pRINCIPLES OF DATA SCIENCE
ABOUT THIS COURSE
Welcome to DSC 10 at UC San Diego! This course aims to teach you how to draw conclusions about data. We will learn how to explore data and make predictions about data. Programming is a useful tool to help us analyze large data sets, and so we will learn how to program in Python towards this goal. We will learn some of the core techniques of data science and we will practice applying them to real data sets from a variety of different disciplines.
Prerequisites: None. This course is an introduction to data science with no prior background assumed beyond high school algebra. If you have taken both a statistics class and a programming class, you should take a more advanced course.
COURSE TIME & LOCATION
A00: Tuesday/Thursday, 2-3:20pm, Solis 104
B00: Tuesday/Thursday, 3:30-4:50pm, Solis 104
A01: Wednesday, 3-3:50pm, York 2662
B01: Wednesday, 4-4:50pm, Solis 104
Lab: This class has a lab component that appears in the schedule of classes (Mondays, 12-2:50pm or 3-5:50pm), however we will not be using this block of time. You are free to complete your lab assignments whenever is convenient for you.
Midterm Exam: Thursday, May 2, during lecture
Final Exam: Saturday, June 8, 3-6pm, location to be determined
Instructor: Janine Tiefenbruck
Teaching Assistant: Kyle Vigil
Tutors: Jared Andrews, Joshua Chan, Michelle Tf Chang, Shicheng Fan, Bo Hu, Chunan Huang, Yang Li, Mengyi Liu, Hanbyul Ryu, Amanda Shu, Yilin Xie, Michelle Yang, Weijia Zeng
Contact Information: For questions about the content of the course, please use Piazza.
Office Hours: See Staff Hours.
ASSESSMENTS AND GRADES
Your mastery of class material will be assessed in the following ways, and final grades will be computed as follows:
5% Class Participation
25% Homework Assignments
15% Labs (best 8 out of 10)
10% Midterm Exam
30% Final Exam
During class, you will work through problems and answer questions using iClickers. You will need an iClicker version 2 remote, available at the UCSD bookstore (you will not be able to use iterWrite, HITT brand, the smartphone clicker app, or anything other than the genuine iClicker2 remote).
Once you purchase an iClicker remote, you must register it online in TritonEd in order to get credit for your responses. You only need to do this once at the beginning of the quarter.
Participation scores will be posted periodically to TritonEd. You must resolve all iClicker registration issues by the end of Week 1. Failure to ensure that you are getting your participation credit before then will result in a 0 for the days that you have not received credit.
Participation points will be recorded starting on the second day of class. You will receive credit for the day if you answer at least half of the questions asked that day. The correctness of your responses does not affect your participation score. Forgetting your clicker counts as missing a class.
Participation points may not be made up, but some can be dropped. Say there are X class sessions, each with one participation point:
- Your first 4 missed classes will be dropped (i.e. your participation score is out of X-4 points).
- If you earn 93% or above on the midterm, an additional 6 missed classes will be dropped.
HOMEWORK ASSIGNMENTS AND PROJECTS
There will be weekly homework assignments and two projects, all of which will be programming assignments to reinforce concepts from class, explore new ideas, and provide hands-on experience working with data . You may work on homework assignments and projects either alone or with a partner, using pair programming.
You are encouraged to do your programming assignments in CSE B260, which is in the basement of the CSE building. The lab will be staffed with tutors who are there to help you during scheduled hours. Make sure to check the calendar before coming to the lab. However, if the lab is full, you may work in any of B220-B260. If you need tutor help, just put your name on the list (using Autograder) and a tutor will help you as soon as they are available.
Deadlines and Late Submissions:
Homework assignments and projects must be submitted by the 11:59pm deadline to be considered on time. See the schedule for due dates. You may turn them in as many times as you like before the deadline, and only the most recent submission will be graded, so it's a good habit to submit early and often. To submit homework assignments and projects, you must do two things:
- Submit your code to OK by running the cell
_ = ok.submit().
- Submit a PDF of your code to Gradescope by downloading a PDF from Jupyterhub (File -> Download As -> PDF) and then uploading this PDF to Gradescope.
Both of these parts must be completed by the 11:59pm deadline to be considered on time.
You have three slip days to use at your discretion on any three homework or project assignments throughout the quarter. Slip days allow you to turn in an assignment up to 24 hours after the deadline, subject to the following rules:
- You may use at most ONE slip day on any single homework assignment or project.
- If you are working with a partner using pair programming, you may use a slip day if both partners have a slip day remaining, and you will both be charged a slip day.
- You do not need to ask to use your slip days. Any submission turned in after the deadline and before 24 hours after the deadline will be charged a slip day automatically.
- Slip days cannot be used on labs.
- You will be charged a slip day even if your assignment is submitted just one minute after the deadline.
- If you submit after the deadline and decide you don't actually want to use up a slip day, you can go back and flag an earlier submission for grading in OKPY.
- Slip days make extensions irrelevant. These are your extensions - use them when you need them.
- Assignments submitted after the 24 hour slip day extension, or after the deadline if you are out of slip days will not receive credit.
- It is your responsibility to keep track of how many slip days you have remaining.
Weekly labs are a required part of the course and will help you develop fluency in Python and working with data. The labs are designed to help you build the skills you need to complete homework assignments and projects. To submit a lab, you only need to submit your code to OK by running the cell
_ = ok.submit() .
This class has a lab component that appears in the schedule of classes (Mondays, 12-2:50pm or 3-5:50pm), however we will not be using this block of time. You are free to complete your lab assignments whenever is convenient for you. Tutors will be available to help you in CSE B260 during tutor hours. Each person must submit each lab independently, but you are welcome to collaborate with any number of other students in lab. If you choose to complete a lab assignment remotely, without physically attending CSE B260, you may do so, but you must work completely on your own. Therefore, lab attendance is encouraged but not required.
Deadlines and Late Submissions:
Labs must be submitted by the 11:59pm deadline to be considered on time. See the schedule for due dates. You may turn them in as many times as you like before the deadline, and only the most recent submission will be graded, so it's a good habit to submit early and often. Late lab submissions will not be accepted. We will drop your two lowest lab scores to account for this.
There is one midterm exam, taken during lecture, and one final exam. Exams are on paper, and they are closed book and closed notes, but you will be provided with a reference sheet for each exam. Exams must be taken during the scheduled time, and you must attend the lecture section in which you are enrolled.
If you have a conflicting final exam (scheduled at the exact same time), or if you have three or more exams in one day, please see your instructor by the end of Week 1 to see if alternate arrangements can be made. Outside of this, no makeup exams will be given.
- You must score at least 55% on the final exam to pass the course. If you score lower than 55% on the final, you will receive an F in the course, regardless of your overall average.
- You have one week from the time an assignment or exam is graded to request a regrade. After that, the grade is set in stone.
- I will use a standard scale for assigning letter grades: 90-100 = some kind of A; 80-89.9= some kind of B, 70-79.9= some kind of C, 60-69.9=D, <60=F. Plus and minus cutoffs will be determined at the instructor’s discretion.
We expect that students in this class will have a wide range of backgrounds and relevant experience. If you find that the class is moving fast, and especially if you are new to programming, you will benefit from taking advantage of the opportunity to attend discussion section and catch up on the material that goes by too fast. Even if you are following along well in class, discussion section allows you the opportunity to practice the skills learned in lecture and develop your expertise. Discussion sections are purely for your benefit, and do not impact your course grade.
DIVERSITY AND INCLUSION
I am committed to an inclusive learning environment that respects our diversity of perspectives, experiences, and identities. My goal is to create a diverse and inclusive learning environment where all students feel comfortable and can thrive. If you have any suggestions as to how I could create a more inclusive setting, please let me know. We also expect that you, as a student in this course, will honor and respect your classmates, abiding by the UCSD Principles of Community Please understand that others’ backgrounds, perspectives and experiences may be different than your own, and help us to build an environment where everyone is respected and feels comfortable.
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 email@example.com and is located in Atkinson Hall #2010.
COLLABORATION POLICY AND ACADEMIC INTEGRITY
The basic rule for DSC 10 is: Work hard. Make use of the expertise of the staff to learn what you need to know to really do well in the course. Act with integrity, and don't cheat.
If you do cheat, we will enforce the UCSD Policy on Integrity of Scholarship. This means: You will fail the course, no matter how small the affected assignment, and the Dean of your college will put you on probation or suspend or dismiss you from UCSD.
Students agree that by taking this course, their assignments will be submitted to third party software to help detect plagiarism.
Why is academic integrity important?
Academic integrity is an issue that is pertinent to all students on campus. When students act unethically by copying someone’s work, taking an exam for someone else, plagiarizing, etc., these students are misrepresenting their academic abilities. This makes it impossible for instructors to give grades (and for the University to give degrees) that reflect student knowledge. This devalues the worth of a UCSD degree for all students, making it imperative for the the campus as a whole to enforce that all members of this community are honest and ethical. We want your degree to be meaningful and we want you to be proud to call yourself a graduate of UCSD!
The UCSD Policy on Integrity of Scholarship and this syllabus list some of the standards by which you are expected to complete your academic work, but your good ethical judgment (or asking us for advice) is also expected as we cannot list every behavior that is unethical or not in the spirit of academic integrity. Ignorance of the rules will not excuse you from any violations.
What counts as cheating?
In DSC 10, you can read books, surf the web, talk to your friends and the DSC 10 staff to get help understanding the concepts you need to know to complete your assignments. However, all code must be written by you, together with your partner if you choose to have one, where allowed.
The following activities are considered cheating and are not allowed in DSC 10 (This is not an exhaustive list):
- Using or submitting code acquired from other students (except your partner, where allowed), the web, or any other resource not officially sanctioned by this course
- Having any other student complete any part of your assignment on your behalf
- Acquiring exam questions or answers prior to taking an exam
- Completing an assignment on behalf of someone else
- Using someone else's clicker for them to earn them credit or giving your clicker to someone else so that they can participate for you to earn credit
- Providing code, exam questions, or solutions to any other student in the course
- Using any external resource on closed-book exams
The following activities are examples of appropriate collaboration and are allowed in DSC 10:
- Discussing the general approach to solving homework problems or projects
- Talking about debugging strategies or debugging issues you ran into and how you solved them
- Discussing the answers to exams with other students who have already taken the exam after the exam is complete
- Using code provided in class, by the textbook or any other assigned reading or video, with attribution
- Google searching for documentation on Python
How can I be sure that my actions are NOT considered cheating?
To ensure that you don't encounter any problems, here are some suggestions for completing your work.
- Don't look at or discuss the details of another student's code for an assignment you are working on, and don't let another student look at your code.
- Don't start with someone else's code and make changes to it, or in any way share code with other students.
- If you are talking to another student about an assignment, don't take notes, and wait an hour afterward before you write any code.
Note: in the discussion above, we are talking about other students that are not your pair programming partner. See the pair programming guidelines for information on working with a partner.
Remember, Academic Integrity is about doing your part to act with Honesty, Trust, Fairness, Respect, Responsibility and Courage.