Using R PASS
This certificate program is aimed at those who need to use the R statistical programming environment for statistical analysis, visualization and modeling, and offers the equivalent of 18 credits, in the US academic system.
This program provides an appropriate balance between conceptual understanding and application. As a successful PASS candidate, you will acquire the statistical skills and software facility you need for projects, reports, and research. While open to all, the flexible format and scheduling is particularly suitable for working professionals.
The program courses are taught by leading experts with whom you share a private discussion forum for the entire course period. Students participate from all over the world - lessons and assignments are opened and closed on a weekly basis. You organize your time however you like within each week - there are no set times when you must be online.
Courses in the Program are typically 4 weeks long, and require about 15 hours per week. Courses operate throughout the year, and there is no "semester" system. You can enter and start taking courses whenever you like, after reviewing the Program requirements and guidance on course sequencing. Most students take one course at a time, and complete their Program over a period of 3-5 years.
There are four main components to each course:
- Course materials (course text, course files, video and other materials)
- Discussion with the instructor and other students via daily web forum (asynchronous - you need not be online at any particular time of day)
- Homework (submitted online weekly, with individualized feedback the following week)
- Final project/exam
For more details on how these online courses work, click here. Students successfully completing the Program receive a certificate and transcript.
Placement Services
As a PASS candidate, once you successfully complete half your program courses, you are eligible for our personalized placement services. We will review your background, your interests, and your work at the Institute, and try to find a match with appropriate recruiters. Our specialty is early-to-mid-career placement (not entry level).
When To Apply
Applications are accepted and students are enrolled year round on a rolling basis. You pay an application fee and submit it with your application form. You may pay the fee online here, or enclose a check (USD drawn on a US bank) with your application. NOTE: The application is your opportunity to specifically outline your past education and experience, and provide any other information you want us to consider during our review. The value of this background and experience is the ability to place the statistical methods covered in the Program in an appropriate application context. Once we have accepted your application, we will be happy to assist you with more specific questions about PASS.
Admission Criteria
- A Bachelor's degree from an accredited college or university,
- Successful completion of an introductory statistics course equivalent to Statistics.com's Intro courses series ("Introduction to Statistics for Beginners" through "Introduction to Statistics 3") that prepares the applicant for a passing score in the required entrance exam which covers basic statistics.
- Passing score on the online entrance exam.
Note: While it is not an admission requirement, applicants will also benefit from employment experience (or plans for employment) in an organization that works in the selected field of the Program you select.
Enrollment In PASS
Enroll in the Program of your choice here, after your application is approved AND you have passed the entrance examination. Once enrolled, register for each specific course within your PASS using the academic tuition rate as you are now a matriculated student at the Institute for Statistics Education at Statistics.com.
Application Fee: $75
Program Registration Fee:$495
Estimated Required Course Tuition: $2394
Estimated Electives Course Tuition: $2351
Estimated Material Cost: $600
Estimated Total Cost: $5915
Using R PASS
Program Objectives
R is a powerful open-source environment for statistical analysis, visualization and modeling, and is growing rapidly. The R community contributes new statistical routines to R on a continual basis (there are several thousand), so R is both a statistical programming environment and a repository for high-level analysis tools. Students completing the Program successfully will gain mastery of statistical topics, plus specific skills and use of software tools important to statistical analysis of epidemiological data.
The Program consists of courses offered completely online at statistics.com. Required courses for all students include those aimed at fundamental programming skills, as well as courses covering the use of R functions and routines for statistical analysis and modeling. Electives allow for further study of graphics (ggplot2), domain specific applications (data mining, clinical trials), and statistical methods (linear modeling, spatial analysis, resampling). Some electives are devoted exclusively to R, others feature R among other software options.
The courses are taught by recognized authorities with whom you share a private discussion forum for the entire course period. Among the faculty are some of the leading members of the R development community. Most students take one course at a time; courses are scheduled throughout the year on a continuous basis, providing for maximum flexibility in meeting program requirements.
Students successfully completing this program will acquire skills in two areas:
- Data manipulation and programming
- Application of R routines to statistical analysis and modeling
Sequencing your Program
Most courses are four weeks long, and are scheduled either once or twice a year. Courses start on specific dates, but do not require you to be online at any particular time of the day. Since various courses are available throughout the year, the Program provides flexibility in scheduling.
Listed below in alphabetical order, are the required and elective courses with available dates. Additionally, use these tools to help you map out your program:
- Prerequisites – each course lists its prerequisite. These prerequisite courses should be taken prior to the course you are considering.
- Suggested sequencing document - this lists the courses in groups, based upon their approximate level of difficulty. It is a guide, not a set of requirements.
- Calendar - You can easily search course start dates here.
After your successful acceptance into PASS, you can begin your program with any course, based on your own background, the level of difficulty and if you meet the prerequisites. You will work out a tentative schedule (or at least the first few courses), then register and pay tuition.
As a PASS candidate, you are eligible to register for any course using the academic tuition link, indicating “PASS” when prompted for your academic affiliation during online registration. As an added benefit, if you register online for three courses simultaneously, an automatic USD200 reduction is applied. Please note that payment of tuition and fees does not include the purchase of any required texts or software.
Suggested sequencing document
Calendar
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Required Courses (6)
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Graphics in R
The aim of this course is to teach you how to produce statistical plots of data using the R language and environment for statistical computing and graphics. The creation of standard plots such as scatterplots, bar plots, histograms, and boxplots will be covered and time will be spent on the underlying model used to produce plots in R so that you can extensively customize these plots.
Tuition: $399 (5.0 CEUs)
New available dates:
May 11, 2012 to June 08, 2012October 19, 2012 to November 16, 2012 more >>
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Introduction to R - Data Handling
This course will provide an easy introduction to R and its use in organizing and exploring data. Once you've completed this course you'll be able to enter, save, retrieve, manipulate, summarize and display data using R.
See also the related course Introduction to R-Statistical Analysis, which is an introduction to the use of R for executing statistical tests and procedures.
Tuition: $399 (5.0 CEUs)
New available dates:
July 27, 2012 to August 24, 2012 more >>
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Introduction to R - Statistical Analysis
This course covers how to use R for basic statistical procedures.
Tuition: $399 (5.0 CEUs)
New available dates:
May 18, 2012 to June 15, 2012September 28, 2012 to October 26, 2012 more >>
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Modeling in R
This course will show you how to use R to create statistical models and use them to analyze data.
Tuition: $399 (5.0 CEUs)
New available dates:
March 09, 2012 to April 06, 2012August 31, 2012 to September 28, 2012 more >>
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Programming in R
The aim of the course is to give you the skills to work with a variety of data types and data sources in R. You'll also learn some techniques for programming "in-the-large", when you are trying to provide a suite of functions to flexibly solve a large class of problems.
Tuition: $399 (5.0 CEUs)
New available dates:
April 13, 2012 to May 11, 2012October 26, 2012 to November 23, 2012 more >>
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Programming in R - Advanced
Programming in R - Advanced will help you write better code, focused on the mantra of do not repeat yourself. You will learn powerful new tools of abstraction, allowing to solve a wider range of problems with fewer lines of code.
Tuition: $399 (5.0 CEUs)
New available dates:
June 15, 2012 to July 13, 2012 more >>
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Elective Courses (6 required)
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Biostatistics in R: Clinical Trial Applications
This course covers the implementation in R of statistical procedures important for the clinical trial statistician.
Tuition: $399 (5.0 CEUs)
New available dates:
May 25, 2012 to June 22, 2012November 16, 2012 to December 14, 2012 more >>
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Bootstrap Methods
This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications.
Tuition: $399 (5.0 CEUs)
New available dates:
March 16, 2012 to April 13, 2012 more >>
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Data Mining - R
The main goal of this course is to teach users how to perform data mining tasks using R.
Tuition: $399 (5.0 CEUs)
New available dates:
June 29, 2012 to July 27, 2012 more >>
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Generalized Linear Models
This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis. GLM allows the modeling of responses, or dependent variables, that take the form of counts, proportions, dichotomies (1/0), positive continuous values, as well as values that follow the normal Gaussian distribution. Logistic, Poisson, and negative binomial regression models are three of the most noteworthy GLM family members.
Note: Detailed study of model specification and the interpretation of software output is handled in statistics.com's individual courses on regression, logistic regression, count data modeling, etc., and in the Categorical Modeling course.
Tuition: $399 (5.0 CEUs)
New available dates:
April 06, 2012 to May 04, 2012November 23, 2012 to December 28, 2012 more >>
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Introduction to Resampling Methods
The course introduces the basic concepts and methods of resampling methods, including bootstrap procedures and permutation (randomization) tests, with little or no complex theory or confusing notation.
Tuition: $299 (3.75 CEUs)
New available dates:
February 17, 2012 to March 09, 2012July 06, 2012 to July 27, 2012 more >>
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Introduction to Support Vector Machines in R
This course begins by developing basic concepts such as hyperplanes, features spaces and kernels; then it covers the development of support vector machines (SVM's).
Tuition: $399 (5.0 CEUs)
New available dates:
November 16, 2012 to December 14, 2012 more >>
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Logistic Regression
Logistic regression extends ordinary least squares (OLS) methods to model data with binary (yes/no, success/failure) outcomes. Rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
Tuition: $399 (5.0 CEUs)
New available dates:
March 16, 2012 to April 13, 2012June 15, 2012 to July 13, 2012September 07, 2012 to October 05, 2012 more >>
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Modeling Count Data
This course deals with regression models for count data; i.e. models with a response or dependent variable data in the form of a count or rate. The course will cover Poisson regression, the foundation for modeling counts, as well as extensions and modifications to the basic model.
Tuition: $399 (5.0 CEUs)
New available dates:
May 11, 2012 to June 08, 2012October 19, 2012 to November 16, 2012 more >>
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Probability Distributions
This course cover statistical probability distributions. Participants will learn how to identify which distribution(s) reasonably fit given data, and to evaluate the fit.
Tuition: $399 (5.0 CEUs)
New available dates:
June 22, 2012 to July 20, 2012 more >>
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Smoothing with P-splines (Using R)
Splines are combinations of different functions that are used to describe and model data differentially in a smooth fashion over different ranges. In this course, you will learn how to use R software to develop splines for data smoothing.
Tuition: $399 (5.0 CEUs)
New available dates:
June 22, 2012 to July 20, 2012December 07, 2012 to January 11, 2013 more >>
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Spatial Analysis Techniques in R
This course will teach users how to implement spatial statistical analysis procedures using R software.
Tuition: $399 (5.0 CEUs)
New available dates:
December 14, 2012 to January 18, 2013December 13, 2013 to January 17, 2014 more >>
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Statistical Analysis of Microarray Data with R
This course will acquaint you with the process of analysis of microarray data. You will learn how to preprocess the data, short list the differentially expressed genes, carryout principal component analysis to reduce the dimensionality and to detect interesting gene expression patterns, and clustering of genes and samples. Illustrations of the statistical issues involved at the various stages of the analysis will use real data sets from DNA microarray experiments.
Tuition: $399 (5.0 CEUs)
New available dates:
April 20, 2012 to May 18, 2012October 19, 2012 to November 16, 2012 more >>
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Survey Analysis in R
The purpose of this course is to teach survey researchers who are familiar with R how to use it in survey research.
Tuition: $399 (5.0 CEUs)
New available dates:
March 23, 2012 to April 20, 2012 more >>
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Visualization in R with ggplot2
R Project ggplot2 taught by its creator. This course teaches the "Grammar of graphics" in an elegant, efficient and systematic manner.
Tuition: $399 (5.0 CEUs)
New available dates:
July 20, 2012 to August 17, 2012 more >>
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Download Application
Download your application to qualify for PASS today.
Click Here.