Erasmus Summer Programme Courses

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Data Science in Epidemiology [ESP80]

Course highlights

EC points

0.7

Start date

23 August 2021

End date

27 August 2021

Course days

Monday to Friday (5 afternoons)

Course time

From 13:00 till 16:00 CEST

Faculty

Dr. Daniel Bos and Dr. Gennady Roshchupkin

Course fee

€ 490

Location

Online

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

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

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Itai Magodoro

Zimbabwe

The professors - who are at the cutting edge in their respective fields - bring science to life!

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Detailed information about this course:

Description

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

Data science is a multi-disciplinary field that uses scientific methods and algorithms to extract knowledge and insights from structured and unstructured data. Recent advances in technology allows 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 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.


//Please note that the course information is subject to change and will be updated from time to time. We will do our utmost best to ensure the accuracy and reliability of the information on this website.//

Objectives

  • Understand the concept of data science in the epidemiological environment;
  • Learn basic skills in Python and Jupyter notebooks for data science: the most popular and efficient programming software for high-performance scientific analysis;
  • Apply the general methods of data visualization using Python;
  • Understand Machine Learning methods and neural networks algorithms in epidemiology;
  • Appraise the role of big data/data science in terms of study design and scientific value

Participant profile

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

Assessment

Attendance