DSC95

About This Course

Welcome to DSC95 at UCSD! This class is titled "Tutor Apprenticeship in Data Science", and is a 2-unit, P/NP discussion-based course that is required of all first-time DSC tutors.

The class meets Wednesdays only (ignore the official schedule), from 8:00-8:50 AM (I'm sorry) in Warren Lecture Hall 2207.

This class is designed to guide new DSC tutors through their first quarter as a tutor.  Specifically, the goals are:

• thinking carefully about your teaching,

• considering different scenarios that might arise (and how to deal with them), and 

• genuinely enjoy teaching!

Note that the class is not lecture based. We tell our students that the best way to learn concepts in data science is by doing data science, whether that’s actually writing pandas code or practicing runtime analysis problems. Likewise, the best way to learn how to teach data science is to actually teach data science, which you will get practice within this class. 

Links

• Slack (use this for all communication)

• Gradescope

• Schedule

People

Grading

DSC 95 is graded P/NP. My goal is to have everyone pass, but to make that happen there are three things you need to do:

1. Attend and participate in class

2. Complete weekly readings and homework assignments

3. Complete a satisfactory final project

Attend and participate in class

This is a discussion-based class, so your attendance is critical both for your learning and for helping your peers learn from you!

I hope that you are able to attend all ten class meetings, but I understand that life can get in the way.  If you are sick or have an unavoidable conflict let Colin know via Slack before the class. Absence without notification makes me grumpy. Please don't come to class sick!

Complete weekly readings and homework assignments

Each week, we will provide you with readings and tasks to complete that should help you reflect on your time as a tutor so far. These will all be posted in the above Schedule, and are usually due on Sundays at 11:59PM. Since the readings and homeworks are short (~1 hour per week), there are no slip days or extensions.

Homework is graded on a 2/1/0 scale

• 2: Thoughtful and complete

• 1: Lack of effort

• 0: Not submitted

Complete a satisfactory final project

The Final Project in DSC 95 is an open-ended assignment where you’ll produce some sort of educational material (e.g. a video, diagram, or website) that will benefit students in the course you’re tutoring.

Not only will your Final Project be given to your class’ instructor, but it’ll also be part of the “DSC 95 Project Showcase” for future DSC 95 students to see!

More details to come.

Getting Paid

To get paid for your tutoring hours, submit your hours biweekly into Ecotime.

• Resources for using Ecotime can be found here; a quick guide (that is also linked on this site) can be found here.

• Instructions for setting up Direct Deposit can be found here. Note that you must set up your UC Path account to access the Direct Deposit page. Follow this guide for accessing UC Path for the first time.

• The payroll calendar can be found here; it describes when timesheets are due and when you’ll be paid.

Academic Integrity

For this class, the key to academic integrity is accurately representing the status and authorship of your work. I strongly encourage you to read the official UCSD policy on integrity of scholarship.

Inclusion

I am committed to an inclusive learning environment that respects our diversity of perspectives, experiences and identities. You, as a student in this course, are also responsible for maintaining an environment where your fellow students feel safe and respected.

In my opinion, the key to this is recognizing the inherent worth and dignity of every person. If there is a way you could feel more included please let me know in whatever way you feel most comfortable.

Acknowledgements

DSC 95 was designed and run by Marina Langlois for many quarters, so much of what you see here was originally developed by her. 

Other contributors include Suraj Rampure, Mia Minnes, and Victor Huang at Berkeley.