This course will present the basic skills to design and analyze functional magnetic resonance imaging (fMRI) experiments. At the end of the course a student should be able to design, acquire and analyze a fMRI study. There are four modules of the course: (1) Computer skills, (2) Physics of fMRI, (3) Experimental Design, and (4) Statistics. We start with reviewing the basic skills necessary to manipulate image data, using Mat lab and the Unix operating system. Next we introduce the basics of MRI, principals of T1, T2 and T2*, and how images are formed; in the remainder of the physics section we cover the BOLD effect and artifacts that corrupt the signal. In the experimental design section we start by introducing blocked and event-related designs, and how to create designs with optimal statistical power; we cover safety issues and how to screen subjects to enter and be scanned in a MR magnet. The remainder of the experimental design section is focused on practicalities of placing a subject in the scanner and how to use the paradigm presentation software, E-prime. We start the statistics section with a review of the basics of hypothesis testing and linear regression, then present the statistical tools specific to neuroimaging; we cover the analysis of fMRI data, starting from preprocessing to eliminate systematic noise (e.g. subject movement, physiological effects), fitting of models (e.g. intrasubject versus group analysis), diagnosing of model fit and finally statistical inference on statistic maps. The intended audience is a graduate student with basic mathematical and statistical background. Prerequisites are an introductory statistics course; advanced statistics course and experience with Matlab will be an asset.