Ji Meng Loh (19 reviews)

Richard Davis (3 reviews)

Irene Hueter (9 reviews)

Daqing Zhang (0 reviews)

Junyi Zhang (0 reviews)

Kayur Patel (2 reviews)

Kamiar Rahnama Rad (1 review)

Fanesca Young (0 reviews)

Olympia Hadjiliadis (0 reviews)

Ji Meng Loh (1 review)

Diego Saldana (1 review)

Shaw-Hwa Lo (14 reviews)

Zhiliang Ying (1 review)

David Madigan (5 reviews)

Anna Nicolau (1 review)

Chang Ha (2 reviews)

Xin Feng (0 reviews)

Jingchen Liu (0 reviews)

Gregory Eirich (1 review)

Regina Dolgoarshinnykh (2 reviews)

Samory Kpotufe (2 reviews)

Christopher Heyde (3 reviews)

Kristen Gore (2 reviews)

James Landwehr (1 review)

Chun-Fang Yu (2 reviews)

Michael Sobel (5 reviews)

Frank M Caridi (9 reviews)

Ivan Zorych (1 review)

Cynthia Rush (1 review)

Marco Scarsini (1 review)

Ronald Neath (13 reviews)

Aleks Jakulin (1 review)

Martin Lindquist (17 reviews)

Tamara Kucherenko (1 review)

Frank Wood (6 reviews)

Karl Sigman (15 reviews)

Abolfazl Safikhani (3 reviews)

Xin Liu (0 reviews)

Sheela Kolluri (18 reviews)

Mladen Laudanovic (3 reviews)

Anthony Donoghue (17 reviews)

Lucy Robinson (3 reviews)

Konstantinos Kardaras (0 reviews)

Rahul Mazumder (2 reviews)

Georgios Fellouris (2 reviews)

Birol Emir (14 reviews)

Yang Feng (8 reviews)

Joyce Robbins (4 reviews)

Mengling Liu (0 reviews)

Christina Dan Wang (3 reviews)

David Aaron Rios (16 reviews)

Rohit Patra (3 reviews)

Anne van Delft (2 reviews)

John P Cunningham (7 reviews)

Christopher Volinsky (1 review)

Giovanni Motta (2 reviews)

Edward Whalen (9 reviews)

Tian Zheng (13 reviews)

Julia Salzman (14 reviews)

Anna Nicolaou (2 reviews)

Sumit Mukherjee (4 reviews)

General Review of Statistics Department (0 reviews)

Miguel Garcia (1 review)

Alan Yang (5 reviews)

Carsten Chong (3 reviews)

Cristian Pasarica (0 reviews)

Hammou El Barmi (8 reviews)

Hammou Elbarmi (1 review)

Gerardo Hernandez-Del-Valle (13 reviews)

Marianthi Markatou (1 review)

Christopher Dolan (4 reviews)

Andrew Gelman (6 reviews)

Michael Hogan (8 reviews)

Ioannis Karatzas (7 reviews)

Kamiar Rahnama Rad (3 reviews)

Victor H. de la Peña (16 reviews)

Guy Cohen (6 reviews)

Chris Volinsky (1 review)

Steven Kou (13 reviews)

Gabriel Young (7 reviews)

Larry Heuer (10 reviews)

Yuanjia Wang (4 reviews)

Rachel Schutt (4 reviews)

Takaki Hayashi (3 reviews)

Rishi Talreja (2 reviews)

Tyler McCormick (5 reviews)

Daniel Rabinowitz (19 reviews)

Ying Liu (2 reviews)

Ragnheidur Haraldsdottir (2 reviews)

Libor Pospisil (2 reviews)

Victoria Stodden (3 reviews)

Ha Nguyen (7 reviews)

Jan Vecer (8 reviews)

Banu Baydil (10 reviews)

Wayne Lee (7 reviews)

Xin Yan (4 reviews)

Heng Liu (1 review)

Mark Brown (18 reviews)

Young Kim (5 reviews)

Larry Wright (16 reviews)

Eric Johnson (2 reviews)

John Patrick Cunningham (2 reviews)

Michael Shnaidman (7 reviews)

Duncan Szeto (1 review)

Liam Paninski (4 reviews)

Ha Nguyen (3 reviews)

Morgane Austern (1 review)

Jay Devore (1 review)

Stephanie Zhang (1 review)

Haipeng Xing (1 review)

  • 4105 Probability
  • 4107
  • Analysis I: 2005 Winter
  • Applied Data Mining
  • Applied Data Science
  • Applied Linear Regression Analysis
  • Applied Linear Regression Models
  • Applied statistical computing
  • Applied Statistical Methods
  • Bayesian Statistics
  • Calc Based Intro to Stat
  • Calculus Based Introduction to Probability and Statistics
  • Data Analysis
  • Data Mining
  • Elementary Stochastic Processes
  • Generalized Linear Models
  • Introduction to Data Science
  • Introduction to Probability
  • Introduction to Statistical Reasoning
  • Introduction to Statistics
  • Introduction to Statistics (A)
  • Introduction To Statistics: Probability Models
  • Intro to Probability and Statistics
  • Intro. to Probability and Statistics
  • Intro to Statistical Computing
  • Intro to Statistical Inference
  • Intro to Statistics
  • Intro to Statistics
  • Intro to Statistics
  • Intro to Statistics (B)
  • Intro to Stats -- TA
  • Intro to Stats with Calculus
  • Linear Regression Models
  • Linear Regression Models
  • Linear Regression Models R
  • Linear Regression W4315
  • Linear Regression W4315 with Haipeng Xing
  • Mathematical Methods for Statistics
  • Probability
  • Probability
  • Probability
  • Probability
  • Probability 3000
  • Probability and Statistical Inference
  • Probability and Statistics 3600
  • Probability Models in Statistics
  • Probability Models W3000
  • Probability Theory
  • Quantitative Techniques
  • S1111Q - Intro - Summer 2003
  • SIEO 4105
  • SIEO 4150
  • Statistical Inference
  • Statistical Inference
  • Statistical Inference 4107 Fall 2005
  • statistical reasoning
  • STAT W1001
  • Stat W1111 Recitation
  • Stochastic Processes: Applications I
  • Survey Sampling
  • Survival Analysis
  • Theory of Interest
  • Time Series Analysis
  • Time Series and Statistical Inference
  • w4107 Statistical Inference
  • W4109
  • W4220
  • W4220 Analysis of Categorical Data
  • W4221 Categorical Data Analysis Fall 2004
  • W4315-Linear Regression
  • W4437 Time Series Analysis
  • May 2021

    This class will be tough for those (like me) who have never coded before. I struggled intensely in the course. Wayne considers this course to be introductory, but I think this course is really at the high introductory level given the breadth of the material covered and the pace at which the class progresses. The topics this course covers are variables, vectors, data structures (lists, data frames, matrices, etc.), joining data, functions, creating functions, loops, data wrangling, scraping, regular expressions (processing text data), calling data from APIs, and debugging. The examples done in class may make you feel confident that you know the material, but you will not be prepared to complete the homework based on the examples Wayne goes through in class. For example, on the unit on APis, Wayne did a relatively simple API call in class. On the homework and exam, you needed to do multiple API calls using a loop. Multiple people opted to not submit that particular homework because they were not confident they could construct the API call (You are allowed to drop one homework assignment, which is nice to have when you get to the end of the course and the material begins to feel quite impossible). This also applied to other topics, where the examples that Wayne did in class did not really prepare you to do the homework and exam questions. Wayne has not exactly mastered teaching yet, so learn how to Google well enough to be able to more deeply understand concepts and learn how to solve problems that test them. Honestly, class would be better served if he went through multiple example problems to illustrate a concept. To succeed in this course, you should work with friends to complete the homework and attend office hours when you are stuck on the homework. At the very least, make sure you understand the homework solutions, so you can be prepared for the exams. Wayne does not really give out practice exams, and when he does, he does not give out the solutions to the practice exams. He also does not really release midterm solutions. The best preparation for the exams is to understand the homework. For the most part, the homework prepares you for the exams. The only thing that makes the exams difficult is that they can be very time-intensive (I generally spent 8+ hours on exam 2 and the final exam and so did many others). Also, Wayne wants the code and output to be structured in a certain kind of way. He loves to threaten to take off points. Do not output an entire data frame in your submissions and try to avoid hardcoding.

    May 2021

    Best professor I've had in the stats department, I completely agree with the review below.

    May 2021

    The good: Wayne is an extremely friendly person and he will always make time for students even when he is visibly swamped and burnt out. He never gets too theoretical and will always try to connect the algorithms he's teaching with real-world applications. Very generous curve although I think he was kind because it's his first time teaching. The questionable: I personally didn't like the simulations he used to teach the algos. It seems easier to dive right into applications. The breakout rooms were also very unhelpful in my opinion because we all just stare at each other while one enthusiastic person does all the work. The bad: Wayne is not the best communicator. He would go off tangents that have very little relation to the topic at hand. He also doesn't word his homework properly and we often don't know what he wants. The horrible (dealbreaker): He didn't have transparent metrics for evaluating our projects. He would arbitrarily insert his personal data science experience and expect us to know what sort of things should be in the report. Given that the projects were 70% of the grade he should have been a lot clearer with what he wants.

    May 2021

    I enjoyed the course and I think I learned a lot. I think the curve was VERY generous. I was expecting to get a B but I got an A-. It is true that take-home exams were too long, and it took me 9-10 hours to finish the midterm that was supposed to take 1.5 hours. However, I think that having a day to think about the problems helped me absorb the concepts a lot better. The bad thing is, however, that I was thinking I am incompetent because I did not know that other students also took a day to complete the midterm. It was very unfortunate that Wayne didn't listen to the class when we asked him to give a 48 hours window for the final exam (which was twice as long). I would suggest giving the exams as projects with 48-hour windows. Then students would have proper expectations, and won't panic during the exam when they can't finish. Another suggestion is to talk about the curve at the beginning of the class. Wayne excluded students who did the best in the class while calculating the curve. If I knew he would do that, I wouldn't have been as stressed after the midterm. I think that Wayne kept his promise in providing the class a generous curve. The first exam was easiest if you understand your homework assignments you could do pretty well on it. The midterm was pretty hard. But don't panic, I got an 23/39 on the midterm but still managed to get an A-. The final exam is actually pretty doable IF you properly understand the midterm. Take time to go to office hours, and ask Wayne to go over the midterm questions. And after that go over them by yourself. If you understand them, you will perform well in the final. The final is composed of questions that were either on midterm, in the class, or in the homeworks. I basically read a question in the final, found a similar problem that I solved during the lecture/midterm/ assignment, and applied it to the final. If you follow that rule you would do great. Also Wayne drops your lowest homework grade! I hope Wayne continues to record his lectures in the future because it was very helpful to go back and rewatch the recordings.

    May 2021

    Nice and funny professor, though I don't know if one could take this class without prior experience with statistics. He's very chill (maybe too chill) and I don't think anyone would have a problem with him. That being said, if the concepts are new, I'd expect outside reading in addition to his notes to learn more. He also made sure not to talk too fast. He also liked to show us his dogs via Zoom video, Zoom backgrounds, and problem questions.