Probability and Mathematical Statistics in Data Science
Spring 2020 - Adam Lucas
Probability and Mathematical Statistics in Data Science
Spring 2020 - Adam Lucas
In this connector course we will state precisely and prove results discovered while exploring data in Data 8. Topics include: probability, conditioning, and independence; random variables; distributions and joint distributions; expectation, variance, tail bounds; Central Limit Theorem; symmetries in random permutations; prior and posterior distributions;probabilistic models; bias-variance tradeoff; testing hypotheses; correlation and the regression model.
You must enroll in the lecture and in a discussion section that meets twice weekly. See the Academic Guide for details. You can attend only the discussion section in which you are enrolled on CalCentral. Note that quizzes will be held in discussion sections.
The course textbook will be available on the course website. Lectures and the textbook will be closely related in content and sequence, though examples done in lecture might be different from those in the text.
Weekly Homework which will be done on paper and turned into the course Grade scope. Homework will be posted each Monday evening and will be due the following Monday at 10pm. In some weeks (such as Week 5) there may be deviations from this due to exams or holidays; well let you know. Homework is graded based on correctness. As there is plenty of support available and you have a week to do the work, we expect that you will get the problems pretty much wholly right.
Midterm on Friday March 6 2020 during the lecture hour. No substitutes except as required by university rules.
Data science is not a solitary activity; please expect to participate in lectures and discussion section. Lectures will not be webcast. The online text will contain the main points covered.
This class has a typical statisitics course curve with 30% of the class getting some kind of A and 30% of the class getting some kind of B and 30% some kind of C.
There will be a student run Piazza site for this class.
In the calculation of your overall score, we will drop
Course grades will be assigned by taking the maximum of:
Late submission of homework will not be accepted under any circumstances, unless you have relevant university accommodations. If you have such accommodations, please provide the formal notification to your lab GSI before the end of the second week of classes. There will be no alternate due dates for assignments missed due to illness, other commitments, and so on. The drops are intended to cover those situations.
You are encouraged to discuss practice problems and homework with your fellow students and with course staff. Arguing with friends about exercises is an excellent and time-honored way to learn. However, you must write up all your own assignments.
Copying assignments from others is not only dishonest, it also doesn’t help anyone. Each exercise requires its own combination of ideas, and each student needs practice in coming up with those combinations, or else they will be at a loss when trying to use probability theory in their future work. From a purely practical perspective, all students must work independently on Stat 88 quizzes and exams – no collaboration allowed. If a test is the first time a student works independently, then the test is not likely to go well.
Stat 88 materials including exams and solutions are the intellectual property of the course developers. From the campus statement on Academic Integrity: “… students may not circulate or post materials (handouts, exams, syllabi, i.e. any class materials) from their classes without the written permission of the instructor.”
I am extremely tough with dishonest students and I hope that I will not be put in that situation in Stat 88 I expect that you will work with integrity and with respect for other members of the class, just as the course staff will work with integrity and with respect for you.