Introduction to Statistical Programming with R

Lecture, three hours; discussion, one hour. Enforced requisite: one course from course 10, 12, 13, 15, Economics 11, 41, or Psychology 100A, or score of 4 or higher on Advanced Placement Statistics Examination. Designed to prepare students for upper-division work in statistics. Introduction to use of R, including data management, simple programming, and statistical graphics in R. P/NP or letter grading.

Review Summary

Clarity
10.0 / 10
Organization
10.0 / 10
Time
15-20 hrs/week
Overall
10.0 / 10

Reviews

    Quarter Taken: Summer 2021 Online
    Grade: A

    Great class, the professor cares a lot with a lot of office hours opportunities to ask questions.

    Quarter Taken: Fall 2021 In-Person
    Grade: A

    Not an easy course by any means, but not as difficult as some people make it out to be. Further, Mike is one of the most passionate and goated instructors around--it is super clear how much he cares about you learning, evident through his numerous office hours. As is in the nature of an introductory programming course, there is a lot of work outside of class, but if you do the homework each week and make sure you understand it, the tests are manageable.

    Quarter Taken: Fall 2021 In-Person
    Grade: A-

    Lectures were easy to follow along, Prof Mike reiterates what the slides/notes says during lecture. He also provides examples that he might have missed in the notes so you should attend lecture just in case. Although the lectures were easy to follow along, they did not necessarily reflect what was being asked on the homework. For me, there was a large gap between understanding the lecture notes and trying to understand the homework so I had to supplement my understanding with external resources. However the homework is based off completion so as long as you attempt the problem you will be okay. The tests were less difficult than the homework so it was bearable and more related to what was taught in lecture. I bombed all the tests, but I improved my score each time and I ended with an A-. Overall, the class is structured around the homeworks and tests, so that is what makes it hard, however, Prof Mike will reiterate that he just wants you to learn and improve your understanding of R each week so as long as you show that through your tests, go to office hours, ask questions, or etc., you will be okay.

    Quarter Taken: Fall 2021 In-Person
    Grade: A

    This was a well-structured class. The material could be difficult at times and I averaged a B- on the exams, but the curve was very generous. Professor Tsiang is amazing and I would recommend this class.

    Quarter Taken: Fall 2021 In-Person
    Grade: A

    Great class! Mike really cares about your success.

    Quarter Taken: Spring 2021 Online
    Grade: B

    this class is so hard

    a lot of homework

    Quarter Taken: Spring 2021 Online
    Grade: A

    Great class! Highly recommend this professor for learning R. Be sure to go to OH and talk with professor Tsiang, the TA, and the LAs! Workload may be slightly heavy for someone who hasn't programmed before so I recommend taking PIC 10A or CS 31 beforehand, both of which have higher course-loads IMO and better introduce you to programming in general. This isn't to say that this isn't a great course for an introduction to programming, it's just that this is oriented toward statistical programming and may be easier if one had prior coding experience.

    Quarter Taken: Fall 2021 In-Person
    Grade: A

    Triangle is a really friendly prof who are really generous in giving advices. Don’t be shy.

    Quarter Taken: Winter 2022 In-Person
    Grade: A

    good

    Quarter Taken: Summer 2022 In-Person
    Grade: A-

    Professor Tsiang definitely teaches this class with more of an emphasis on the programming portion of the course, and less focus on the statistical applications of R. Either way the class is very manageable if you go to Office Hours. Professor Tsiang was always extremely helpful with any questions I had, and had a genuine concern for my learning.

Course

Instructor
Michael Tsiang
Previously taught
23W 22F 22Su 22S 22W 21F 21Su 21S 21W 20F 20Su 20S 20W 19F 18F 18Su 17F

Grading Information

  • No group projects

  • Attendance required

  • 1 midterm

  • Finals week final

  • 64% recommend the textbook

Textbooks

Textbook information not available.