Why take this Program CliP-R (Proficiency in Clinical R Programming) ?
R Programming, an open-source tool, is extensively used for data analysis in universities. Professors and researchers in the field of medicine use R for quantitative analysis. Google and Facebook use R Programming for data exploration, model prototyping and predictive analytics. It is the main programming tool for many quantitative analysts in the finance domain. R Programming is a powerful tool with numerous applications: data import and cleaning, exploration and visualization, statistics generation and analyses and trading simulations. Thus, it is popular in varied fields such as finance, bio-science, clinical and life science, supply chain, sports, retail, marketing and manufacturing.
The two main advantages of R programming:
1. It has been designed specifically for statistical analysis; therefore, programs in R always require fewer lines of code than programs created using other analytical tools.
2.It is an open-source tool; therefore, it benefits from constant upgrades, improvements, enhancements and contributions from a global community of passionate users and developers.
Using R in a Clinical and Regulatory Environment
The Office of Biostatistics, US FDA, uses R on a daily basis. FDA scientists have written R packages that can be used by other scientists (FDA or non-FDA). It is a popular misconception that the FDA mandates or endorses the use of a specific software package for statistical analysis â this is not true. The FDA in fact encourages using anything that can be validated for correctness, including R. 21 CFR Part 11 regulates electronic records and their storage, not the software that is used to generate reports. No regulation restricts the use of open-source software (including R) at the FDA. Meanwhile, in the pharmaceutical and clinical research domain, SASÂ® Programming enjoys immense popularity and is widely used, although regulations do not specify or endorse the use of either SASÂ® or R programming software for statistical analysis and generation of reports and graphs. Owing to the advantages and ease of use of both these tools, various organizations are creating talent pools that are trained in both SASÂ® and R Programming. Considering this, CliPLab launched âCliP-Râ, a new course in Clinical R Programming and welcomes candidates to join this course. At CliPLab, we are aware that R requires a data analysis background, and therefore, it must be taught in a manner different from Java or Python. Hence, we use relevant clinical datasets as examples and exercises in the CliP-R module.
Duration: 6 weeks / 30 hours / Weekend Program
Eligibility: Candidates are preferred from:
- Pharmacy (M. Pharm., B.Pharm.,)
- Life sciences (M.Sc., PhD – Biotechnology, Bioinformatics, Microbiology)
- SAS Programmers / Biostatisticians / Data Science professionals
- Life Science Students / Researchers / Faculties in colleges
- Specially designed for Life Sciences domain by Cytel
- a global expert in biostatistics and clinical programming
- Comprehensive and top-notch Clinical R Programming training
- Experienced trainers, who are proficient clinical R programmers with exposure to global clients, including large pharmaceutical & biotech companies
- Coverage of fundamentals of clinical operations and basic statistics
- Practical exposure to R Programming using clinical data sets and case studies
- Regular assessments to improve and evaluate performance
- For batch schedule & course fee kindly contact us at respective locations or email us at firstname.lastname@example.org
Mode of Payment:Â
- Payment can be done by Cheque / DD / Credit Card / Debit card / Online Transfer
- Introduction to R
- Basics of R Data
- Structures Reading Data in R
- Writing R Functions
- Control Statements
- Group Manipulation
- Data Reshaping
- Manipulating Strings
- Probability Distributions
- Basic Statistics
- R Graphics
- Report Generation