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.
Key Facts
Degree
Participation without an exam:
Certificate of Enrollment
Participation with an exam:
ECTS CertificateProgram start
1 October 2026
Duration
5 months
Place of study
Online
Program type
part time
Credit points
5 ECTS
Lecture language
English
Tuition fee
€1,725
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
Our course is designed for:
- Data scientists and analysts in healthcare and life sciences who want to work with clinical data, electronic health records (EHRs), and real-world datasets using R.
- Clinical research professionals who aim to strengthen their data analysis skills and gain hands-on experience with modern data science tools.
- Healthcare professionals and researchers interested in leveraging clinical data for evidence generation, decision-making, and innovation.
Content & qualifications
This module introduces you to the analysis of clinical data using R, with a focus on electronic health records (EHRs) and real-world datasets. You will learn how to manage, analyze, and interpret complex clinical data to generate meaningful insights for research and practice.
What you will cover
- Introduction to R for clinical data analysis
- Data structures and data management in R
- Working with electronic health records (EHRs)
- Data cleaning, transformation, and preprocessing
- Exploratory data analysis and visualization
- Statistical analysis using R
- Reproducible research workflows
- Ethical and regulatory considerations in handling clinical data
What you will be able to do
After completion, you will be able to:
- Use R to manage and analyze clinical datasets
- Work with real-world data, including EHRs
- Clean, transform, and prepare data for analysis
- Perform exploratory and statistical analyses
- Create reproducible workflows and visualizations
- Interpret results and communicate findings effectively
How you will learn
- Hands-on coding sessions in R
- Applied case studies using real-world datasets
- Interactive workshops and exercises
- Guided projects and assignments
- Discussions with faculty and peers
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.
Personal advice
FAQs
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.
