**Ani Adhikari**

Welcome to Data 140! This page contains the nitty-gritty details of the class. Please read it carefully, and also read the companion page about the class.

Enrolled and waitlisted students have been added to our **Ed** forum.

Data 140 is an academic community; Ed is a formal, academic space. We must demonstrate appropriate respect, consideration, and compassion for others. Please be friendly and thoughtful; our community draws from a wide spectrum of valuable experiences. For further reading, please reference Berkeley’s Principles of Community and the Berkeley Campus Code of Student Conduct.

The textbook is *Probability for Data Science* by Ani Adhikari and Jim Pitman.

**Videos are embedded in the textbook**. For access, use your CalNet credentials to open YouTube. The videos consist of explanations of selected portions of the written text, typically those that require detailed calculation or discussion.

There’s more in the written text that you are expected to work through. In fact, you can learn the material by studying only the written text, as many students have done in the past.

Prof. Adhikari’s lectures are versions of the textbook content and videos. They will follow the textbook sections in sequence according to the calendar on the homepage. Students are expected to attend. Lectures are partly driven by conversation that arises from students’ questions and responses, and the examples used in lecture might be different from those in the textbook.

Lectures will not assume that you have already worked through the content of the lecture. However, many students have found it useful to at least skim the relevant sections before coming to lecture. The most common approaches among students who did well in past semesters:

- Go over the textbook and its videos, and then attend lectures. This helps reinforce ideas and works for many students.
- Attend lectures and then work through the textbook, using the videos only if needed to clear something up. This also works for many students.
- Attend lectures and work through the textbook carefully and regularly, skipping the embedded videos. This is an efficient method for students who can follow written math pretty fluently.
- Attend lectures sporadically or hardly at all, but work through the textbook carefully and regularly, using the embedded videos when necessary. This requires significant personal discipline and abstract math skills, and is the least common approach among students who did well.

Over the semester, all students become more fluent at math. You will learn best by working out the calculations yourself as you watch or read, and by trying the Quick Check questions and some of the chapter exercises. Though it might seem as though this will take too long, you will find that a good grasp of the weekly content will speed up your work on assignments.

*Probability*by Jim Pitman, published by Springer NY. Available for Berkeley students on SpringerLink at no cost or low cost (for a printed version) if you log on via CalNet.*Introduction to Probability*by Joe Blitzstein and Jessica Hwang*Introduction to Probability*by Dimitri Bertsekas and John Tsitsiklis*Theory Meets Data*, the Data 88S textbook written by Prof. Adhikari, covers some of the basic concepts of Data 140 at a more elementary level.

Data 140 is primarily a math class. Your main tools for working will be paper-and-pencil or a tablet equivalent.

The weekly Study Guide is intended to help you map out your work. It will provide you with an outline of the week’s main ideas, connections to the textbook, and a schedule that describes the focus of all the week’s sessions.

The week’s content, sessions, homework, and lab are carefully coordinated. **Each week’s assignments are based on the material of that week, not of the previous week.**

The Study Guide is designed to help you distribute your work effectively over the week. It is only a guide; you are not required to follow it. You are welcome to follow a different study schedule if that suits you better. But leaving all the work till the weekend is not a good idea.

As past students have advised in Study Tips, it is important to work regularly. This is not a bingeable class.

Attending all lectures and sections is an excellent way of working regularly. We don’t take attendance. Instead, we work hard to make each session effective for learning.

Please keep in mind that **each session assumes that you have attended the previous sessions.** For example, each discussion section assumes that you attended lecture on the previous day. GSIs will not re-teach what was covered in lecture.

A typical week includes the following sessions. All sessions are in person and start at “Berkeley time” following our cherished practice of starting 10 minutes after the listed time.

- Prof. Adhikari’s lectures: 12:30PM to 2PM on Tuesdays and Thursdays in 155 Dwinelle.
**You are expected to attend.** - Sections led by GSIs: twice weekly on Wednesdays and Fridays at various times, in person in Evans. These will be conversations about exercises and may sometimes include portions of the weekly lab. They will cover how to select appropriate methods, similarities and differences between exercises, etc.
**You are expected to attend your assigned discussion section**on Wednesdays and Fridays. You cannot attend other sections. - Assignment “parties” (work sessions with staff support) on Fridays, times TBA
- Office hours by the instructors and the course staff, days and times TBA.

A few weeks into the term, we will start the Supplemental Section, which will typically consist of “catch up” content to reinforce material you may have missed in the previous week. Details will be posted on Ed. Enrolling in this section is optional.

Student participation and informal conversation will be encouraged during all lectures and sections. You are expected to attend and participate. To help make this possible, the class allows no time conflicts.

There will be a homework assignment and a lab each week. Assignments will be released on Monday night and will be due by 11:59 p.m. on the following Monday. There might be some changes in release dates or due dates because of exams and holidays. We’ll let you know.

**You are allowed one lab partner from among students in the class.** You may have different partners for different labs. Logistical details will be posted on Ed.

All assignments will involve both math and computing. You will do them on paper and in Jupyter notebooks.

Assignments must be submitted on Gradescope. Please follow all submission instructions. Not doing so will result in no credit and no regrade request allowed for the work. It is your responsibility to make sure your submission is complete.

**Late assignments will not be accepted**. But if you have DSP accommodations for extended time on assignments, please make sure we have received your DSP accommodation letter. We will contact you about arrangements.

Assignments will be graded for correctness. There is plenty of support available while you work on them, so if you get started early and use the support then you should be able to turn in work that you understand well and know to be correct. That is by far the most efficient way to succeed in the class.

Please keep in mind that **homework assigned each week is based on the material covered in the same week, not in the previous week.** The first two exercises (and possibly more) of each week’s homework will be based on material covered in Tuesday’s lecture. So you will be able to start homework as early as Tuesday night.

All of these will be in person and proctored.

**Quizzes**three times during the term,**in the discussion section in which you are enrolled.**- Quiz 1: Wednesday 2/1
- Quiz 2: Wednesday 3/22
- Quiz 3: Wednesday 4/12

**Midterm**on Tuesday February 28 in lecture**Final Exam**on Thursday May 11, 3:00 PM to 6 PM, Exam Group 15, room TBA. It is your responsibility to make sure you are not enrolled in another class that has a conflicting final exam.

There will be **no alternate quizzes or exams** except as required by campus rules. If you have extended time accommodations for tests from DSP, please make sure that you have enough available hours around the times of the regularly scheduled tests. This is particularly important for the midterm and the final.

We strongly recommended that you turn in all assignments even if you can only complete some of them partially. However, to give you some leeway in case of illness and emergencies, we will drop the following in the calculation of your overall score:

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

Course grades will be based on your overall score in the class, calculated using the following weights:

- Homework 20%
- Labs 12%
- Quizzes 12%
- Midterm 20%
- Final 36%

Grades will be based on a combination of absolute cutoffs and the distribution of overall scores. Towards the end of the term, I will make three guarantees: “An overall score of at least *x* will result in a grade of at least C-; at least *y* will result in at least B-; at least *z* will result in at least A-“. The thresholds *x*, *y*, and *z* will depend on this term’s performance.

Last term’s grade distribution was unusual because of the P/NP option. In Spring 2022, my end-of-term Ed post included the following: “In the end, just under a third of the class got A-/A/A+ and just under 30% got B-/B/B+.”.

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 all your own assignments and code by yourself; for labs, this will be in collaboration with your lab partner if you have one.

Copying assignments from other sources 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 Data 140 exams – no collaboration allowed. If a test is the first time a student works independently, then the test is not likely to go well.

Data/Stat/Prob 140 materials including solutions and exams 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.”

We are tough with dishonest students and we hope that we will not be put in that situation in this class. We 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.