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. Note that portions of the discussion sections will be devoted to the weekly labs.

You may also enroll in an optional Supplementary Section which will be held late on Friday afternoons, room and time TBA. This is for additional support with the weekly lab.

Please see the Berkeley Academic Guide or the About page for details, and note that enrollment is restricted to undergraduates.

- We will revisit all of the inference covered in Data 8 (Stat/CS/Info C8). The basics of probability are in Section 9.5 as well as Chapters 14 and 18 of the Data 8 textbook. You should also remind yourself of Python and the datascience library.

It is your responsibility to familiarize yourself with the content of Data 8.

- 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. As students have noticed, what’s required is a general mathematical fluency rather than knowing lots of computational formulas. You will rarely have to work out complicated integrals or derivatives by hand, but you will constantly (and swiftly) have to 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, Stat 89A, or Math 110, or an equivalent linear algebra course taken at another college. This requirement can be fulfilled concurrently with Prob 140.

- Probability for Data Science by Ani Adhikari and Jim Pitman.
- 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.

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

Prob 140 is not designed for remote learning. Nor does it contain a lot of routine plug-and-chug. Please attend lectures and discussion sections because those always include conversations about problem solving. We aim to give useful lectures and select useful exercises for the sections. 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 is not reasonable for you 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 review 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 assignments much faster and more independently if you have done the preparatory work beforehand.

To help you select what to read and practice, we will provide detailed weekly preparation 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 Piazza or staff office hours immediately. We’ll be there to help.

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

**Weekly homework**, typically involving both math and computing, which you will do in Jupyter notebooks and on paper and then turn in on Gradescope. Homework will be posted each Tuesday and will be**due by noon 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. 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.**Weekly lab**, typically involving both math and computing, which you will also do in Juypter notebooks and on paper and then turn in on Gradescope. Labs will be posted each Tuesday and will be**due by noon on Saturday**of the same week. How long it will take you to complete each lab will depend on your fluency with both the mathematical and computational aspects of the content. Part of discussion section content will be pertinent to lab. We recommend that you familiarize yourself with the lab at the latest after attending section on Wednesday and preferably earlier in the week. Labs will be graded for correctness as well as completion.**Quizzes**four times during the term,**in the discussion section in which you are enrolled**. No computers involved. Dates will be posted before the first day of class.- Quiz 1: Wednesday 2/6
- Quiz 2: Wednesday 3/20
- Quiz 3: Wednesday 4/10
- Quiz 4: Wednesday 4/24

**Midterm in class on Thursday February 28**. No substitutes except as required by university rules. No computers involved.**Final Exam on Friday May 17, 11:30 a.m. to 2:30 p.m., Exam Group 18**. Room to be announced. No substitutes except as required by university rules. No computers involved, though you will have to read code.**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 10%
- Labs 20%
- Quizzes 15%
- Midterm 22%
- Final 33%

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 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 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.