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Statistical Software Programming Training Program


Industries where statistical software can be founded:

  • Clinical Research
  • Automotive
  • Banking
  • Financial Services/Insurance
  • Government & Education
  • Healthcare
  • Life Sciences
  • Manufacturing
  • Media/Entertainment
  • Pharmaceutical
  • Retail
  • Telecommunication
  • Etc.

This training  program can  help you gain appropriate knowledge and practical skills in order to apply for the position of Statistical Software Programmer.  Upon completion of this  program and after passing on-line final exams you will receive, by mail, a diploma stating your new qualifications.

 

Statistical Software Programming - Course Outlines

  • Session 1 Introduction
  • Session 2 Briefing
    1. The company and the software
    2. Statistical software V8 On Windows environment
    3. Simple programs
  • Session 3 Basic concepts
    • Sessions, steps, temporary/permanent dataset, dataset name, observations, variables, naming conventions, char, numeric, date, ending comments, documentations, options for sessions/steps/statements etc.
  • Session 4 Data Input
    1. Input methods: list, column, formatted, named, and mixed, default delimiter
    2. Column pointer, line pointer, repeated read, implicit data step loop
  • Session 5 Data Input
    1. Length, informat statements
    2. External files, filename, infile
  • Session 6 More Data Input
    1. Input modifiers, infile options
    2. Do loop input, output
  • Session 7 More Data Input
    1. Proc import
    2. Import wizard
  • Session 8 Simple Output
    1. Proc print, sort, contents, format
    2. Proc mean, summary, unvariate, freq
    3. Proc report
  • Session 9 Language elements
    1. Operators, expressions
    2. Functions for date, numeric, char
  • Session 10 Language elements
    1. More functions
    2. Conditional statements if, where, select
  • Session 11 Objectives:
    1. Sort, First./Last., and retain
    2. Array and loop
    3. Variable regrouping
  • Session 12 Objectives: Dataset combination
    1. Stacking with set, append
    2. Interleaving with set by
  • Session 13 Dataset combination
    1. Merge
    2. Update master set with transaction
  • Session 14 Data cleaning
    1. Identify dirty data with proc freq
    2. Identify dirty data with proc mean (min, max), proc univarite (extreme values)
    3. Identify dirty char data with char functions
  • Session 15 Dataset transformation
    1. By proc transform
    2. By array
  • Session 16 Longitudinal data samples
    1. With lag, dif
    2. With First./ and retain
    3. With multiple set statement
  • Session 17 Combining summary with individual observations
  • Session 18 Introductory to Proc SQL
    1. Create table
    2. Insert
    3. Update
    4. Select
  • Session 19 Introductory to Proc SQL
    1. More select
    2. Dataset combination with SQL vs. with data step
  • Session 20 Macro by sample
    1. Macro variable
    2. Macros programming
    3. Macro functions
  • Session 21 More Macro
  • Session 22 Summary report Proc tabulate
  • Session 23 Summary report
    • Proc report
  • Session 24 ODS
    • Sample of ODS
  • Session 25 Efficiency
    1. SAS data step compilation and execution
    2. Efficient programming
  • Session 26 Common program errors
  • Session 27 Samples of statistics procedures
    1. Ttest
    2. Anova
  • Session 28 Samples of statistics procedures
    1. Corr
    2. Reg
  • Session 29 Samples of Drawings
    1. Plot
    2. Chart
  • Session 30 Data security in SAS
    1. Password
    2. Lock
    3. Encryption
  • Session 31 Data security in SAS Continued
    1. Integrity constrains
    2. Audit trail
    3. E-mail automation
  • FINAL EXAM