Format: Live instructor-led online training via Zoom / Microsoft Teams

SAS PROGRAMMING AND COMMERCIAL AI/LLM TRAINING-DATA TRAINING COURSE

COURSE OVERVIEW

The SAS Programming and Commercial AI/LLM Training-Data Training Course is a comprehensive practical programme designed to equip SAS programmers, data professionals, statisticians, clinical programmers, researchers, and AI data specialists with advanced skills in developing, documenting, validating, organizing, and preparing high-quality SAS programming content for commercial Artificial Intelligence (AI) and Large Language Model (LLM) training-data applications. The course covers Base SAS programming, SAS macros, PROC SQL, statistical procedures, clinical and pharmaceutical programming, data transformation, analytical workflows, code documentation, dataset construction, metadata, provenance, licensing considerations, data quality, and AI-ready programming examples. Participants will learn how professional SAS programs and analytical workflows can be structured as high-quality, traceable, reusable, and well-documented training-data assets for responsible commercial AI and LLM development.

The programme provides extensive hands-on coverage of Base SAS programming, SAS DATA steps, PROC procedures, SAS libraries, formats, informats, functions, arrays, conditional logic, loops, data manipulation, data cleaning, merging, joining, reshaping, and reporting. Participants will also develop advanced SAS programming capabilities using PROC SQL, SAS Macro Language, statistical procedures, automated workflows, reusable code libraries, parameterized programs, validation checks, and programming documentation. Particular emphasis is placed on transforming authentic analytical workflows into structured programming examples that preserve context, provenance, reproducibility, technical correctness, and instructional value while avoiding unnecessary duplication, undocumented assumptions, or unclear data lineage.

The course further addresses SAS programming for clinical trials, pharmaceutical data management, CDISC-related workflows, SDTM and ADaM concepts, Tables, Listings and Figures (TLFs), statistical analysis, reporting automation, and real-world analytical programming. Participants will explore how specialized SAS programming workflows can be represented as commercial AI/LLM training data through code-and-explanation pairs, problem-and-solution examples, annotated programming tasks, transformation examples, debugging exercises, statistical-analysis workflows, metadata-rich datasets, and quality-controlled programming repositories. The programme also examines intellectual-property considerations, licensing, permissions, provenance, de-identification, confidentiality, dataset documentation, version control, quality assurance, and responsible commercialization of programming content.

Through practical SAS programming exercises, code-generation tasks, debugging activities, PROC SQL exercises, macro programming, statistical analysis, clinical programming simulations, SDTM/ADaM examples, TLF development, dataset documentation, metadata creation, provenance tracking, and AI/LLM training-data design exercises, participants will develop commercially relevant technical capabilities. The course is particularly valuable for organizations seeking to create or license large volumes of original SAS programs and analytical workflows for AI and LLM development. By the end of the training, participants will be able to develop high-quality SAS programming examples, structure analytical workflows for machine-learning and LLM training, document data provenance, apply quality assurance procedures, organize programming datasets, and establish repeatable processes for creating commercially valuable and responsibly managed SAS training-data assets.

COURSE OBJECTIVES

By the end of the SAS Programming and Commercial AI/LLM Training-Data Training Course, participants will be able to:

1. Apply advanced Base SAS programming techniques to develop accurate, reusable, and well-documented analytical programs.
2. Develop SAS DATA step, PROC SQL, statistical procedure, reporting, and data-management workflows.
3. Use SAS Macro Language to create parameterized, reusable, scalable, and automated programming solutions.
4. Develop professional SAS programming workflows for statistical, clinical, pharmaceutical, research, and business analytics applications.
5. Apply SDTM, ADaM, TLF, and related clinical-programming concepts to structured SAS analytical workflows.
6. Design high-quality SAS programming examples and analytical workflows suitable for responsible AI and LLM training-data development.
7. Apply provenance, metadata, documentation, version control, quality assurance, and reproducibility principles to programming datasets.
8. Structure code-and-explanation pairs, programming tasks, debugging examples, analytical workflows, and other AI-ready training-data formats.
9. Understand intellectual-property, licensing, confidentiality, permissions, de-identification, and responsible commercialization considerations for training data.
10. Develop an integrated SAS programming and commercial AI/LLM training-data production workflow with appropriate technical and quality controls.

ORGANIZATIONAL BENEFITS

Organizations participating in the training will benefit from:

1. Stronger internal SAS programming and advanced analytical programming capabilities.
2. Improved ability to create large volumes of structured, high-quality SAS programming content.
3. Enhanced capacity to develop commercial AI and LLM training datasets based on original analytical workflows.
4. Improved code quality, documentation, reproducibility, metadata, and programming standards.
5. Greater capacity to develop Base SAS, SAS Macro, PROC SQL, statistical, clinical, and pharmaceutical programming assets.
6. Improved data provenance, version control, quality assurance, and training-data governance.
7. Reduced risks associated with poorly documented, duplicated, unauthorized, or low-quality training content.
8. Enhanced ability to transform specialized analytical expertise into reusable commercial training-data assets.
9. Improved collaboration among SAS programmers, statisticians, data scientists, clinical programmers, and AI data teams.
10. Development of sustainable organizational capabilities for responsible AI/LLM training-data production and commercialization.

TARGET PARTICIPANTS

This course is suitable for SAS Programmers, Senior SAS Programmers, Statistical Programmers, Clinical SAS Programmers, Pharmaceutical Programmers, Biostatisticians, Statisticians, Data Analysts, Data Scientists, Data Engineers, Research Data Managers, Clinical Data Managers, Monitoring and Evaluation Specialists, AI Data Specialists, Machine Learning Professionals, LLM Data Curators, AI Training-Data Managers, Data Annotators, Knowledge Engineers, Software Developers, Analytics Managers, Research Organizations, pharmaceutical companies, clinical research organizations, universities, consulting firms, AI companies, technology companies, and organizations developing or licensing original analytical programming content for commercial AI and LLM applications.

COURSE OUTLINE

MODULE 1: SAS PROGRAMMING FUNDAMENTALS AND PROFESSIONAL WORKFLOWS

• Introduction to SAS programming environments, SAS libraries, datasets, variables, observations, formats, and informats.
• Developing SAS DATA step programs for importing, transforming, validating, and managing datasets.
• Applying SAS functions, conditional logic, loops, arrays, and programming structures.
• Reading and writing CSV, Excel, text, database, and other structured data sources.
• Developing professional SAS coding standards, comments, documentation, naming conventions, and reusable programming practices.
• Structuring SAS programming examples as reproducible and well-documented technical training-data assets.
  Case Study: A data analytics organization develops a standardized repository of original Base SAS programming examples, with code explanations, expected outputs, metadata, and provenance documentation for internal training and AI development.

MODULE 2: ADVANCED SAS DATA MANAGEMENT AND DATA TRANSFORMATION

• Sorting, merging, joining, transposing, reshaping, filtering, aggregating, and transforming SAS datasets.
• Handling missing data, duplicates, inconsistent formats, invalid observations, and data-quality issues.
• Developing SAS programs for complex data integration and analytical dataset preparation.
• Applying PROC SORT, PROC TRANSPOSE, PROC FORMAT, PROC CONTENTS, PROC DATASETS, and related procedures.
• Developing validation checks and automated quality-control procedures for SAS datasets.
• Creating structured transformation examples that demonstrate data-processing logic for AI/LLM training.
  Case Study: A research organization transforms multiple raw datasets into a standardized analytical dataset and documents every SAS transformation step for reproducibility and future model-training applications.

MODULE 3: PROC SQL AND DATABASE-DRIVEN SAS PROGRAMMING

• Introduction to PROC SQL and relational database concepts within SAS environments.
• Creating SQL queries for filtering, aggregation, grouping, subqueries, joins, and calculated variables.
• Combining SAS datasets and database tables using SQL-based data integration techniques.
• Developing views, reusable SQL queries, and automated database-reporting workflows.
• Applying SQL validation and data-quality checks to complex analytical datasets.
• Structuring SQL programming examples with clear problem statements, solutions, explanations, metadata, and expected outputs.
  Case Study: A commercial analytics team creates a library of original PROC SQL examples covering customer, financial, operational, and research datasets for structured technical learning and AI training.

MODULE 4: SAS MACRO PROGRAMMING AND AUTOMATION

• Introduction to SAS Macro Language, macro variables, macro statements, macro functions, and macro parameters.
• Developing reusable macro programs for repetitive data-processing and reporting tasks.
• Creating parameterized SAS workflows for scalable analytical programming.
• Automating dataset creation, statistical procedures, reports, quality checks, and file generation.
• Debugging and validating SAS macros while maintaining readable and maintainable code.
• Developing documented macro-programming examples suitable for high-quality AI/LLM training datasets.
  Case Study: A statistical programming team develops parameterized SAS macros for generating standardized analytical reports across multiple datasets while documenting the programming logic for reuse.

MODULE 5: SAS STATISTICAL PROGRAMMING AND DATA ANALYSIS

• Introduction to statistical programming using SAS procedures for descriptive and inferential analysis.
• Applying PROC MEANS, PROC SUMMARY, PROC FREQ, PROC UNIVARIATE, and related statistical procedures.
• Developing regression, correlation, analysis-of-variance, and other appropriate statistical workflows.
• Producing analytical tables, statistical summaries, and reproducible analytical outputs.
• Validating statistical results and documenting assumptions, parameters, and expected outputs.
• Creating problem-based statistical SAS programming examples for AI/LLM training and professional learning.
  Case Study: A research organization creates a collection of original SAS statistical programming tasks showing how analysts transform research questions into reproducible statistical workflows.

MODULE 6: CLINICAL AND PHARMACEUTICAL SAS PROGRAMMING

• Introduction to SAS programming applications in clinical trials and pharmaceutical research.
• Clinical data management, patient-level datasets, treatment variables, visit structures, and analysis populations.
• Programming descriptive statistics, safety summaries, efficacy analyses, and clinical reporting outputs.
• Understanding clinical-programming workflows, validation, documentation, traceability, and reproducibility.
• Developing controlled examples using appropriately de-identified or synthetic clinical datasets.
• Structuring clinical SAS programming workflows as high-quality technical training examples with clear provenance.
  Case Study: A clinical research organization develops a controlled repository of synthetic clinical-programming examples covering data preparation, analysis, validation, and reporting workflows.

MODULE 7: SDTM, ADaM AND CLINICAL DATA STANDARDS

• Introduction to CDISC concepts and the role of SDTM and ADaM in clinical data standardization.
• Understanding SDTM domains, variables, metadata, controlled terminology, and standardized data structures.
• Understanding ADaM analysis datasets and the relationship between source data, SDTM, and statistical analysis.
• Developing SAS programming workflows for standardized clinical datasets.
• Applying traceability, metadata documentation, validation, and quality assurance principles.
• Creating educational SDTM/ADaM programming examples using authorized, synthetic, or appropriately licensed datasets.
  Case Study: A clinical-programming team develops a synthetic SDTM-to-ADaM workflow and documents the transformation logic, metadata, validation checks, and programming outputs.

MODULE 8: TABLES, LISTINGS AND FIGURES (TLFs)

• Principles of clinical and statistical Tables, Listings, and Figures development.
• Using SAS procedures to create professional analytical tables and statistical summaries.
• Developing listings with appropriate formatting, sorting, grouping, and variable presentation.
• Creating charts and figures for clinical, pharmaceutical, research, and business analytics.
• Applying reproducibility, validation, formatting standards, and output-quality checks.
• Creating TLF programming examples with problem descriptions, SAS code, expected outputs, and technical explanations.
  Case Study: A statistical programming team creates a controlled library of synthetic TLF examples covering demographics, safety summaries, efficacy analyses, listings, and analytical figures.

MODULE 9: SAS CODE QUALITY, VALIDATION, DEBUGGING AND REPRODUCIBILITY

• Applying SAS programming standards for readability, consistency, maintainability, and reusability.
• Identifying syntax errors, logical errors, data errors, performance issues, and unexpected analytical outputs.
• Developing code-review procedures, peer review, validation checks, and testing frameworks.
• Comparing expected and actual outputs and documenting discrepancies and corrective actions.
• Applying version control, change management, program documentation, and reproducibility principles.
• Creating high-quality debugging and code-correction examples for AI/LLM training datasets.
  Case Study: A SAS programming team develops a curated collection of original debugging examples showing defective code, error diagnosis, corrected code, explanations, and validation results.

MODULE 10: COMMERCIAL AI/LLM TRAINING-DATA DESIGN FOR SAS PROGRAMMING

• Understanding AI, machine learning, generative AI, Large Language Models, and programming-focused training data.
• Designing code-and-explanation pairs, problem-solution datasets, instruction-following examples, debugging datasets, and analytical reasoning examples.
• Structuring SAS programming content with prompts, source code, expected outputs, explanations, metadata, and validation information.
• Creating diverse and representative SAS training examples across Base SAS, Macro, PROC SQL, statistics, clinical programming, SDTM, ADaM, and TLF workflows.
• Applying quality-control criteria including correctness, completeness, clarity, consistency, reproducibility, and technical relevance.
• Developing training-data schemas that support efficient dataset ingestion, annotation, versioning, and model-development workflows.
  Case Study: An AI company partners with a SAS programming organization to create a structured dataset containing original SAS programming tasks, solutions, explanations, test cases, metadata, and quality-assurance results.

MODULE 11: PROVENANCE, LICENSING, DATA GOVERNANCE AND RESPONSIBLE COMMERCIALIZATION

• Understanding data provenance, source documentation, ownership, permissions, licensing, and chain of custody.
• Distinguishing original programming content from third-party, restricted, confidential, proprietary, or otherwise controlled materials.
• Applying confidentiality, privacy, de-identification, synthetic-data, and sensitive-information management principles.
• Developing metadata documenting source, creator, date, version, licensing status, permitted use, and transformation history.
• Establishing governance procedures for reviewing and approving SAS programming content before commercial AI/LLM use.
• Developing responsible commercialization strategies for authorized and properly licensed training-data assets.
  Case Study: A data partnership team establishes a governance framework requiring every SAS programming asset to have documented ownership, provenance, permissions, version history, and quality-assurance status before inclusion in a commercial AI training dataset.

MODULE 12: INTEGRATED SAS PROGRAMMING AND AI/LLM DATA PRODUCTION PROJECT

• Designing an end-to-end workflow for creating original SAS programming content for commercial AI/LLM training.
• Developing representative SAS tasks covering Base SAS, DATA step, PROC SQL, Macro, statistical procedures, and specialized analytical workflows.
• Creating code, explanations, expected outputs, metadata, provenance records, validation results, and quality-control documentation.
• Applying technical review, data-quality assurance, licensing review, confidentiality checks, and dataset acceptance criteria.
• Organizing programming assets into structured datasets suitable for controlled AI/LLM training-data pipelines.
• Presenting a complete SAS programming training-data production framework covering creation, validation, governance, versioning, licensing, and responsible commercialization.
  Case Study: Participants develop a prototype commercial SAS/LLM training-data repository containing original programming tasks, validated solutions, explanations, metadata, provenance documentation, and quality-control records.

PRACTICAL TRAINING METHODOLOGY

The training will use instructor-led presentations, live SAS programming demonstrations, practical coding exercises, PROC SQL laboratories, macro programming exercises, statistical analysis assignments, clinical-programming simulations, SDTM and ADaM exercises, TLF development, debugging activities, code-review sessions, dataset-design workshops, metadata exercises, provenance documentation, and AI/LLM training-data structuring activities. Participants will work with original, synthetic, public-domain, or appropriately licensed datasets and programming examples. The methodology emphasizes technical correctness, reproducibility, documentation, data quality, intellectual-property awareness, responsible data governance, and practical commercial application.

GENERAL INFORMATION

1. Customized Training: All our courses can be tailored to meet the specific needs of participants.
2. Language Proficiency: Participants should have a good command of the English language.
3. Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.
4. Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).
5. Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.
6. Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.
7. Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.
8. Additional Services: Accommodation, pickup services, flight booking, and visa processing arrangements are available upon request at discounted rates.
9. Equipment: Tablets and laptops can be provided to participants at an additional cost.
10. Post-Training Support: We offer one year of free consultation and coaching after the course.
11. Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.
12. Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.
13. Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.
14. Website: Visit our website at www.fdc-k.org for more information.