Clinical Data Lab: R, EHRs & Applied Biostatistics
Apply theoretical knowledge to real-world clinical research challenges.
This module bridges theory and practice by equipping participants with hands-on skills in applied biostatistics, clinical data analysis, and research data management. Working directly with real-world datasets, students use R/RStudio to analyze, interpret, and present research findings in a professional context.
The focus is on transforming complex healthcare data into meaningful evidence. Participants gain practical experience in handling electronic health records (EHRs), understanding data structures, and applying foundational database and SQL skills for efficient clinical data workflows.
As part of the modular certificate structure, this course can be taken individually or applied toward the Master's program “Clinical Reserach (M.Sc.)”at DIU.
Course Content
- Applied biostatistics: descriptive, inferential, and predictive analytics
- Propensity score methods for observational research
- Principles of data visualization in clinical research
- R/RStudio hands-on training:
- Data import and export
- Data transformation and cleaning
- R Markdown scripting
- Interactive reporting
- Clinical data sources and electronic health records (EHRs)
- Medical terminologies and structured data extraction
- Introduction to database systems
- SQL fundamentals for clinical research
Qualifications
Upon successful completion, participants will be able to:
- Apply statistical methods to real-world clinical datasets
- Conduct and interpret descriptive and inferential analyses
- Implement predictive analytics approaches
- Use R/RStudio confidently for research workflows
- Develop reproducible analytical reports
- Understand EHR data structures and terminology systems
- Apply basic SQL queries for data extraction and management
Graduates strengthen their qualifications for roles in clinical research, health data analytics, medical research coordination, and evidence-based healthcare management.
Teaching & Learning Format
- Practice-oriented, case-based learning
- Supervised hands-on data analysis sessions
- Guided R/RStudio workshops
- Real-world datasets and clinical research scenarios
- Interactive discussions and applied exercises
The module combines academic rigor with practical implementation to ensure immediate transferability to professional environments.
Admission Requirements:
- A suitable academic and/or professional background is recommended.
- In general, you can enroll in an individual course even if you do not meet the admission requirements for the full Master’s degree program.
When requesting participation, please briefly outline your educational pathway and professional background. Based on your profile, we will either invite you to a study advisory appointment or—if direct entry is possible—send you a module agreement for enrollment.
Overview
Degree:
Certificate of Completion upon
Successful Completion of the
Module ExamProgram start:
11 April 2026
Location:
Online
Duration:
5 Month
Credit points:
5 ECTS
Program type:
Part time
Tuition fees:
1.725,00 EUR
Program language:
English
Scientific Director
Dr. Ben M. W. Illigens, MD, MBI
Instructor in Neurology, Beth Israel Deaconess Medical Center Boston, MA United States and Director, German Sites Development Principles and Practice of Clinical Research Harvard T.H. Chan School of Public Health
Managing Director, CEO, D4L data4life gGmbH
Personal advice
Officially Recognized & Accredited
Frequently Asked Questions
Who is this module for?
This module is designed for clinical research professionals, healthcare and life sciences specialists, and anyone who wants to build practical skills in applied biostatistics, R/RStudio, and clinical data workflows—including EHR-based research.
Can I take this module without enrolling in a full Master’s program?
Yes. In general, you can enroll in this course without meeting the formal admission requirements for the full Master’s degree program. The module is individually bookable.
What will I learn in this module?
You’ll learn how to analyze and communicate results using applied biostatistics, propensity score methods, and data visualization principles—supported by hands-on training in R/RStudio, reporting with R Markdown, and an introduction to databases and SQL for clinical research.
Do I need prior programming knowledge?
Not necessarily. Prior experience with data analysis is helpful, but the module is designed to guide you step by step through practical R/RStudio workflows using real datasets.
Are there admission requirements?
A suitable academic and/or professional background is recommended. When requesting participation, please briefly describe your educational pathway and professional background. Depending on your profile, we will either invite you to a study advisory appointment or—if direct entry is possible—send you a module agreement for enrollment. Request course registration now.
What learning format can I expect?
Expect a practice-oriented format with guided exercises, real-world datasets, hands-on workshops in R/RStudio, and structured self-study—focused on skills you can apply immediately in clinical research settings.
What do I receive after completion?
After successful completion, you receive an official DIU certificate of completion.
How do I request participation?
Email us with a short description of your educational pathway and professional background, plus your preferred contact method (email or phone). Our Study Advisory Team will follow up with next steps and participation options. Request course registration now.