Course: Math 152 (Course Catalog)
Title: Topics in Data Science
Credit Hours: 4 units
Prerequisite: Math 20D and Math 18 (or Math 20F). I highly recommend that students are familiar with probability theory, combinatorics, and linear algebra. The class will attempt to be self contained (but this is not always possible). Moreover, the class is theoretical, and is devoted to ideas, algorithms, and proofs. Students who are interested in explicit data science applications should not register.
Catalog Description: We will cover among other topics (tentative): sampling, finding frequent items, counting distinct elements, general frequency moment estimation, dimensionality reduction, and matrix approximation.
Textbooks: There is no course textbook. We will primarily be following Edo Liberty’s course notes, and Jelani Nelson’s. Naoki Saito’s lecture notes is also good preparation for Linear Algebra. I will post a reference for each lecture.
- Data Stream Algorithms by Amit Chakrabarti. This course note can be downloaded here
- Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman. The book can be downloaded here.
- Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan. You can download it here
- Matrix Methods in Data Mining and Pattern Recognition by Lars Eldén. You can download it here if you are on campus
Related courses at other schools
- Streaming Algorithms: Justin Thaler (Georgetown University)
- Sketching Algorithms for Big Data: Jelani Nelson (Harvard University)
- Data Mining: Edo Liberty (Tel Aviv University)
- Mining Massive Data Sets: Jure Leskovec (Stanford)
Lecture: Attending the lecture is a fundamental part of the course; you are responsible for all material presented in the lecture whether or not it is discussed in the textbook. You should expect questions on the exams that will test your understanding of concepts discussed in the lecture.
Reading: Reading the sections of the textbook corresponding to the assigned homework exercises is considered part of the homework assignment; you are responsible for material in the assigned reading whether or not it is discussed in the lecture. It will be expected that you read the assigned material in advance of each lecture.
Homework: Homework will be assigned each week. You should solve these homework problems and discuss them with your TAs during discussion sessions.
Exams: There will be two midterm exams and a final exam. You may use one 8.5 x 11 inch page of handwritten notes. (Both sides are okay, but no photocopies are allowed.) You may not use any other notes or any electronic devices. Please bring your student ID to the exams. There will be no makeup exams.
Midterm Exams: There will be two midterm exams. The first midterm will be on January 30, and the second on February 27 in class. Most of the problems on these quizzes will be picked from the homework assignments.
Final Exam: It is your responsibility to ensure that you do not have a schedule conflict involving the final examination. You should not enroll in this class if you cannot sit for the final examination at its scheduled time. You must pass the final exam ( >59% ) in order to pass the class. (The actual required percentage may be lowered, depending on overall class performance.)
Regrade Policy: Your exams will be graded using Gradescope. You will be able to request a regrade directly from your TA for a specified window of time. Be sure to make your request within the specified window of time; no regrade requests will be accepted after the deadline.
Make-up Exams: Make-up exams will not be given.
Administrative Deadline: It is your responsibility to check that your quiz/exam scores on TritonEd are correct.
Contact your TA before the end of the 10th week of the quarter to resolve recording errors.
- Questions regarding missing or incorrectly recorded quiz/exam scores will not be considered after the last day of instruction.
- Be sure to check that your quiz/exam scores entered in TritonEd are the same as your exam scores published on Gradescope.
Grading: Your course grade will be determined by your cumulative average at the end of the term and will be based on the following scale:
I may adjust the scale to be more lenient, but guarantee that the grade corresponding to a given percentage will not be lower than specified by the above scale.
Your cumulative average will be the best of the following two weighted averages:
- 50% Best Midterm, 50% Final Exam
- 40% Best Midterm, 60% Final Exam
You must pass the final examination in order to pass the course.
There will be no curve, but we may adjust the scale to be more lenient (depending on the overall performance of the class).
Please notice that outside factors, including the need for a certain grade for admission/retention in any academic program, scholarship or transfer credit, graduation requirements or personal desire for a specific grade DO NOT appear in the above calculations, and thus are not considered in any way in the determination of your course grade. Effort, improvement, class attendance and participation will all dramatically improve your grade in the course in that they will allow you to do well on quizzes, exams, and the final exam. They will NOT, however, actively participate in the calculation of your course grade.
Academic Dishonesty: Academic dishonesty is considered a serious offense at UCSD. Students caught cheating will face an administrative sanction which may include suspension or expulsion from the university. Click here for more information.