Modern SAS for Statistical Analytics

From messy data to documented, reproducible, verified results — the professional SAS workflow

Course identity hero for Modern SAS for Statistical Analytics — a banded green malachite crystal surrounded by SAS analytics-workflow graphics including an import-validate-analyze-report-decide pipeline, a DATA step example, a SAS project folder tree, a PROC MEANS table, and a PROC SGPLOT scatterplot, with the course title.

SAS is not just a language for running procedures. It is a professional analytics environment — a place to move data from raw and messy to clean, validated, analyzed, and reported, where reliability, traceability, and clear reporting matter as much as the answer itself. This course teaches you to work in that environment the way an analyst does: read the log, check the row counts, confirm the types, validate before you trust the output, and say plainly what an analysis does and does not show.

What this course is

This is a course in applied statistical analytics with SAS, built around one idea: real analytic work is a workflow, not a single command. You begin with data that is messy — duplicated rows, a typo where an age reads 199, a date imported as text, blank values, impossible measurements — and you move it, step by documented step, to a result another person could rerun and verify. Along the way you set up a SAS project and libraries, write DATA steps, import and clean and validate data, join tables with PROC SQL, summarize with PROC MEANS and PROC FREQ, produce report-ready output through ODS, run the core statistical procedures, reshape and merge, simulate, and finally assemble the whole pipeline into one reproducible analysis report.

The throughline on every page is that workflow, and its soul is reliability, traceability, and verification. SAS is a deep system, but this course keeps syntax in service of a reliable, traceable analysis — never the other way around. After every program you read the log (the NOTE/WARNING/ERROR lines that are SAS’s primary output), check the counts, confirm a variable is the type you expect, and ask the recurring question of the course: “would someone else be able to understand, rerun, and verify this?” That habit, more than any one procedure, is what the course is trying to build.

This is a professional SAS analytics-workflow course. It is not a generic programming course, not a generic intro-statistics course, and not a SAS-syntax reference. The statistics it uses — a t-test, an ANOVA, a regression, a logistic model — are tools in the workflow, introduced where the analysis needs them and always interpreted responsibly. No prior SAS experience is assumed; the course builds SAS up gradually from the environment itself.

What you will be able to do

By the end of the term, you should be able to:

  • Describe SAS as a professional analytics environment and explain what an analytics workflow is — from messy data to a documented, reproducible result.
  • Open and navigate the SAS environment, organize an analytics project into folders, and assign a libname library pointing at permanent data.
  • Read a SAS log as primary output — distinguishing NOTE, WARNING, and ERROR, and checking the observation count after every step.
  • Work with libraries, datasets, observations, and variables, and control display with labels, formats, and informats (reading a character date like "08/24/2026" into a real SAS date).
  • Write DATA step logic — IF/THEN, subsetting, derived variables — and handle missing values correctly, including the classic if x < 5 includes-missing trap.
  • Import external data, then clean and validate it: detect duplicates, type problems, and impossible values; document the move from raw rows to a clean analysis dataset.
  • Distinguish character vs numeric variables and recognize why it is load-bearing — a number stored as character blocks PROC MEANS until a put/input conversion fixes it.
  • Join tables with PROC SQL (inner vs left join) and with the DATA step MERGE, and check the output row count against what you expect every time.
  • Summarize data with PROC MEANS, PROC FREQ, and PROC UNIVARIATE, reading proportions, counts, and N vs NMISS correctly.
  • Produce report-ready ODS output (HTML, PDF, RTF) and graphics with PROC SGPLOT/SGPANEL.
  • Run and interpret the core statistical procedures — PROC TTEST, PROC GLM/ANOVA, PROC REG, and PROC LOGISTIC — stating each procedure’s assumptions before its conclusions.
  • Keep the interpretation honest: distinguish statistical significance from practical importance, an odds ratio from a risk ratio, and an association from a cause in observational data.
  • Reshape and merge data with PROC TRANSPOSE and MERGE, validating that the row count is what the grain of the data implies.
  • Use simulation — call streaminit(...), RAND, and PROC SURVEYSELECT — to study power, error rates, and sampling variability, with a fixed seed for reproducibility.
  • Assemble a complete analysis as one reproducible program with a verification-notes section, and write up what the analysis does and does not show.
  • Apply the recurring verification discipline — what the log should say, the row-count check, the type check, the range sanity check — to your own work.

These goals correspond to the 16 outcomes in the syllabus, summarized here for orientation. The authoritative, graded versions of these goals — with their checkpoints and due dates — live in Blackboard (the LMS).

How the site is organized

This public site has three working areas, reachable from the sidebar:

  • Notes — the weekly instructional spine, 15 weeks following the workflow from “what SAS is for” through importing, cleaning, joining, summarizing, the statistical procedures, simulation, and a reproducible report. Each week poses a workflow question, develops the idea, shows the SAS code with its synthetic log and output, names a common mistake, and offers ungraded self-checks. Start here.
  • Labs — the hands-on strand. Four short labs (companions to weeks 4, 6, 10, and 13) walk you through building and validating a DATA step, joining tables and checking relationships, fitting a regression with diagnostics, and running a simulation. The SAS code is shown for study; you run it in your own SAS environment.
  • Resources — a SAS workflow glossary, a side-by-side procedure reference, a log-and- verification guide, and a SAS academic-access and project-organization page. Keep these open while you read.

Software

The course uses a course-designated SAS environment — SAS Studio via SAS OnDemand for Academics, SAS Viya for Learners, SAS Skill Builder for Students, or a university-supported SAS installation. The exact provisioned environment is a syllabus placeholder for now (set up when available); the SAS academic-access page explains how to obtain access once it is confirmed.

On this site, SAS is shown as static, syntax-highlighted code in plain sas code blocks and is not executed in place. SAS is proprietary, and no SAS was run in this build. Every SAS log excerpt and every PROC output table you see is a hand-authored synthetic listing — drafted “as if run” for teaching — so the site renders deterministically and SAS-free. You run the real programs yourself in your own SAS environment, which is exactly how you will work on the labs.

Source and attribution

These notes are the course’s own synthesis, grounded in but not copied from three tiers of sources:

  • Primary spine (Tier 1) — instructor-original SAS workflow notes. The notes, examples, SAS code, and the recurring synthetic study on this site are instructor-original. They lead the sequence, the terminology, the example ecology, and the rigor. This is the course’s own material, written for it.
  • Reference (Tier 3, proprietary) — official SAS documentation. The SAS documentation (documentation.sas.com, support.sas.com) is copyrighted by SAS Institute Inc., all rights reserved. The notes link and cite specific doc pages as reading pointers, in the course’s own words, with at most a short attributed quote — they never reproduce or closely paraphrase SAS-doc prose, examples, listings, tables, or figures, and the course never becomes a mirror of the SAS reference. Learning to read the documentation is itself a course skill.
  • Background (Tier 2, OER) — Introduction to Modern Statistics (IMS). Introduction to Modern Statistics, 2nd ed. (Çetinkaya-Rundel & Hardin) — free at openintro-ims.netlify.app, License: CC BY-SA 3.0 — is used only for statistical background (a review of t-tests, ANOVA, regression, logistic regression, and responsible interpretation) on the statistical-procedure weeks, never as a SAS syntax manual. No IMS prose, examples, figures, or solutions are reproduced.

All example data on this site are synthetic, with the seed set (call streaminit(20260824)); the prose and SAS code are original. SAS® and all SAS Institute product names are the property of SAS Institute Inc.

A note on what is public here

Everything on this site is public and ungraded — study material only. You will not find graded prompts, answer keys, rubrics, point values, or due dates here. The operational side of the course — graded SAS workflow checkpoints, skill checks, homework, analytics labs, the midterm practical, the final analytics project, and the final practical, along with all dates and submissions — lives in Blackboard (the LMS), which is authoritative. If this site and Blackboard ever disagree, follow Blackboard.

NoteDraft course site

This site is a draft course site, not a finished release. No SAS was run: every SAS program, log excerpt, and PROC output table is hand-authored and synthetic (the wellness-program study, seed streaminit(20260824)), drafted “as if run” and provisional pending human/SAS-run review. The statistical results are invented for teaching, the study is synthetic and observational (not real health data, and not causal), and no accessibility-compliance claim is made. Treat it as a work in progress rather than the final word.