Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research 

• Format: asynchronous online course, 50 (virtual) seats
• Dates: Full course materials will be available on May 12. The course can be completed in one week (full-time) or flexibly over up to four weeks.
• ECTS: 3
• Course Language: English

Audience:
The course is designed for researchers, public health professionals, epidemiologists, and clinicians who want to improve their R coding skills, learn modern tools in the R ecosystem such as the tidyverse and Quarto, or get started writing software in R. 

Learning Objectives:
At the end of the course, participants will have:

• Mastered the tidyverse, a set of principled tools for data science. The tidyverse is a friendly, readable, and fast set of packages intended to work well together, improve code readability, and make analyses more reproducible.
• Written dynamic documents using best practices for reproducible research using Quarto. Quarto intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. Quarto also has excellent support for citation management and formatting for journals.
• Modeled simple statistical problems using linear and logistic regression.

Prerequisites:
Basic epidemiology and statistics. Some experience with R programming will be helpful, but those new to R can take a free introductory course on DataCamp (https://www.datacamp.com/courses/free-introduction-to-r) or work through the Posit Primers (https://posit.cloud/learn/primers). 

Materials:
R is a free open-source programming language and tool for statistics. For the hands-on exercises, you can either use Posit Cloud (advantage: “plug-and-play”, no need to install anything) or download and install the most recent version of R (https://www.r-project.org/) as well as RStudio (https://rstudio.com/products/rstudio/download/).
All the course materials will be made available through a course website.

Lecturer: 
Malcolm Barrett, PhD
Malcolm Barrett is a data scientist, epidemiologist and research software engineer at Stanford University. During his PhD, he studied vision loss and quality of life and has since worked at Posit (formerly known as RStudio) and in the private sector, including Teladoc Health and Apple. He develops and contributes to open-source software related to causal inference, reproducible research, R package development, and more.

About the online format:
The course will be entirely asynchronous and participants can work entirely at their own pace. The content is equivalent to a one week, 40 hour, full-time course. Some content will be clearly marked as “optional” for those who want to take a “deeper dive”, seek additional examples and/or want to learn more advanced programming skills. Those who are less advanced can skip these sections.

• The course consists of 40 teaching units of material organized into 5 full workdays (8 hours of student investment time per workday).
• Course material will be made available on May 12 and will be available through June 9 2025. An office hour to help with access and setup will be available in the week before the course start. 
• Participants who can devote 5 full work days to the class should be able to complete all the required work by May 16. Participants may also opt to complete the course over up to four weeks. The last opportunity for students to hand in their proof of achievement is June 9.
• Live office hour support via Zoom will be provided on May 12, 19, 16 and June 2 from 4-5pm (Berlin time) by the instructor, Malcolm Barrett. Additional office hours will be provided by a teaching assistant. The office hours are optional.

Proof of achievement:
Participants will need to complete and submit 4 assignments (online) in order to receive ECTS credit. If you only want a certificate of participation, you will need to submit all assignments showing reasonable effort (i.e. at least 20 points to “pass”).

Fees and Registration:
Course Fees:

• € 510 reduced fee (students, Charité researchers, BSPH alumni)
• € 750 regular fee 

Please register using the following link: https://forms.office.com/e/xf2YstRCSE 

Advanced Epidemiologic Methods: Mastering R for Epidemiologic Research 

• Format: in-person course, 50 seats
• Dates: July 28 - August 1 2025, 9:00 – 17:00h Berlin-time, (on Friday from 9:00 - 13:00h)
• Venue: Charité Campus Mitte
• ECTS: 3
• Course Language: English

Audience:
The course is designed for researchers, public health professionals, epidemiologists, and clinicians who want to improve their R coding skills, learn modern tools in the R ecosystem such as the tidyverse and Quarto, or get started writing software in R. 

Learning Objectives:
At the end of the course, participants will have:

• Mastered the tidyverse, a set of principled tools for data science. The tidyverse is a friendly, readable, and fast set of packages intended to work well together, improve code readability, and make analyses more reproducible.
• Written dynamic documents using best practices for reproducible research using Quarto. Quarto intertwines code and text so that reports and articles are fully reproducible and exportable to PDF, HTML, Word, and more. Quarto also has excellent support for citation management and formatting for journals.
• Modeled simple statistical problems using linear and logistic regression.

Prerequisites:
Basic epidemiology and statistics. Some experience with R programming will be helpful, but those new to R can take a free introductory course on DataCamp (https://www.datacamp.com/courses/free-introduction-to-r) or work through the Posit Primers (https://posit.cloud/learn/primers). 

Materials:
R is a free open-source programming language and tool for statistics. For the hands-on exercises, you can either use Posit Cloud (advantage: “plug-and-play”, no need to install anything) or download and install the most recent version of R (https://www.r-project.org/) as well as RStudio (https://rstudio.com/products/rstudio/download/).
All the course materials will be made available through a course website.

Lecturer: 
Malcolm Barrett, PhD
Malcolm Barrett is a data scientist, epidemiologist and research software engineer at Stanford University. During his PhD, he studied vision loss and quality of life and has since worked at Posit (formerly known as RStudio) and in the private sector, including Teladoc Health and Apple. He develops and contributes to open-source software related to causal inference, reproducible research, R package development, and more.

Proof of achievement:
Participants will need to complete and submit 4 assignments (online) in order to receive ECTS credit. If you only want a certificate of participation, you will need to submit all assignments showing reasonable effort (i.e. at least 20 points to “pass”).

Fees and Registration:
Course Fees:

• € 510 reduced fee (students, Charité researchers, BSPH alumni)
• € 750 regular fee 

Please register using the following link: https://forms.office.com/e/xf2YstRCSE 

Introduction to Medical Informatics

• Format: asynchronous lectures and assignments + live exercises and office hours
• Dates: September 22 - 26 (please see schedule for live elements and office hours)
• ECTS: 2.5
• Course Language: English

Learning Objectives:
The aim of this introductory course is to provide participants with an overview of the most important areas of medical informatics. This will enhance the participants' ability to assess and work with medical informatics technologies. The course will introduce students to information systems and standards in healthcare and in the context of medical research. Participants will also learn about key data processing methods, data integration, and data sharing. The course also covers other topics of high practical relevance, such as information security and data protection, software development methods and open science approaches in medicine. After completing this course, participants should be able to assess the opportunities and risks of data-driven approaches in healthcare and medical research.

Format and Materials:
The course consists of 25 teaching units of material organized into 5 days. Particpants are encouraged to work through these units within this schedule, as live exercises and (optional) office hour support will be provided throughout these 5 days.
The units are a combination of digital lectures and hands-on exercises (not graded) plus four graded assignments. Lectures will be pre-recorded while exercises will be live. Additional instructor/TA support will be available via virtual office hours during the 5-day course period at 4:00-5:00 pm.
All course material (lectures, slides, Q&A document, exercises and assignments) will be linked from the teaching platform “Moodle” (access will be provided for external participants).

Lecturers: 
The Medical Informatics Group, led by Univ.-Prof. Dr. Fabian Prasser at the Berlin Institute of Health (BIH) and Charité – Universitätsmedizin Berlin, focuses on advancing digital medicine through innovative solutions for integrating healthcare and medical research data. Their research addresses key challenges such as efficient data collection, integration, and analysis of large, heterogeneous datasets, while maintaining public trust in the context of rapid technological change. The group focuses on four key areas: (1) technologies for interconnecting health data and for patient participation, (2) information integration and big data architectures in translational medicine, (3) cross-site data analysis and data sharing, (4) information security and data protection in digital medicine.

Proof of achievement:
Participants will need to complete and submit 4 assignments (online) in order to receive ECTS credit.

Fees and Registration:
Course Fees:

• € 425 reduced fee (students, Charité researchers, BSPH alumni)
• € 625 regular fee 

Please register using the following link: https://forms.office.com/e/xf2YstRCSE