I'm very excited to start Exploratory Data Analysis, and I hope you are too. This is the fourth course in the Data Science Specialization. Exploratory data analysis (EDA) is a key element of data science because it allows you to develop a rough idea of what your data look like and what kinds of questions might be answered by them. EDA is often the "fun part" of data analysis, where you get to play around with the data and, well, explore! As of now, the course web site on Coursera is open. You are free to start watching lecture videos, taking the quizzes, and looking at the two Course Projects.

The recommended background for this course is the course The Data Scientist's Toolbox and R Programming. It is possible to take this class concurrently with that class but you may have to read ahead in the prerequisite class to get the relevant background for this class. The good news is that we will offer all courses in the Specialization every month, so if you don't feel ready to take on Exploratory Data Analysis this month it will start again next month! For a complete set of course dependencies in the Data Science Specialization please see the course dependency chart.

As you browse the course web site, please make sure to read through the syllabus which contains important information about the grading policy for the Quizzes and Course Projects as well as the course schedule. Please note that Quiz 1 and Course Project 1 are both due BEFORE 11:30 PM UTC this coming Sunday. You can access Quizzes and Course Projects by clicking the corresponding links in the left navigation bar.

The primary way to interact with me 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 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 of analytic graphics and the base plotting system in R. 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 demonstration 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.

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

Roger Peng and the Data Science Team