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STAT 1601 Introduction to Data Science with R
Last taught: Fall 2026 Add to Schedule
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Review Summary Updated April 05, 2026

This course is incredibly beginner-friendly and focuses almost entirely on writing R code rather than interpreting statistical concepts, so prior programming or statistics experience is completely unnecessary. You absolutely need to attend lectures, though, since the slides lack the step-by-step code walkthroughs required to handle the assignments and in-person exams. The grading structure is highly forgiving, relying on weekly homework and quizzes that feature retakes and drop your lowest scores, while exams remain straightforward thanks to allowed open-note sheets. Assignments can occasionally be strangely worded and take longer than expected, but consistently using office hours or TA support will easily keep you on track to finish with a high grade while building a strong foundation for future statistics courses.

14 Reviews

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Fall 2024
3.7
Average

This class consists of weekly quizzes and homework (all online and on your own time), and 3 exams (in person). I would say that this class is fairly easy, just go to office hours for the homework and you'll be fine. The quizzes aren't that bad either, some harder than others but you can retake it which makes it easy to get a good score. The exams are not too hard because you get to bring your own cheat sheet for it. The lecture consists of Roland writing codes on RScript and I was confused most of the semester, but I ended with an A, so go to every lecture.

Instructor 4.0
Enjoyability 3.0
Recommend 4.0
Difficulty 3.0
Hours/Week 3.0
Fall 2024
2.7
Average

Ok so this class was a requirement for me. Basically never skip class bc you will fall behind. All of class is just going through r scripts and her slides do not give you that info so u def have to go to class. It is easy if you go to class. There are weekly quizzes and homework and it's all fairly straightforward, you get two tries on the quiz and she averages the score. There are also unit exams, you get a cheat sheet tho. I will say that the exams are the worst part bc they are in person and pretty long but I got an A in the class and had no idea what was going on so if I can do it so can y'all.

Instructor 3.0
Enjoyability 2.0
Recommend 3.0
Difficulty 3.0
Hours/Week 1.0
Spring 2024
5.0
Average

As someone who had never touched coding, Prof. Roland made this class super approachable! I was not excited to take this class as a psych major with no interest in coding, but that completely changed. Prof. Roland is very friendly and willing to answer all the questions we had with coding. Grades were composed of weekly homework (30%), weekly quizzes (20%), three class activities (10%), final project check ins (10%), and the final project (30%). The first half of the semester, the homeworks took me 30 mins max, but as we got deeper into coding, the later ones took me 2+ hours, but they were all very doable. There are also 8 TA office hours per week and a Piazza board that Prof. Roland is responsive on. For quizzes, they were timed for around 20 minutes, and we had two attempts and our score was averaged. She also let us drop our lowest homework/quiz grade which was helpful. It is easy to succeed in this class with the vast number of assignments and the availability of support. The only thing I would note is that Prof. Roland is not flexible with turning things in late. If you forget to turn something in, too bad. She seems really strict with this policy, but also has the built in drop homework/quiz to balance that out, so it's your responsibility to stay on top of the work. Overall, I highly recommend this class with Prof Roland! She is very friendly, knowledgeable, and committed to us succeeding, just make sure you turn things in on time and utilize the resources available. #tCFS24

Instructor 5.0
Enjoyability 5.0
Recommend 5.0
Difficulty 2.0
Hours/Week 2.0
Spring 2024
5.0
Average

(Semester was Fall 2023 but there was no option)
Really fair professor; every assignment was announced far in advance with email reminders leading up to each; quizzed content was never rushed, always drew from at least 2 lectures back; prof was considerate of holidays/busy periods and would avoid scheduling deadlines then. Nice organization where all class notes were basically transcribed in the day's R file, and solutions to class problems were posted afterward. Canvas page was also very organized and easy to refer back to. Overall, just a really organized class with a fair, transparent, organized professor that makes the whole experience smooth! Grade breakdown was 40% HW, 20% Check-ins (short canvas quizzes once in a while), 40% final project. Course content was a very practical starting pt for data sci I think! Learned how to write in base R and use popular libraries like dplyr, tidyr, and ggplot. Some skills u learn are basic data cleaning, data wrangling, finding summary statistics, data visualizations like scatter plots and histograms. Course doesn't go too deep into Statistics but the towards the end u have a unit on hypothesis testing, so you will learn how to use R to find test null hypotheses, find its p-value, etc.

Instructor 5.0
Enjoyability 5.0
Recommend 5.0
Difficulty 2.0
Hours/Week 4.0
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