You must enroll in the lecture, one discussion section, and one lab. See the Academic Guide for details. You can attend only the discussion section and lab in which you are enrolled.

**Students interested in Fall 2018 enrollment, please see the About page for Fall prerequisites.**

- Data 8 (Stat/CS/Info C8). The basics of probability are in Sections 8.3 through 9.3 as well as Chapters 12 and 17 of the Fall 2017 version of the Data 8 textbook. We’ll also touch on all of the inference covered in Data 8, in one way or another. You should remind yourself of Python and the datascience library.

In Spring 2018 we will accept the following alternatives to Data 8, consistent with the Data 8 “grandfathering” alternatives proposed during the transition towards a new Data Science major. - DS 100 (Stat/CS C100), or - Stat 133 and CS 61A, or - Stat 20 (or 21) and CS 61A

- A year of calculus at the level of Math 1A-1B or higher. Math 53 is ideal; you can take it simultaneously with Prob 140 but you should have taken 1A-1B earlier. We will post examples for you to work on before the start of term. As students have noticed, what’s required is a kind of mathematical maturity rather than knowing lots of computational formulas. You will rarely have to work out complicated integrals or derivatives by hand, but you will constantly work with abstraction – functions, domains, ranges, inverses, limits, and so on – as well as bounds, approximations, orders of magnitude, and so on.
- Linear algebra, as in Math 54, EE 16A, or Math 110, or an equivalent linear algebra course taken at another college. This requirement can be fulfilled concurrently with Prob 140.

Enrollment is restricted to undergraduates, and all of the prerequisites will be enforced. If you have taken more advanced courses but not the ones above, please take a more advanced probability course or study Prob 140 materials independently on the course website.

*Probability for Data Science*by Ani Adhikari and Jim Pitman. This will be available on the course website.- Probability by Jim Pitman, published by Springer NY. Available for Berkeley students on SpringerLink at no cost or low cost (for a printed version).
*Theory Meets Data*by Ani Adhikari (Dibya Ghosh, Editor), with contributions from students in the pilot offering of the Data 8 connector Stat 88, Probability and Mathematical Statistics in Data Science. The PDF will be available on the course website.

- Introduction to Probability by Joe Blitzstein and Jessica Hwang
- Introduction to Probability by Dimitri Bertsekas and John Tsitsiklis

Participate in lectures and discussion sections. We try to give useful lectures and select useful exercises for the discussion sections. Discussion section content is designed to provide practice relevant to homework, lab, and exams. Don’t miss out.

Each lecture contains a lot of detailed material. It’s not reasonable to expect that you will simply remember it all when you start doing your homework. The assignments are created under the assumption that you will have read the text (yes, really) and done many of the practice problems *before* you attempt the assignments. This is standard in upper division math and stat classes.

You will be able to follow the text much faster if you have attended lecture. You will be able to do your homework much faster and more independently if you have done the preparatory work beforehand.

To help you select what to read and practice, there will be detailed weekly study guides.

The material in Prob 140 builds on itself week after week. Work regularly so that you don’t fall behind; don’t expect to do well by cramming right before tests. If there is something you don’t understand, make use of staff office hours. We’ll be there to help.

Students have found these methods to be useful. For their advice, see the About page.

**Weekly homework**which you will turn in on Gradescope. Some of the homework will be done in Jupyter Notebooks. Homework will be posted on Wednesday evening and will be due by 8 p.m. on Tuesday of the following week. In some weeks there may be deviations from this due to exams or holidays; we’ll let you know.**Weekly lab**typically involving both math and computing. Labs will be graded for correctness as well as completion. After the first week, labs will be posted on Wednesday evening and will be due by 8 p.m. on Friday of the same week. How long it will take you to complete each lab will depend on your fluency with the material. Part of discussion section content will be pertinent to lab; we recommend that you at least familiarize yourself with the lab after attending section on Thursday, and preferably get started on the work. Lab sessions are on Fridays in Room 330 Evans. You may attend only the session in which you are enrolled.**Quizzes**four times during the term,**in the lab**in which you are enrolled. No computers involved.- Quiz 1: Friday 2/2 (Week 3)
- Quiz 2: Friday 2/16 (Week 5)
- Quiz 3: Friday 3/16 (Week 9)
- Quiz 4: Friday 4/13 (Week 12)

**Midterm in class on Wednesday February 28**. No substitutes except as required by university rules. No computers involved.**Final Exam on Friday May 11, 3 p.m. to 6 p.m., Exam Group 19**. Room to be announced. No substitutes except as required by university rules. No computers involved.**The final is required for a passing grade. Please make sure that you are not enrolled in a class that has a conflicting final exam.**

Data science is not a solitary activity; please expect to participate in lectures, discussion section, and lab. Lectures will not be webcast. The online text will contain what is covered, but it might have different examples. And of course it will not contain the discussions generated by questions asked in class.

In the calculation of your overall score, we will drop

- your two lowest homework scores
- your two lowest lab scores
- your lowest quiz score

Course grades will be assigned using the following weighted components:

- Homework 15%
- Labs 20%
- Quizzes 15%
- Midterm 20%
- Final 30%

You are encouraged to discuss practice problems, homework, and labs 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 your all own assignments and code.

Copying assignments from one another 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 Prob 140 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.

Prob 140 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,–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 Prob 140. 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.