Statistical
Software Programming Training
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.
For
prices and registration please contact us at info@kriger.com
or give us a call
at
(866) 757-9791 (USA and Canada) or + 1(416) 630-0038
(Internationally)
To
register on-line please click
Register
Please
note that all the payments are secure.
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
For
prices and registration please contact us at info@kriger.com
or give us a call
at
(866) 757-9791 (USA and Canada) or + 1(416) 630-0038
(Internationally)
To
register on-line please click
Register
Please
note that all the payments are secure.
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