Computing for Data Analysis @ FIX University Cultural Campus

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Computing for Data Analysis

Roger D. Peng

This course is about learning the fundamental computing skills necessary for effective data analysis. You will learn to program in R and to use R for reading data, writing functions, making informative graphs, and applying modern statistical methods.

Announcements

Clarification of Late Days Grading Policy

There have been some questions on the Discussion Forums regarding the policy for grading late assignments and the application of Late Days. I wanted to provide some details in order to clarify the matter:

1. You have 5 total late days which can be used on quizzes or programming assignments during the course.

2. Using one late day extends your deadline for that assignment by one day. You can view your new deadline by clicking the 'Apply Late Days' button next to a particular assignment/quiz and you can also add or subtract late days to a particular assignment right there. 

3. Whether or not you use late days, you must submit before the hard deadline in order to get credit. If you submit before the hard deadline but after the normal deadline and do not use late days, your assignment will be penalized 50% per day late. 

4. The purpose of late days is to accomodate special situations when you might be traveling or not have an internet connection when a particular assignment is due.

I apologize for the confusion regarding the late days policy and I hope that this clears things up.

Tue 9 Oct 2012 6:19:00 AM PDT

Modification of Forum Topics

Due to the release of the second programming assignment, I've made a small modification to the forum topics to (hopefully) make things a bit easier to manage. There are now separate forums for each of the Programming Assignments as well as a forum for the Quizzes. I hope this helps to clarify the discussion and make questions/answers easier to locate.

Mon 8 Oct 2012 5:53:00 PM PDT

Computing for Data Analysis: Week 3

Welcome to Week 3 of Computing for Data Analysis. This week we cover plotting with both the base graphics and the lattice graphics system. We also cover the random number generation and simulation functions in R. As before, there is a YouTube playlist of the lecture videos. 

This week there is a new Programming Assignment that is due in about 2 weeks. The assignment has multiple parts that cover plotting as well as programming. The plotting parts of the assignment will not be graded, but the remaining programming parts will be. I encourage everyone to try out the plotting assignment parts and to experiment with the different plotting functions. This assignment uses data on hospital quality of care from the Department of Health and Human Services' Hospital Compare web site.

I have been following the forums and participating when possible. In particular, I've been reading the comments/threads about the difficulty of the first Programming Assignment. I sympathize with those that have struggled with the assignment and I've attempted to break down the second assignment into somewhat smaller and more manageable pieces. I hope that you find this useful and more instructive. 

Finally, there is a new quiz this week covering the plotting and simulation material. For those that are curious, the average score on the Week 2 quiz was 8.75 out of 10 with 6,479 students submitting.

Mon 8 Oct 2012 11:11:00 AM PDT

Computing for Data Analysis: Week 2

Welcome to Week 2 of Computing for Data Analysis. So far we've covered installing R and the basic data types. This week we'll be covering control structures, functions, debugging, and the various loop functions in R. There is also a YouTube playlist for the videos for those who prefer to watch the videos there. (Please note that the videos on YouTube do not have the in-video quizzes.) In addition to the Quiz which will cover this week's material, the first Programming Assignment is due this Sunday. I've noticed that many of you (~1,900) have already submitted the assignment, which is fantastic. For those of you that haven't yet, there's still time to get started and submit a solution.

For those that are curious, I posted the data from the pre-course survey that was given the week before the course started. A link to the data can be found on the Discussion Forum (http://goo.gl/ayzew). If you've got any interesting analyses of these data please post your results there.

Finally, I want to thank everyone for participating in the Discussion Forums and for helping out with answering questions. So far we have over 2,000 posts (and 800 comments) to the Forums in over 400 threads. I'm trying my best to answer your questions as they come up but with so many of you out there, community participation is the only way we can make it all work.

Mon 1 Oct 2012 6:37:00 AM PDT

Week 2 Lectures Released

I'm excited that many of you have decided to tackle the programming assignment head on and it seems that many of you have already made substantial progress. I realize that the lectures for Week 1 don't completely cover the concepts you'll need to write the programming assignment (which is due at the end of Week 2), most notably the topic of functions. I was originally going to cover that in the second week but I've released the videos for that topic today so that they may be of use for the programming assignment.

Thank you all for the feedback so far. Please keep the discussions going in the forums.

Wed 26 Sep 2012 7:51:00 AM PDT

Computing for Data Analysis now Open

I'm very excited to start Computing for Data Analysis and I hope you are too. As of now the course web site on Coursera is open and you are free to start watching lecture videos, take the Week 1 quiz, and look at the first programming assignment. 

As you browse the course web site, please make sure to read through the syllabus which contains important information about the grading policy for quizzes and programming assignments as well as the course schedule.

The primary way to interact with me and the teaching assistant in this course is through the discussion forums. Here, you can start new threads by asking questions or you can respond to other people's questions. If you have a question about any aspect of the course, I strongly suggest that you search through the discussion boards first to see if anyone as already asked that question. If you see something similar to what you want to ask, you should up-vote that question using the up-arrow button rather than asking your question separately. The more votes a question or comment gets, the more likely it is that I or the TA will see it and be able to respond quickly. Of course, if you don't see a question similar to the one you want to ask, then you should definitely start a new thread on the appropriate forum.

This week will cover the basics to get you started up with R. There are videos demonstrating how to install R on Windows and Mac and there are a few optional videos showing some more advanced aspects in case you are interested. The Week 1 videos will cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the order that they are listed on the web page, but watching the videos out of order isn't going to ruin the story. For each lecture video you can download a separate PDF document of the slides (the demo videos don't have slides associated with them).

Watching the videos on the Coursera web site is the best way to watch the lectures. However, there are alternative ways to view the lectures if that suits you. You can download the lecture video MP4 files and watch them locally on your computer. Also, I have created a YouTube playlist for the Week 1 lectures athttp://goo.gl/8HBAS in case it is easier for you to watch the videos there.

I hope you enjoy the class. I anticipate a fun four weeks!

Sun 23 Sep 2012 11:11:00 AM PDT

Welcome!

I want to welcome everyone to Computing for Data Analysis. I am delighted that so many people have taken an interest in learning statistical computing and R and am looking forward to working with everyone in the class. 

Of course, this course is about the statistical programming language R and so you will need to install R on your computer if have not done so already. The main R web page is at http://www.r-project.org and it contains a lot of useful information. To download R and install it on your computer, you can get it at the Comprehensive R Archive Network (http://cran.r-project.org). There are videos in this week's set of lectures that explain how to install R on Windows and Mac machines (as well as how to build it from source). 

One option that you may want to explore is RStudio (http://rstudio.org) which is a very nice front-end to R and works on all platforms. It is not required for the course, but it's a nice piece of software that some people may enjoy using.

You will need a text editor to edit R code and write your programming assignments. The Windows and Mac versions of R both come with a text editor (as does RStudio). They will be sufficient for the course. However, if you have a favorite text editor, you are welcome to use that too.

Thu 20 Sep 2012 1:57:00 PM PDT