# Study Tips from Staff and Students

#### Ani Adhikari

## Table of Contents

In Data 140 you will be learning probability based on your knowledge of math. Your work will consist mostly of math, with some computing to enhance your understanding of the math and also to point to interesting mathematical avenues to explore.

The programming prerequisite is Data 8, nothing more. The code will not be long or complicated. But you’ll have to figure out what to code and what its logic should be, based on the math.

**The key to success in Data 140 is logical clarity and math.** Students who have greater mathematical fluency have an easier time. They can focus more on the probabilistic concepts because the math and hence the course takes them less time.

Students don’t always know what faculty mean by terms like “mathematical maturity” or “math fluency”. I’ve tried to explain what I mean by math fluency in Data 140 and Stat 134, the upper division probability courses that I have taught, here. You’ll see that it’s less about the set of results that you know and more about how you understand what you do know and how you approach math.

**You don’t need math beyond what’s listed in the requirements. But you do need to be confident with all those requirements so you can use them at speed and follow the textbook.** Or you should be prepared to spend time learning math while you learn the probability. Many students do the latter but it’s very hard work.

### Math Background

If you’re worried that you don’t have the necessary fluency, the Math Prerequisites page includes the math needed for different parts of the course, some concise reference materials, and some exercises for you to try.

Student experience is unsurprising – if you take the page seriously and study the prereqs before the sections in which they are needed, it will be easier for you to pick up the new probabilistic concepts and do your assignments.

For many students, Data 140 is their first time doing math at the upper division level. There’s a learning curve that can be quite steep at the start, especially because the course involves both math and computing. The staff and student tips below will help.

### Tips from Students

Each term, the official course evaluation form ends with the question, “What advice would you give to another student who is considering taking this course?” Student responses tend to be consistent across terms: it’s a tough course (for some, too demanding), but if you put in consistent effort you will improve and succeed.

This response from Fall 2020 is long but it summarizes the vast majority of student opinion.

To quote a review I read for CS61B that applies here as well: “once the bleeding stops, you’ll realize you learned something.” The course demands a lot of time and consistent effort on the part of the student and moves at an aggressive pace, but it is incredibly well–organized with plenty of resources to help students succeed. The textbook and lectures were highly effective in conveying the concepts, and discussions covered very relevant material and served as bridges between seeing the material in lecture and applying it to the homework. Office hours were an indispensable resource as well. It was a very painful class, but that pain is directly proportional to how consequential the material is to Data Science and how much the student gets out of it. It is well worth the investment, but only if you put the work in.

Students repeatedly advise reading the textbook. From Spring 2021:

Stay on top of assignments, go to lecture, read the textbook.

Be prepared to spend a lot of time reading the textbook and getting in-depth on course concepts to truly master the material succeed in this class.

I think reading the textbook and watching the textbook videos really helps you understand the material.

Excellent practical advice from a Spring 2021 student:

- Do not fall behind. Everything builds upon another. There is no point in asking “is midterm 2 cumulative?” because later topics builds on the foundations from starting topics.
- Do homework alone first before discussing with peers. It is crucial to understand how to approach problems BY YOURSELF!
- When studying for exams, make sure to understand every calculation in the textbook. Redo homework problems/chapter exercises and do not simply skim solutions.

A broader perspective from Spring 2021, reflective of many such comments over the semesters:

This is very important material to master as it is so fundamental to statistics. If there’s one class that gives you exactly what you put into it, it’s this one, so please make an effort to put a lot into it.

Take it! its fun but a lot of work. Be ready to spend a lot of time with the coursework but it will be rewarding.

### Alternatives

If you think this might not be for you this semester, the About page lists other upper division probability classes you can take. They’re all terrific. Be aware though that none of them is an easy ride. For example, student recommendations about Stat 134 often include tips to join the adjunct class at the Student Learning Center, which in effect is advice to spend more time on practice.

Students who want to take 140 after strengthening their math beyond the prerequisites often ask what math class they should take as preparation. Almost invariably my answer is that they should take any math class numbered in the 50s or higher. All math classes make you better at doing math.

I think it’s better to prepare by taking more math instead of lower division probability as in Stat 88. Math skills will have more general applicability and will help more with the second half of 140. However, an increasing proportion of students are taking 140 after Stat 88, partly because Stat 88 satisifes requirements for the Data Science minor and also some majors.