PASS

Programs in Analytics and Statistical Studies

Master the software and practical skills
you need to get ahead.

Using R (legacy)

This program is not longer being offered. Those originally in this program may continue with it or choose to transfer to the Analytics for Data Science or Programming for Data Science program.

The Institute offers 100+ courses in statistics and analytics; this particular certificate program focuses on those courses relevant to learning how to program in R and use it for statistical analysis.  Most courses are 4 weeks long, do not require you to be online at specific times during the week, and offer continuing education credits.  The workload for the entire program is the equivalent of 21 credits in the U.S. academic system.

The courses are taught by recognized authorities with whom you share a private discussion forum for the entire course period.

Most PASS candidates choose to take one course at a time.  With courses starting every week of the year, there is considerable scheduling flexibility.  Admission applications are accepted on a rolling basis throughout the year.

 

Using R (legacy)

Program Content

The program consists of courses offered completely online at Statistics.com. There is a small group of required topics, plus a number of electives. Different concentration strands within the program are available, corresponding to different sets of suggested electives. These strands are optional; if you successfully complete the courses in the strand this will be reflected on your PASS transcript. In addition to the concentration electives, students select several additional electives from among the general Program electives.

R Programming:  We suggest R Programming Intermediate, R Programming Advanced, and Wrangling and Munging Data with SQL and R.  If you are experienced with programming languages or statistical computation environments, you can skip over R Programming - Introduction.  If you are new to programming altogether, this strand will not be useful to you unless you build in a couple of years of on-the-job programming experience between the initial courses and R Programming Intermediate.

R for Statistical Analysis:  We suggest Logistic Regression, Introduction to Smoothing and P-spline Techniques Using R, and Wrangling and Munging Data with SQL and R.


Plan your PASS

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Planning 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.

Use the interactive "Plan Your PASS" tool to map out your program.  Additionally, the required and elective courses are listed below in alphabetical order with the next available date.  IMPORTANT:  The interactive planning tool does not include detailed information on prerequisites for each course.  See the course description for that information.

After your successful acceptance into PASS, you can begin your program with any course, based on your own background, the level of difficulty and whether you meet individual course prerequisites.

As a PASS candidate, you are eligible for the academic affiliation tuition reduction - indicate "PASS" when prompted for your academic affiliation during online registration.  As an added benefit, if you register online for three courses simultaneously, an automatic $200 reduction is applied.  Please note that payment of tuition and fees does not include the purchase of any required texts or software.


Required Courses (6)

  • 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 charts, 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: $469 (5.0 CEUs) New available dates: May 02, 2014 to May 30, 2014October 10, 2014 to November 07, 2014May 01, 2015 to May 29, 2015October 09, 2015 to November 06, 2015 more >>
  • Modeling in R This course will show you how to use R to create statistical models and use them to analyze data. Tuition: $549 (5.0 CEUs) New available dates: August 22, 2014 to September 19, 2014 more >>
  • R for Statistical Analysis This course covers how to use R for basic statistical procedures. Tuition: $469 (5.0 CEUs) New available dates: July 25, 2014 to August 22, 2014October 03, 2014 to October 31, 2014 more >>
  • R Programming - Introduction 1

    This course will provide an easy introduction to programming in R.

    Tuition: $469 (5.0 CEUs) New available dates: May 09, 2014 to June 06, 2014August 01, 2014 to August 29, 2014November 07, 2014 to December 05, 2014 more >>
  • R Programming - Introduction 2 This course continues the introduction to R programming. Tuition: $469 (5.0 CEUs) New available dates: June 13, 2014 to July 11, 2014September 05, 2014 to October 03, 2014 more >>
  • Visualization in R with ggplot2

    ggplot, created by Hadley Wickham, is an implementation of the grammar of graphics in R.  It combines the advantages of both base and lattice graphics: conditioning and shared axes are handled automatically, while maintaining the ability to build up a plot step by step from multiple data sources. It also implements a more sophisticated multidimensional conditioning system and a consistent interface to map data to aesthetic attributes.

    ggplot won the 2006 John Chambers Award for Statistical Computing.

    Tuition: $469 (5.0 CEUs) New available dates: July 25, 2014 to August 22, 2014 more >>

Elective Courses (8 required)

  • Bayesian Statistics in R Using R and the associated R package JAGS, you will learn how to specify and run Bayesian modeling procedures using regression models for continuous, count and categorical data. Tuition: $549 (5.0 CEUs) New available dates: September 26, 2014 to October 24, 2014 more >>
  • Biostatistics in R: Clinical Trial Applications This course covers the implementation in R of statistical procedures important for the clinical trial statistician. Tuition: $549 (5.0 CEUs) New available dates: May 23, 2014 to June 20, 2014 more >>
  • Bootstrap Methods This course covers the basic theory and application of the bootstrap family of procedures, with the emphasis on applications. Tuition: $549 (5.0 CEUs) New available dates: September 19, 2014 to October 17, 2014 more >>
  • Data Mining - R The main goal of this course is to teach users how to perform data mining tasks using R. Tuition: $469 (5.0 CEUs) New available dates: June 27, 2014 to July 25, 2014 more >>
  • Introduction to Smoothing and P-spline Techniques 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: $549 (5.0 CEUs) New available dates: June 20, 2014 to July 18, 2014 more >>
  • 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: $549 (5.0 CEUs) New available dates: June 13, 2014 to July 11, 2014September 05, 2014 to October 03, 2014 more >>
  • R Programming - Advanced

    This course covers key concepts for writing advanced R code, emphasizing the design of functional and efficient code.  It will set students down the road to mastering the intricacies of R.  After completing the course, students should be able to read, understand, modify, and create complex functions to perform a variety of tasks.

    Tuition: $489 (5.0 CEUs) New available dates: May 23, 2014 to June 20, 2014January 09, 2015 to February 06, 2015 more >>
  • R Programming - Intermediate

    This course is intended for experienced data analysts looking to unlock the power of R.  It provides a systematic overview of R as a programming language, emphasizing good programming practices and the development of clear, concise code.  After completing the course, students should be able to manipulate data programmatically using R functions of their own design.

    Tuition: $489 (5.0 CEUs) New available dates: September 26, 2014 to October 24, 2014 more >>
  • Spatial Analysis Techniques in R This course will teach users how to implement spatial statistical analysis procedures using R software. Tuition: $549 (5.0 CEUs) New available dates: December 12, 2014 to January 02, 2015 more >>
  • SQL and R - Introduction to Database Queries The purpose of this course is to teach you how to extract data from a relational database using SQL, and merge it into a single file in R, so that you can perform statistical operations. Tuition: $469 (5.0 CEUs) New available dates: August 01, 2014 to August 29, 2014 more >>
  • 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; background will be provided on the use of Bioconductor.

    Tuition: $549 (5.0 CEUs) New available dates: April 18, 2014 to May 16, 2014 more >>
  • 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: $499 (5.0 CEUs) New available dates: March 20, 2015 to April 17, 2015 more >>

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From a Student Profile:

I hear IT people commenting that they’re always needing to learn new technology because things in their field evolve and change quickly. The same thing is true in analytics. New techniques are developing rapidly.

Robert Wood
Director, Advanced Analytics Group, Merkle

see the complete student profile
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