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*Computing for Data Analysis*

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*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.*

* 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*

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.

Each programming assignment is worth 30 points and is broken down into sub-parts. For each sub-part you will be allowed an unlimited number of submissions and your latest score will be taken as the final score.

Here is some platform-specific information:

Performance in this course will be evaluated on a pass/fail basis. The final grade for the course will be based on the total number of points earned across the four quizzes and two programming assignments. In order to receive a passing grade, you must have a earned a total number of points of 70 or more.

## Syllabus

In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment, discuss generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, creating informative data graphics, accessing R packages, creating R packages with documentation, writing R functions, debugging, and organizing and commenting R code. Topics in statistical data analysis and optimization will provide working examples.

## Objectives

After taking this course you should be able to- Read formatted data into R
- Subset, remove missing values from, and clean tabular data
- Write custom functions in R to implement new functionality and making use of control structures such as loops and conditonals
- Use the R code debugger to identify problems in R functions
- Make a scatterplot/boxplot/histogram/image plot and modify a plot with custom annotations
- Define a new data class in R and write methods for that class

## Lecture Materials

Lecture videos will be released weekly and will be available for the week and thereafter. You are welcome to view them at your convenience. Accompanying each video lecture will be a PDF copy of the slides, unless the lecture is a demo, in which case there will be no lecture slides.## Quizzes

There will be four weekly quizzes that will test your comprehension of the material covered in the lecture material provided that week. The quizzes will consist of multiple choice, true/false, or short answer questions. Each quiz is worth 10 points. You will be allowed 3 attempts to submit each quiz and your maximum score will be taken as the final score.### Quiz Opening/Closing Dates

- Quiz 1: 2012-09-24 12:00:00 AM PDT to 2012-09-30 11:59:00 PM PDT
- Quiz 2: 2012-10-01 12:00:00 AM PDT to 2012-10-07 11:59:00 PM PDT
- Quiz 3: 2012-10-08 12:00:00 AM PDT to 2012-10-14 11:59:00 PM PDT
- Quiz 4: 2012-10-15 12:00:00 AM PDT to 2012-10-21 11:59:00 PM PDT

## Programming Assignments

There will be two programming assignments that will involve writing R code and R functions. These assignments will allow you to work on your R programming skills and practice writing and debugging code. For each programming assignment you will be asked to write R code or functions that produce output given a certain input. Your grade on the assignment will be based on whether the output your function produces matches the correct output. Details can be found in the descriptions of each programming assignment.Each programming assignment is worth 30 points and is broken down into sub-parts. For each sub-part you will be allowed an unlimited number of submissions and your latest score will be taken as the final score.

### Programming Assignment Due Dates

- Programming Assignment 1: 2012-10-07 11:59:00 PM PDT
- Programming Assignment 2: 2012-10-21 11:59:00 PM PDT

## Technical Information

Regardless of your platform (Windows or Mac) you will need a high-speed Internet connection in order to watch the videos on the Coursera web site. It is possible to download the video files and watch them on your computer rather than stream them from Coursera and this may be preferable for some of you.Here is some platform-specific information:

### Windows

The Coursera web site seems to work best with either the Chrome or the Firefox web browsers. In particular, you may run into trouble if you use Internet Explorer. The Chrome and Firefox browsers can be downloaded from- Chrome: http://www.google.com/chrome
- Firefox: http://www.mozilla.org

### Mac

The Coursera site appears to work well with Safari, Chrome, or Firefox, so any of these browsers should be fine.## Grading

Your grade in this course will consist of performance on four weekly quizzes and two programming assignments. The breakdown of the weighting for these elements is- Week 1 Quiz: 10 points
- Week 2 Quiz: 10 points
- Week 3 Quiz: 10 points
- Week 4 Quiz: 10 points
- Programming assignment 1: 30 points
- Programming assignment 2: 30 points

Performance in this course will be evaluated on a pass/fail basis. The final grade for the course will be based on the total number of points earned across the four quizzes and two programming assignments. In order to receive a passing grade, you must have a earned a total number of points of 70 or more.

## Weekly Schedule

Week 1- Introduction and overview
- Installing R
- Data types, subsetting
- Reading/writing data

- Control structures
- Functions
- Loop functions
- Debugging

- Simulation
- Plotting, visualizing data
- Priniciples of data graphics

- Objected oriented programming
- Data abstraction
- Statistical modeling

Created Mon 25 Jun 2012 10:48:56 AM PDT

Last Modified Sun 23 Sep 2012 10:36:16 AM PDT

Last Modified Sun 23 Sep 2012 10:36:16 AM PDT

*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 at http://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*
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