Erasmus Summer Programme Courses

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Data Science in Epidemiology [ESP80]

Course highlights

EC points

0.7

Months

August

Course days

Monday to Friday (5 mornings)

Faculty

Dr. Daniel Bos and Dr. Gennady Roshchupkin

Course fee

€ 490

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

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Testimonial


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.


Disciplines:

  • Data Science
  • Prediction
  • Epidemiology
  • Machine learning

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