Best Clinical SAS Training in Hyderabad — Empowering You for a Successful Career in Clinical Research.

Sclindasys provides the best clinical sas training in Hyderabad. The Clinical SAS Course at Sclindasys is designed to build expertise in statistical programming for the pharmaceutical and clinical research industry. This program delivers in-depth training on SAS programming, clinical data management, and regulatory reporting.

Whether you’re a fresh graduate in life sciences or an experienced professional seeking specialization, this course offers a perfect balance of foundational knowledge and hands-on experience. You’ll learn how to work with real clinical trial data, prepare analysis datasets, and generate tables, listings, and figures (TLFs) used in FDA submissions.

Our curriculum is aligned with current industry practices, ensuring you gain the skills employers expect from clinical SAS programmers and statisticians. By the end of the course, you’ll be well-prepared for roles in CROs, pharma companies, and healthcare analytics team

Why Choose Sclindasys?

• 100% practical, project-based training
• CDISC-compliant modules with real trial data
• Resume building, mock interviews & placement support
• Weekend and weekday batch options
• Industry connections with top CROs and pharma companies

Curriculum

At Sclindasys, our Clinical SAS curriculum is built to transform beginners into industry-ready professionals. The training combines in-depth theoretical knowledge with real-world application through hands-on exercises, clinical data projects, and SAS programming essentials. This structured and time-bound syllabus ensures a complete grasp of clinical programming fundamentals and regulatory standards used by CROs and pharmaceutical companies.

The Clinical SAS course at Sclindasys is the best clinical sas course in Hyderabad. It  is carefully designed to help learners gain practical skills used in real-world clinical research and pharmaceutical analytics. The curriculum is divided into ten key learning modules:

1) Students begin with essential reporting and utility procedures, learning how to generate clinical reports using procedures such as PROC PRINT, REPORT, TABULATE, and explore tools like PROC CONTENTS, DATASETS, FORMAT, and COMPARE for dataset management and automation.

2) The course introduces foundational SAS concepts early on, including system architecture, programming rules, naming conventions, file structures, and setting up the SAS environment. It also covers variable types, data length, and how SAS handles missing values.

3) Data manipulation and transformation come next. Learners explore ways to merge, interleave, and concatenate datasets, and use commands like SET, UPDATE, and MODIFY to manage large and complex clinical data.

4) A major portion of the training is focused on DATA step programming. This includes mastering INFILE, INPUT, DATALINES, and RUN statements, along with the use of IF-THEN logic, DO loops, and output control statements.

5) Working with grouped data is another vital skill covered in the course. Students learn to sort data, apply BY-group processing, and use FIRST. and LAST. variables to handle grouped observations for patient-based analyses.

6) Iteration techniques are introduced through array programming and loop structures. One-dimensional and multi-dimensional arrays, array references, and iterative DO loops are used to streamline repetitive tasks.

7) The course places strong emphasis on applying SAS functions. Over 100 functions across categories such as character, numeric, and date functions are practiced to manipulate and analyze clinical datasets efficiently.

8) Conditional processing and logical flow control are covered in detail. Learners use IF-THEN statements, WHERE clauses, and logical operators to filter and process data based on clinical trial conditions.

9) Accessing external data is taught through the use of LIBNAME engines, PROC SQL, PROC IMPORT and EXPORT, as well as database loading tools. Students learn how to integrate SAS with Excel, text files, and relational databases.

The training concludes with visual analytics and a capstone project. Students work on clinical datasets involving CDISC standards such as SDTM and ADaM, generate submission-ready reports, and prepare for real-world job interviews and certification.