Education

Data Science in Epidemiology [ESP80]

Course highlights

EC points

0.7

Start date

23-08-2021

End date

27-08-2021

Course days

Monday to Friday (5 afternoons)

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

Application

Go to the ESP website

Downloads

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.


Disciplines:

  • Data Science
  • Prediction
  • Epidemiology
  • Machine learning


//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 epidemiological environment;
  • Apply basic skills of Python and Jupyter notebooks for data science: the most popular and efficient for high-performance scientific analysis programming language;
  • Apply the general methods of data visualization using Python;
  • Understand Machine Learning methods and neural networks algorithms in epidemiology.

Participant profile

Those interested in gaining knowledge about how to work with big data using python and learn basic principles of machine learning and deep learning.

Assessment

Attendance


Reduction on fees

PLEASE NOTE THIS DOES NOT APPLY TO THE RESEARCH MASTERS (120 EC points)

50% reduction on our fees for participating institutes of NIHES

This 50% reduction is offered to participants formally appointed at the departments or sections participating in NIHES.

University Medical Center Rotterdam/Erasmus University Rotterdam:

  • Department of Epidemiology
  • Department of Public Health
  • Department of Psychiatry, Section of Medical Psychology and Psychotherapy
  • Department of Child and Adolescent Psychiatry/Psychology
  • Department of Medical Informatics
  • Department of General Practice
  • Erasmus School of Health Policy & Management
  • Generation R

50% reduction on our fees for NIHES affiliated partners

This 50% reduction is offered to participants formally appointed at the following NIHES affiliated partners:

University Medical Center Rotterdam/Erasmus University Rotterdam, departments of:

  • Dermatology
  • General Paediatrics
  • Plastic and Reconstructive Surgery
  • Rehabilitation Medicine

UMIT, Department of Public Health, Health Services Research and Health Technology Assessment.

25% reduction on our fees for Erasmus MC employees

For participants formally appointed at all departments of Erasmus MC (except the departments mentioned above).

25% reduction for all (international) PhD students
Please upload proof of enrollment as a PhD student in the application form and state the name of your tutor on the application form (remarks field).

25% reduction on our course fees (maximum 3 EC points per academic year) for all NIHES alumni
Please state that you are a NIHES alumnus/alumna and the academic year you graduated (remarks field). Please note that the reduction on fees for alumni does not apply to the Erasmus Summer Programme.