Welcome to DSC96 at UCSD! This class is titled "Workshop in Data Science", and is an optional, 2-unit project course in data science. It is meant to be taken concurrently with DSC10, and has no prerequisites.
This class is about using data to answer questions. This is in contrast with most of your other classes this year, which are about fundamentals and theoretical underpinnings. The questions you get to answer are the big important ones, including “What happened?”, “Why did it happen?”, and “What will happen?”. The data used to answer the questions will range from real-world government data to tweets about UC San Diego to sound recordings.
By the time you finish this class, you will be able to:
Identify problems that are good candidates for data science,
Re-frame the problem in a way that can be answered with the available data,
Evaluate the limitations and quirks of the data,
Manipulate the data to answer relevant questions, and
Communicate the results clearly.
Instructor: Colin Jemmott, cjemmott@ucsd.edu
The required book for this class is “Confident Data Skills” by Kirill Eremenko. The book is meant for a wide audience, and is inexpensive. We will only be reading chapters 1-5, after which the book goes into techniques that will be covered in more detail in your other classes.
Much of the readings for this course are taken from popular publications that do a good job of communicating arguments using data. Links are on the schedule page.
This class operates as a data science lab class, meaning that the bulk of time in class is spent doing data science. There are also some formal lectures, but most of the instruction is meant to help you understand the tasks and the context.
You should bring a laptop to every class. If you can't, please contact Colin and we will work something out.
Unless we get behind, there should not be much mandatory coding or analytics work outside of class (though you are encouraged to expand on the projects and see how much you can do!). There is significant reading outside of class, and it is important to keep up with the reading because of the limited formal instruction during class time.
Each time we meet there will be activities you need to complete before class. Typically that will be watching a lecture to prepare for our in-class activity, or doing some reading to prepare for a discussion. The schedule is linked at the top of this page.
Each week, before the start of the Wednesday class, you must email me a paragraph or two about the weekly reading. The goal of this is to have a more in-depth conversation than our class time allows. Think of these journals more as emails to discuss something you read with a colleague than as a formal essay.
The topic is up to you, but examples include:
Relating a topic in the reading to something in your life or in the news,
Asking a thoughtful question about the reading, or
Picking a quote from the reading that you agree or disagree with and explaining why.
I will read what you wrote and give a quick response (possibly up to a week later). I may bring up what you write to me in class unless you explicitly ask me in that email not to.
I understand that some of you will not attend a few or even all of the classes. That's ok! You don't need to let me know or get permission, though I am available if you want to talk.
If you can't make it to class, you should spend a couple of hours on the exercise, making a good effort.
Without the collaboration and explanations that happen in class, this will be much more difficult, so I recommend finding a buddy to work with. Important note: You do not need to completely finish the activities! Many of the in-class activities are designed to be much longer than can be completed in a reasonable time - I like to include the extra material in case someone wants to learn more.
In-class assignments from a missed class will not be accepted more than a week late unless you ask for and receive special permission. Only some assignments are collected - others will just be on the honor system for you to make a good effort at. I will note the ones that get turned in on the schedule.
The activities in this class are collaborative. You are encouraged to ask for help from the instructor or from other students. This means that for this class it is totally ok to show your work to other students and discuss it openly.
However, even in this collaborative environment, the work you do must be your own. Specifically, you must do the actual work of completing the assignment (i.e. typing out the code, moving the mouse) and understand what your code or analysis is doing.
Please ask for clarification if that distinction is not clear. This is important to me.
Below is a formal grading rubric, but here is the honest truth: this is an optional pass/no pass class. If you make a serious effort to do the class, you will pass. I am only going to really calculate grades if you really don't do the class. If you are planning on really not putting effort in, I would ask you to drop the class to make room for someone else.
Class discussion on reading = 20%
SDPD traffic stops project = 20%
Audio project = 30%
Image project = 30%
Final Grade
70%-100% = Pass
0%-69% = No Pass
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.
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 via email.
Students requesting accommodations for this course due to a disability or current functional limitation must provide a current Authorization for Accommodation (AFA) letter issued by the Office for Students with Disabilities (OSD), which is located in University Center 202 behind Center Hall. If you have an AFA letter, please make arrangements to meet with the instructor and with the Data Science OSD Liason by the end of Week 2 to ensure that reasonable accommodations for the quarter can be arranged. The Data Science OSD Liaison can be reached at dscstudent@ucsd.edu and is located in Atkinson Hall #2010.