Below are some of the questions I get asked almost everytime I teach Stat 133.
Depending on the size of the waitlist, and the available space in the labs, you may or may not have a chance to join the class. In previous semesters, about half of concurrent student applications were accepted.
If you are a grad student enrolled in Stat 133, then you should schedule a meeting with me within the first 2-3 weeks of classes. I would like to know more about your program/project, and discuss the scope of the course with you.
Unfortunately, this is NOT possible. You must attend the lab discussion you are officially registered in.
Yes. We actually expect that most of you come without any coding experience. It is nice to have some programming experience under your belt, which makes the learning curve less steep.
You may find some parts of this course somewhat slow (and boring?) in terms of basics concepts such as data types, data structures, conditionals, loops, and functions. Please consider taking more advanced courses if what you are interested in is algorithms, computational statistics, data bases, or machine learning.
Yes. We actually expect that most of you come without any data analysis experience. In this course you will be working with fairly simple real data sets, as well as with simulated data.
Stat 133 is one of the core courses of the Statistics Major. The way I teach the course is having Statistics majors as my target audience. However, much of the content should be helpful for any student who has to analyze data.
What if I want to declare Statistics as my major, but I already have taken other programming courses on Campus?
If you have taken Data 100 (e.g. C100) "Principles & Techniques of Data Science", you can waive Stat 133 by just taking Stat 33B "Introduction to Advanced Programming in R".
Becoming a data scientist is not a sprint. It is a marathon. Like any other profession, it takes years of practice and learning. This course is just the beginning.
That's perfect too. You don't need to be a data scientist aspirant to take this course. Whether your plans are to become a consultant, life scientist, social scientist, journalist, or get some analytic skills, this course should be a good choice.
No. This course is not about machine learning (or statistical learning) methods. The Statistics department offers a dedicated course on this topic: Stat 154: Modern Statistical Prediction and Machine Learning. There is also CS 189: Introduction to Machine Learning offered through Electrical Engineering and Computer Sciences (EECS).
No. If you are interested in Databases you should consider CS 186: Introduction to Database Systems offered through Electrical Engineering and Computer Sciences (EECS).
No. The course for linear models is Stat 151A: Linear Modeling, Theory and Applications.
We are just going to scratch the surface. We will touch on dynamic documents, practices and tools that are useful in RR (e.g. Git, GitHub).
This course requires many hours of practical work outside class and lab. It also requires reviewing all the material available in the calendar of topics. Having said that, the midterm and final exams are a way to test your understanding of the various concepts presented in the course. The exams are also a way to test whether you really did all the practical work by yourself.
In theory, students who do an honest effort in completing all the assignments (e.g. writing commands, understanding commands, learning the syntax, etc) should not struggle answering the tests.
Tip: try to explain how some piece of code works to your friend(s).
This one is hard to answer, in part because it depends on your personal definition of "success". Simply put, I don't think there's a unique recipe for success. Instead, let me answer this question by telling you about the typical factors that may negatively affect your performance: missing lectures, missing labs, not submitting assignments, looking at the solutions of other students and "inadvertently" copy them, poor studying/working habits.
No. We don't expect that you memorize all commands. However, we do expect that
you learn the most common types of functions: e.g. library(), function(), help(), etc. Likewise, we expect that you understand the "logic" and working principles of certain data objects, common programming structures, good practices, etc.
In the last three editions of Stat 133, students have learned how to create a basic R package. We would like to continue having this activity, but first I need to discuss this idea with my teaching staff.
Yes, absolutely. We strongly encourage you to not work alone. Well, let me rephrase that. You should try to first work on your own (trial and error). Take notes of the things you don't understand. Then get with other people and discuss ideas, share tips (but not the entire solution).
Yes. But you don't learn programming by watching someone else program. The same way that you don't learn to swim by watching someone else swimming. You have to get into the pool, and do all the drills your instructor says. This is a very hands-on course, and you will be required to do a great amount of work on your own.
If there is one or more policies you don't agree with, then please reconsider your enrollment in the course. I am assuming that all students completely agree with the course policies.
Quick answer: No. However, I am happy to write you a letter of recommendation if I have known you for at least one year, and as long as we have developed a good collegial relationship (e.g. I know your name, I know your personal story, you've shown interest in my work). Getting a "good grade" does not guarantee that I will write you a LoR. Conversely, getting a "not so good grade" does not have to be an impediment to write you a LoR.
First: Don't take it personal. It's not you, it's me. Second: if you really want me to be part of your network, why don't you come see me in person? We can meet in OH, we can talk right before or after class. Or you can also schedule a meeting at a different time. Let me know you better than just as a distant contact in a social media networking site.
Lecturing takes most of my time and I don't have a lab. However, I'm always coming up with new ideas and experiments, and it's nice to have additional human resources to create something useful, interesting, open (and cool). If you are interested in volunteering and willing to learn a lot, come talk to me and let's see if we can join forces, and add our two cents to the world.