SAS Course

Lesson 1 • SAS Log and Output Interpretation

Teaches reading the SAS log to identify notes, warnings, and errors. Accurate log interpretation is essential for debugging throughout the course.

Lesson 2 • SAS Program Structure and Syntax

Explains the fundamental rules governing SAS code, including steps, statements, and delimiters. Correct syntax habits prevent errors in all future programs.

Lesson 3 • SAS Platform Overview and Architecture

Covers SAS software history, licensing models, and system architecture. Establishes context for all subsequent programming and data management tasks.

Lesson 4 • SAS Libraries and File Management

Introduces SAS libraries as pointers to data storage locations. Students assign libraries and manage SAS datasets within the file system.

Lesson 1 • Reading SAS Datasets Directly

Explains SET and direct dataset references for reading existing SAS datasets. Builds efficiency by reusing previously created data without re-importing.

Lesson 2 • Data Validation After Import

Applies PROC FREQ, PROC MEANS, and PROC PRINT to verify imported data quality. Catches structural and content errors before analysis begins.

Lesson 3 • Importing Excel and CSV Files

Uses PROC IMPORT to bring spreadsheet and CSV data into SAS automatically. Students control import options to ensure correct variable types and names.

Lesson 4 • Reading Raw Data with DATA Step

Covers INFILE and INPUT statements for reading delimited and fixed-width text files. Forms the core skill for ingesting raw data into SAS datasets.

Lesson 1 • Advanced DATA Step Techniques

Introduces RETAIN, LAG, and BY-group processing for complex row-level calculations. These techniques handle cumulative totals, running values, and group summaries.

Lesson 2 • Combining and Appending Datasets

Teaches SET for appending and MERGE for matching datasets by key variables. Students choose the correct technique based on data structure and goals.

Lesson 3 • Subsetting and Filtering Observations

Uses WHERE, IF, and DELETE statements to control which observations enter the output dataset. Filtering early reduces processing time and dataset size.

Lesson 4 • Reshaping Data: Transpose and Arrays

Uses PROC TRANSPOSE and DATA step arrays to convert between wide and long formats. Reshaping is critical for preparing data for statistical procedures.

Lesson 5 • Creating and Modifying Variables

Covers assignment statements, arithmetic operators, and SAS functions for variable creation. Enables derivation of new analytical fields from existing data.

Lesson 1 • Detailed Statistics with PROC UNIVARIATE

Extends descriptive analysis with PROC UNIVARIATE for distribution shape, outliers, and normality tests. Provides deeper insight than PROC MEANS alone.

Lesson 2 • Summarising with PROC TABULATE

Builds multidimensional summary tables using PROC TABULATE with CLASS and VAR statements. Produces publication-ready tables with custom labels and formats.

Lesson 3 • Descriptive Statistics with PROC MEANS

Applies PROC MEANS to compute means, standard deviations, and percentiles for numeric variables. Supports group-level summaries using CLASS and BY statements.

Lesson 4 • Frequency Analysis with PROC FREQ

Uses PROC FREQ to produce one-way and two-way frequency tables with percentages. Foundational for understanding categorical variable distributions.

Lesson 1 • PROC SQL Fundamentals

Introduces PROC SQL structure, SELECT syntax, and comparison with DATA step approaches. Establishes when SQL is more efficient than equivalent DATA step code.

Lesson 2 • Subqueries and Advanced SQL Features

Applies correlated and non-correlated subqueries, and uses CREATE TABLE and INSERT. These features enable complex multi-step queries within a single PROC SQL block.

Lesson 3 • Aggregating Data with GROUP BY

Uses GROUP BY with aggregate functions to produce summary statistics within categories. HAVING filters groups after aggregation, unlike WHERE.

Lesson 4 • Joining Tables in PROC SQL

Implements inner, left, right, and full joins to combine datasets by matching keys. Students select join types based on data completeness requirements.

Lesson 1 • Generating Reports with PROC PRINT

Uses PROC PRINT options to control observation selection, variable order, and totals. Produces clean tabular reports suitable for business audiences.

Lesson 2 • Variable Labels and Dataset Attributes

Assigns descriptive labels to variables and datasets using LABEL and ATTRIB statements. Labels appear in procedure outputs, improving report clarity.

Lesson 3 • Creating Custom Formats with PROC FORMAT

Builds user-defined formats to recode values and apply meaningful labels to categories. Custom formats improve readability of outputs and reports.

Lesson 4 • SAS Formats and Informats

Explains built-in SAS formats for displaying numeric and character values in readable forms. Informats convert raw input values into SAS internal representations.

Lesson 1 • Correlation and Association Analysis

Uses PROC CORR to measure linear relationships between numeric variables. Correlation results guide variable selection for regression modelling.

Lesson 2 • Analysis of Variance with PROC ANOVA

Conducts one-way and two-way ANOVA to compare means across multiple groups. Post-hoc tests identify which group pairs differ significantly.

Lesson 3 • Logistic Regression with PROC LOGISTIC

Models binary and ordinal outcomes using PROC LOGISTIC with odds ratio interpretation. Covers model fit statistics and classification table evaluation.

Lesson 4 • Simple and Multiple Linear Regression

Builds regression models with PROC REG to predict continuous outcomes from predictors. Model diagnostics assess fit, multicollinearity, and residual behaviour.

Lesson 1 • Writing and Calling SAS Macros

Defines reusable macro programs with %MACRO and %MEND, accepting parameters. Macro calls replace repetitive code blocks with a single parameterised invocation.

Lesson 2 • Macro Conditional Logic and Loops

Uses %IF-%THEN-%ELSE and %DO loops to control macro execution flow. Conditional macros adapt program behaviour based on parameter values or data conditions.

Lesson 3 • Macro Quoting and Special Characters

Applies macro quoting functions to handle special characters and prevent premature resolution. Correct quoting prevents syntax errors in complex macro programs.

Lesson 4 • Debugging and Optimising Macros

Uses MPRINT, MLOGIC, and SYMBOLGEN options to trace macro execution and resolve errors. Optimisation techniques reduce redundant macro calls and improve runtime.