Curriculum

Program Structure and Curriculum

Here, you can find information regarding the structure and curriculum of the Health Data Sciences PhD Program.

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Program Structure

Anticipated Program Duration: 6 semesters

Credit Requirements: 180 ECTS

30 ECTS from coursework in the Health Data Science disciplines of biostatistics, epidemiology, meta-research, population health science and public health.

Mandatory Coursework: 18 ECTS
Elective Coursework: 12 ECTS

150 ECTS is credited for the dissertation research project

HDS PhD Courses and Credit Details

Please note that details regarding the courses listed below are subject to change.

Mandatory Health Data Sciences Coursework (18 ECTS)

Module 1: Research Proposal Seminar (RPS) - 6 ECTS

One of the main components of the cumulative dissertation portion of your PhD is to publish research papers. This course is designed to help you get this process started in a supportive environment. In this course, experienced faculty members and your peers will provide you with structure and feedback as you prepare and refine formal scientific research proposals for your three subprojects during your first year as an HDS PhD Program student. The proposals will detail the theoretical framework, research questions, and methodological approaches you plan to implement in your research.

RPS consists of two parts. In the first semester (October - February), monthly meetings will accompany the proposal writing process and facilitate peer-to-peer as well as expert feedback. In the second semester (April - August), you will join the ongoing Doctoral Student Seminar (DSS), which is also attended by students of higher semesters. During this time, you will further refine your proposals on your own, begin your research work, and present some preliminary progress, with the opportunity to get early input in a collaborative, group setting.

This course takes place during the first year of the HDS PhD Program.

Module 2: Advanced Methods in Health Data Science (MET3) - 6 ECTS

Topics previously covered in this course include mixed models, missing data, meta-analysis methodology, estimating prevalence with imperfect diagnostic tools, data sharing, data anonymization, machine learning, use of scores and weights to control for confounding, causal structure learning, genetic approaches to Mendelian Randomization, and more.

This course is typically completed in the third semester of the HDS PhD Program.

Module 3: Mentored Teaching a Methods Course - 6 ECTS

The doctoral program in Health Data Sciences requires all graduates to develop teaching skills for potential careers in academia. In close collaboration with our faculty, doctoral students prepare lectures, design and evaluate assignments and teach classes.

Upon successful completion of this module, students will be able to prepare and teach methods in the health data sciences domains.

This course is typically completed in the second or third year of the Health Data Sciences PhD Program and can be completed according to the student's and instructor's availability.

Elective Coursework (12 ECTS)

Elective coursework that can be spread out over the course of the studies according to the student’s preferences and in consultation with the supervisory team’s recommendations.

Cumulative Dissertation and Research Project (150 ECTS)

All HDS PhD Program students are expected to complete a cumulative dissertation (doctorate by publication), as is the general Charité guideline for PhD students. For more information, see: https://promotion.charite.de/en/procedure/regulations_2017/dissertation/

Frequently Asked Questions

Research Project

What kind of research project am I required to have? How many details should I provide in the application?

In the initial application, we are looking for interesting, relevant project ideas related to one or more of the health data sciences domains. It is also important that you can convince the HDS PhD Board that your proposed project would be feasible with respect to the 3-year time frame, you have or will gain access to the requisite data, you have a plan for funding the project, and that the project is suited to your skillset and background. We encourage you to discuss your project ideas with at least one potential supervisor prior to submitting your application.