Clinical SAS Progarmming
Earn certification, gain practical skills, and advance your career with job placement assistance and networking opportunities. Join our vibrant learning community and stay ahead in the world of data-driven decision making
Description
This comprehensive course is designed to equip participants with the essential skills and knowledge required to excel in the field of Data Analysis and programming. From foundational concepts to advanced techniques, participants will learn how to leverage various tools and programming languages to analyze data, derive insights, and make informed decisions.
Throughout the course, participants will delve into the fundamentals of Data Analysis, gaining hands-on experience with industry-leading tools and programming languages such as Python, SQL, and Tableau. From data manipulation and visualization to machine learning and big data analysis, this course covers a wide array of topics to equip participants with the skills and knowledge needed to excel in the rapidly evolving field of Data Analysis.
Whether you're a beginner looking to break into the field or a seasoned professional aiming to upskill, Jagan will help you achieve your goals and unlock your full potential in the world of Data Analysis and programming.
What you'll learn
Master essential Data Analysis techniques for informed decision-making.
Gain proficiency in Python programming for data manipulation and analysis.
Understand SQL fundamentals for database querying and management.
Build & test a full website design.
Create impactful visualizations using Tableau for data-driven storytelling.
Explore advanced statistical analysis and hypothesis testing methods.
Develop machine learning models for predictive analytics and pattern recognition.
Dive into big data technologies like Hadoop for scalable data processing.
Apply data cleaning and preprocessing techniques to ensure data quality.
Utilize clustering and classification algorithms for data segmentation and categorization.
Learn data visualization best practices to effectively communicate insights.
Prepare for a rewarding career in Data Analysis, business intelligence, or data science.
Requirements
- No prior programming experience required, but a willingness to learn programming concepts is beneficial.
- Basic understanding of mathematics and statistics concepts.
- Commitment to continuous learning and self-improvement in the field of Data Analysis and programming.
Course Content
Learn from industry experts with years of practical experience in data analysis, programming, and visualization.
- Day 1-3 Reading raw data, Basic SAS programming, Datatypes, Accessing data drom different sources ,Libraries, Statements, Types of input statement and modifiers
- Day 4- 5 Data Step Processing
- day 5 - 6 Data Manipulation
- Day 7 Combining SAS DataSet Concatenation, Interleaving, Merging
- Day 7 - 11 By Group Processing
- Day 12-15 Functions - Numeric Functions, Character Functions, Date and Time Functions, Nesting SAS Functions Day 15 - 20 - By Group Processing Functions - Numeric Functions, Character Functions, Date and Time Functions, Nesting SAS Functions Conditional Statements Do Loop Where Conditions Arrays
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Procedures sort, means,
summary Procedures print, Freq,
Transpose Procedures Datasets,
Printto,
Summary Procedures Tabulate,
Report,
Format Import and export procedures,
ODS Proc SQL
Macro Programming
Data Analysis Using SAS Techniques
User defined functions, Proc Format
Combining data Horizontally, Accessing observations
Data Step hash and hilter objects
Data Step Arrays
Revision and Sharing Dumps and Materials
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1.Introduction about the course
2.Introduction about each department (clinical operations, CDM, Bio-statistics and medical writing)
3.Detailed information about the Bio-Statistics (statistician, statistical programmers and SAS programmers)
4.Introduction about the Client, Regulatory bodies, Submission of the study
5.Introduction to specifications
6.Introduction of CDISC
7.Why CDISC and DATA standards
8.What are the versions of CDISC
9.Impact of CDISC Standards on Clinical Activities
10.CDISC Models
11.Study Data Tabulation Model (SDTM)
12.Analysis Dataset Models (ADAM)
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13.What is SDTM?
14.Observations and Variables in SDTM
15.Special Purpose Datasets
16.General Observation Classes in SDTM
17.SDTM Standard Domain Models
18.Creating New Domain
19.Assumptions for Domain Models
20.General Assumptions for all Domains
Models for Special Purpose Domains : DM, CO, SE AND SV
Domain Models Based on the General Observation Classes
1. Interventions - CM & EX
2. Events - AE & MH
3. Findings - LB, & PE
SDTM Mapping Programming Using SAS
SDTM Annotations on CRF
SDTM Mapping Specifications
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1.Introduction to ADAM
2.Why we need ADAM
3.ADAM naming conventions
4.ADAM Implementation
5.Fundamentals of the ADAM Standards
6.Variables in General
7.ADSL variables
8.BDS Variables
9.Real time Project on ADAM
10.ADSL, ADAE
Table Listing Figures
1.Introduction to Clinical Trail2.Summary Reports (Tables Listings and Fig)
3.Introduction about the ICH E6,E9 and E3
4.Protocol
5.CRF
6.SAP
7.Introduction about the clinical study report
8.SAS programs development, and validation.
9.Generating Summary Reports
10.Generating Listings
11.Generating Graphs
Instructor
Jagananathan
Data Analytica & SAS Expert Trainer
Jagan's passion for teaching and commitment to student success make him a standout instructor at JS Analytics. His ability to simplify complex concepts, coupled with his patient demeanor and hands-on teaching style, ensures that students feel supported every step of the way. Whether you're a beginner looking to break into the field or a seasoned professional aiming to upskill, Jagan will help you achieve your goals and unlock your full potential in the world of Data Analysis and programming.
Over the years, he expanded his expertise to include programming languages such as SAS and Python, mastering the art of data manipulation and machine learning algorithms.
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