This module is the first module within the learning line Data science in Healthcare. It is an introductory module on the foundations of data science and its technologies. It introduces students to an inferential and computational way of thinking and lays the basis for the following modules of the learning line. The module starts with a conceptual discussion about data science and the way it influences healthcare. What is the historical origin of this domain and what do the buzzwords mean (i.e. data science, data analytics, AI, algorithms, machine learning)? Students learn about data, data representation and data interoperability in the healthcare domain, the concepts of existing responsibility frameworks covering topics such as open science and the FAIR (=findable, accessible, interoperable, reusable) data principles. Besides learning about concepts, students are also introduced to common methods used within the field of data science and how it is used within healthcare.

The specific course objectives are;

Expert:

The student is able to:

        ● know the historical origin of data science

        ● explain data science buzzwords

        ● distinguish between data collection methods and data  types

        ● know how health information is stored

        ● know about data collection and conducting experiments

        ● know how to tackle issues with regards to ethics, legal  compliance, data quality, algorithmic fairness and  diversity, transparency of data and algorithms, privacy,  and data protection.

        ● explain the difference between a number of  responsibility frameworks

        ● make their data more FAIR

        ● know about privacy-preserving approaches and  techniques used for data protection (pseudo- and  anonymization, data encryption)

        ● know how data science influences healthcare

Investigator

The student is able to:

        ● formulate research questions for data science problems

        ● query and exchange health data 

        ●  work with data types, collections, tables and data  standards in Python

        ● set up data science experiments

        ●use visualisations for data science storytelling        

●clean and manipulate datause privacy-preserving techniques and data encryption in practice