About Course

Day 1: Introduction to SAS and Basic Data Handling

• Morning Session:

  • Overview of SAS and its applications
  • Installing SAS software (if not already done)
  • Introduction to SAS environment (SAS Studio or SAS Enterprise Guide)
  • SAS syntax and structure

• Afternoon Session:

  • Working with SAS datasets
  • Reading and importing data (CSV, Excel, etc.)
  • Data manipulation: sorting, merging, subsetting

Day 2: Data Cleaning and Transformation

• Morning Session:

  • Data quality assessment and cleaning
  • Handling missing values
  • Creating new variables

• Afternoon Session:

  • Basic descriptive statistics in SAS
  • Data visualization using SAS
  • Exporting data from SAS

Day 3: Statistical Analysis with SAS

• Morning Session:

  • Introduction to statistical analysis in SAS
  • Descriptive statistics (PROC MEANS, PROC UNIVARIATE)
  • Exploratory data analysis (PROC FREQ, PROC CORR)

• Afternoon Session:

  • Hypothesis testing (PROC TTEST, PROC ANOVA)
  • Chi-squared tests (PROC FREQ)
  • Non-parametric tests (PROC NPAR1WAY)

Day 4: Regression Analysis and Modeling

• Morning Session:

  • Linear regression analysis (PROC REG)
  • Logistic regression analysis (PROC LOGISTIC)
  • Model selection and interpretation

• Afternoon Session:

  • Time series analysis (PROC TIMESERIES)
  • Survival analysis (PROC LIFETEST)
  • Introduction to machine learning in SAS (PROC HPML)

Day 5: Advanced Topics and Final Projects

• Morning Session:

  • Data mining and clustering (PROC FASTCLUS)
  • Text mining (PROC TEXTMINER)
  • Introduction to SAS macros

• Afternoon Session:

  • Final projects: Students work on small data analysis projects using SAS
  • Presentation of projects and discussion