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Affordable in-class course for SAS Programming with Data Management practice


The last information sessions et inscription for the in-class course for SAS programming for medical statistics will be held on Wednesday, Feb. 4, 2015 and Friday , Feb 6, 2015 from 5:00 pm to 7:00 pm at 6767 Cote-des-neiges, room 601-1, on the 6-th floor. SAS is a must-have for people targeting jobs in epidemiology, pharmacovigilence, biostatistics and medical writing (protocol design). Bring your laptop to configue it if you consider taking the course.

In line with our mission to assist the career reorientation of highly educated professionals, a special introductory price of $800 is offered for this first course only. The Data management internship is included in this course.

To pay smaller amounts, a deferred payments option is offered in the frame of our Accessibility program to candidates without revenue and newcomers to allow paying in 7 bi-weekly installments.

Note: With all other providers, each one of the included modules costs 3 time more, the SAS programming training material of about 500 pages per module is presented in much shorter time, and they don’t offer any practical work

For more details please contact Mr Mamadou Dakouo, director of the program to <mamadou.d@cra-school.com> or directly to (514) 553-1300. Please pass the information to friends and other interested persons. The next course will be already at the regular price.

—2 weeks : (20 hours)

Learn how to

  • navigate the SAS windowing environment
  • navigate the SAS Enterprise Guide programming environment
  • read various types of data into SAS data sets
  • create SAS variables and subset data
  • combine SAS data sets
  • create and enhance listing and summary reports
  • validate SAS data sets.
—2 weeks : (20 hours)

Learn how to

  • control SAS data set input and output
  • combine SAS data sets
  • summarize, read, and write different types of data
  • perform DO loop and SAS array processing
  • transform character, numeric, and date variables.
—2 weeks: (20 hours)

Learn how to

  • generate descriptive statistics and explore data with graphs
  • perform analysis of variance and apply multiple comparison techniques
  • perform linear regression and assess the assumptions
  • use regression model selection techniques to aid in the choice of predictor variables in multiple regression
  • use diagnostic statistics to assess statistical assumptions and identify potential outliers in multiple regression
  • use chi-square statistics to detect associations among categorical variables
  • fit a multiple logistic regression model.