow) [winter '23]

• Cluster 60A: America in the Sixties: Politics, Society, and Culture, 1954 to 1974 (Teaching Fellow, or TF) [fall '22]

• Undergraduate Research Experience (URE) Seminar (full primary instructor/instructor of record) [fall'22]

• PS149: Local Politics and Policy (TA) [spring '22]

• PS6: Intro to Data Analysis (TA) [winter '22]

• PS40: Intro to American Politics (TA) [fall '21]

A BIT ABOUT ME:

I'm a data scientist working at PG&E and PhD candidate in political methodology and statistics at UCLA.

More importantly, I have been (1) a middle school tutor, (2) a high school tutor, (3) a peer tutor in undergrad at UC San Diego, (4) a teaching assistant (TA) and teaching fellow (TF) at UCLA, and (5) an instructor of record (basically, the main teacher) for coursework at UCLA. I’ve taught math and stats at nearly every academic level—including through in-person and digital/remote channels—and bring that wealth of teaching experience to my 1-1 tutoring work.

MY APPROACH:

I learned my most important data science skills through applied, hands-on training. I use that philosophy—that mastery, especially in data science, is acquired through practical experience and demonstration rather than through lecturing and textbook exams—in my teaching.

In our sessions, we won't be:

- spending a bunch of time reading complicated statistical theory or deriving proofs (I did this so you don’t have to)

- doing a bunch of math by hand/with pen and paper

- learning about outdated analysis techniques that are rapidly being replaced by new techniques

Instead, we will be:

- focusing on what’s important to you (no one comes in to data science with zero skills/knowledge)

- situating our work in a concrete and specific context that makes sense to YOU (it doesn’t matter if the textbook is interested in sports or the new or whatever, if you aren’t)

- using real data, computer programming/code, visualizations, and interactive tools as much as possible—to SHOW rather than tell you how data science works

We will spend the bulk (~80%) of our time choosing a project that interests you (or choosing from a sandbox example that I have come up with), identifying the key statistical/data science problems in that project, and solving those key problems with modern programming and analysis techniques (using R, Python, and a visualization tool of your choosing).

WHERE DOES THIS APPROACH COME FROM?

I honed my approach to teaching data science through two main experiences: teaching classes at UCLA and a decade of professional experience as a data scientist/analyst. 

I had the pleasure of TAing for some of UCLA's best and most innovative stats/data science professors where I worked with hundreds of students during my PhD and taught numerous iterations of very difficult courses. From intro level statistics to applied public policy analysis courses, my teaching experience taught me how to build student confidence and understanding gradually—as opposed to the ‘information dump’ approach—and to quickly sense students' preferred learning styles/approaches.

As an applied data scientist (both at PG&E and in previous roles), I constantly share and explain the work that I'm doing. Over the years, I have honed my ability to explain complicated analyses clearly and succinctly through dozens of projects involving hundreds (if not thousands) of different stakeholders, each of whom has their own unique background, approach, and line of questions.

WHO IS THIS FOR?

I genuinely believe that everyone can benefit from data science skills in today’s economy/information environment. 

For those interested in a data science career, I think this is obvious, but I also see DS skills as tremendously useful for those working in or considering marketing or business, those in or considering law or medicine (differential diagnosis, anyone?), people who want a better handle on their personal data/consumptive patterns (health data, spending data, etc.), engineers in basically any discipline, and virtually anyone else interested in going from being a passive consumer of information to actively participating in its interpretation and framing.

To give some specific examples of past clients and students:

- High school students (and some advanced middle school students) looking to get a head start on a highly useful skillset, and to put their best foot forward for college apps.

- College students looking to supplement their in-class instruction or develop a project to position themselves for a job/internship.

- Working professionals looking to beef up their existing skillset with deeper conceptual knowledge in statistics, enhanced programming skills, or guidance on an advanced project in a nuanced field (e.g., causal inference).

Do you need a letter of recommendation for a class, grant, or something else? I'm happy to write one, but a few things to keep in mind: 

(1) try to approach me during the class I'm TAing, (2) give me at least 3 weeks to work on the letter, and (3) try to come to an office hours or two so we can chat.

I'm also happy to chat with anyone interested in grad school about what that's like (research interests, application process, etc.). Or to chat about what it's like to work in the private sector/in industry, and how to weigh that against a graduate degree.

Below are links to forms that will take you to archived versions of courses I've taught in the past. If you're unsure of the password, send me an email.

PS149 (Local Politics and Policy). Taught Spring 2022 (Prof Dan Thompson, UCLA): LINK.