Welcome to CME/STATS 195!

Announcements

Register for Gear Up for Social Science Data Event! You will be able to learn about publicly available and library purchased data sets, which you can use for the final class project.

Logistics

Location: 200-034
Time: Tue, Thu 12:00 - 1:20pm
Dates: 9/27, 10/2, 10/4, 10/9, 10/11, 10/16, 10/18 and 10/23.

Instructor: Lan Huong Nguyen (lanhuong at stanford dot edu)

Office hours: Mon 3:30-4:30pm, Huang Basement Student Area
Room B015 on Oct 8th, and Room B007 the following weeks

Communication: Please direct your questions on class materials and assignments to: canvas.

Course Description

This short course runs for four weeks and is offered in fall and spring. It is recommended for students who want to use R in statistics, science or engineering courses, and for students who want to learn the basics of data science with R. The goal of the short course is to familiarize students with some of the most important R tools for data analysis. Lectures will focus on learning by example and assignments will be application-driven. No prior programming experience is assumed.

At the end of this class you should be able to:

Import raw data into R.
Tidy and transform data into a format that is convenient for analysis.
Visualize data and make beautiful plots.
Use R to fit simple models, use them to make predictions and test hypotheses.
Use R Markdown to efficiently communicate your data and results.

Some things that you will not learn from this class:

Big data: this class does not cover tools for analyzing large dataset which do not fit in-memory.
Programming: this is not a programming course. If you want to learn the fundamentals of computer programming, you should take a different class (and probably start with a different language).
Statistics: we will not be going over the technical details or the mathematics of the methods presented.

Optional textbook

“R for Data Science” by Garrett Grolemund and Hadley Wickham.

Course prerequisites

There are no formal prerequisites. However, some statistics knowledge and prior programming experience is encouraged.

Course Policy

Grading (Satisfactory/No Credit):

participation: 20%
homework: 40%
final project: 40%

There will be one homework sets due the third week of class. You are also expected to submit a final project (in groups up to 4 students). You are free to choose a topic for your final projects yourself. Please submit a title and a one-paragraph summary/abstract for your intended project by the third week of class. The final report and R code are due a week after the last class.

You are encouraged to take the class for credit, as it forces you to get more practice with R. However, anyone is also welcome to audit the class, but auditors will not be able to have their assignments graded.

Honor code

Please review the Stanford CS honor code, which binds students enrolled in this course.

Students with disabilities

Students with Documented Disabilities: Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty. Unless the student has a temporary disability, Accommodation letters are issued for the entire academic year. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL: (https://oae.stanford.edu/)).