DATASCI 112: Principles of Data Science

This is the archived website from Winter 2024.

This syllabus and everything else you need will be posted on the course website: datasci112.stanford.edu.

Learning Objectives

Acquire and process tabular, textual, hierarchical, and geospatial data.
Uncover patterns by summarizing and visualizing data.
Apply machine learning to answer real-world prediction problems.

Course Staff

We invite you to meet your classmates and work on labs at Data Science Night on Thursdays 6 - 9 PM in Sequoia 105 (Lounge)!

Class
Lecture 01 (Mon, Wed, Fri) 10:30 - 11:20 AM in 380-380Y Lecture 02 (Mon, Wed, Fri) 3:30 - 4:20 PM in 380-380X	Dennis Sun Wed, Fri 11:30 AM - 1 PM in Sequoia 124.
Section 03 (Tues, Thurs) 9:30 - 10:20 AM in 380-380W	Noah Cowan Mon 4:30 - 6 PM in Sequoia 220 (Fishbowl)	Serena Lee Thurs 1 - 2 PM in Sequoia 220 (Fishbowl)
Section 04 (Tues, Thurs) 10:30 - 11:20 AM in McCullough 122	Isaac Gibbs Fri 9:30 - 10:30 AM in Sequoia 220 (Fishbowl)	Jack Hlavka Thurs 7 - 8 PM in Sequoia 100 (Lounge)
Section 05 (Tues, Thurs) 10:30 - 11:20 AM in 160-322	Allison Xu Thurs 6 - 7 PM in Sequoia 100 (Lounge)	Julia Costacurta Thurs 2 - 3 PM in Sequoia 220 (Fishbowl)
Section 06 (Tues, Thurs) 3:30 - 4:20 PM in 160-314	Yash Nair Tues 8:30 - 9:30 PM on Zoom	Niall Kehoe Wed 2 - 3 PM in Sequoia 207 (Bowker Room)
Section 07 (Tues, Thurs) 4:30 - 5:20 PM in Thornton 210	Jack Krew Thurs 8 - 9 PM in Sequoia 100 (Lounge)	Colin McKhann Tues 2 - 3 PM in Sequoia 220 (Fishbowl)
Section 08 (Tues, Thurs) 5:30 - 6:20 PM in Thornton 210	Apratim Dey Fri 4:30 - 5:30 PM in Littlefield 107	Ricky Rojas Wed 4:30 - 5:30 PM in Sequoia 207 (Bowker Room)

Contact Outside Class and Office Hours

We prefer to talk to you in person, during class or office hours! But if you need to reach us outside of these times, there are several options:

If you have a question about class logistics or course material, please post it on the Ed Discussion forum so that everyone can benefit from your question.
If you have a private concern, please e-mail the staff list. Please use this list instead of our individual e-mail addresses for a timely response. You can expect a response within 1 business day.

Grading

Your final grade in the course will be determined from the following components.

Component	Weight
Participation Lecture attendance is expected (although not required). Feel free to attend either lecture, so if you miss the morning lecture, come to the afternoon one! Lectures are not recorded, but we will try to make the slides useful for self-study. Section attendance and participation is required. Do not take this class if you cannot commit to attending every section. If you cannot make it to your assigned section, but you can attend another section on the same day, please e-mail your TA and the TA whose section you plan to attend. You may do this 2x throughout the quarter. If you cannot make it to any sections that day, you may complete the section Colab on your own, and e-mail a PDF to your TA and CA before the following section. They will grade your Colab, and this score will replace your attendance for that day. You may do this 2x throughout the quarter. To allow for emergencies, we will also forgive 2 absences at the end of the quarter. We are effectively allowing you to miss / reschedule up to 6 sections out of 20. If you need to miss more than 6, then you will need to retake this course next quarter.	10%
Attend Data Science Talks You are required to attend two data science talks and submit one reflection synthesizing what you learned from the two talks.	5%
Labs (posted on the Schedule page) Each lab is a self-contained investigation of a data set. Each lab will be due on Gradescope at 8 AM. Late labs are not accepted under any circumstances. You will always have 1 week to complete every lab, so plan ahead. There will be an optional Lab 6, due in Week 10, that will replace your lowest score from Labs 1-5.	15%
Exams There will be two 80-minute midterms, scheduled for 1/31 and 2/28, 6 - 7:20 PM in 420-041. Check your calendars now, and double check sure that you can make both midterms. Take this class in another quarter if you might miss either midterm. We do not offer makeup exams, and you will have to complete the course next quarter if you miss a midterm.	35%
Final Project	35%
*Total*	100%

Regrade Policy

You may submit regrade requests for labs directly on Gradescope.

For exams, if you believe that we have made a mistake in grading, please fill out this form within 1 week of getting the exam back, and hand your graded exam to Professor Sun. Note that Professor Sun will regrade your entire assignment, so your grade could go up or down.

Letter Grades

We only assign a letter grade at the end of the quarter; we do not curve or assign letter grades to individual assignments.

When assigning final letter grades, we will ensure that the median grade among freshmen and sophomores who tried their best is at least a B+.

What does "tried their best" mean? Attending class regularly and submitting good-faith attempts on all assignments on time.

Why do we curve the class based on freshmen and sophomores? To make the class more accessible to students with less background. This way, a freshman or sophomore is not penalized if there are a lot of upperclassmen or graduate students (who often have more background) in the class.

How are upperclassmen and graduate students graded? Once we decide the letter grade cutoffs based on freshmen and sophomores, we apply the same cutoffs to upperclassmen and graduate students. So everyone is graded on the same standard.