Erasmus Summer Programme Courses

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Data Science in Epidemiology [ESP80]

Course highlights

EC points

0.7

Start date

19 August 2024

End date

23 August 2024

Course days

Monday to Friday (5 afternoons)

Course time

From 13:00 till 16:00 CEST

Faculty

Dr. Gennady Roshchupkin, Dr. Daniel Bos and Prof. Kamran Ikram,

Course fee

€ 525

Location

Erasmus MC, Rotterdam NL

Level

Intermediate

Prerequisites

Basic understanding of biostatistics, and epidemiology (as covered in ESP03 and ESP01).

Basic knowledge of programming (experience with R, python or any other scripting language).

Disciplines

  • Epidemiology
  • Clinical Research
  • Methodology

Course Materials

Digitally, download instructions will be sent before the start of the course, by e-mail.

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Detailed information about this course:

Description

Faculty: Dr. Gennady Roshchupkin, PhD, Dr. Daniel Bos, MD, PhD and Prof. Kamran Ikram, MD PhD

Data science is a multi-disciplinary field where – besides domain-specific expertise – several fundamental scientific disciplines converge including mathematics, statistics, computer science, engineering and epidemiology. The aim of this new field is to combine scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. Recent advances in technology allow for the collection of enormous amounts of health-related data. Consequently, skills pertaining to handle and manipulate these data and to extract relevant information have become crucial to perform high quality research. Unfortunately, many (clinical) researchers without a technical background frequently experience troubles obtaining or developing these skills. The aim of this course is to bridge this gap in knowledge by providing an interactive and hands-on programme about data science and how it can be applied in epidemiological research.


Objectives

  • Understand that epidemiology is at the core of data science in healthcare;
  • Understand the basic of Machine Learning methods and neural networks algorithms;
  • Understand the biases and fairness related to handling and analyzing health-related data.


Participant profile

Clinical researchers, clinical epidemiologists, computer scientists, those in health technology assessment or value-based healthcare.

Assessment

Attendance