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Big Data, Artificial Intelligence and Machine Learning in Drug Safety
Get an overview of data science methods in the context of drug safety.
This course is a part of
In this course you will learn how to critically assess scientific studies using big data and you will gain competences within ethical, legal and regulatory aspects of big data and data science.
The need for competencies in Pharmaceutical Data Science is steadily increasing in response to the explosion of available and complex data in biomedicine and related streams.
The vast volume of data covers a diverse landscape from the properties of drug molecules over their biological mechanisms of action to individual patient data collected in clinical trials and healthcare settings.
Through this course you will get an overview of data science methods in the context of drug safety. The course is tailored for both academia and industry.
Topics of the course
- Pharmaceutical data science for drug safety.
- Introduction to artificial intelligence, machine learning, and deep learning.
- Introduction to the Science of "Big Data".
- Data sources and their characteristics, the possibilities for access.
- Case studies of applications of artificial intelligence, machine learning, and deep learning in drug safety.
- Regulatory and ethical aspects of using big data artificial intelligence in pharmaceutical science for drug safety.
For more details about the course, please refer to the course curriculum.
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Course director and lecturers on »Big Data, Artificial Intelligence and Machine Learning in Drug Safety«
Course details for »Big Data, Artificial Intelligence and Machine Learning in Drug Safety«
Dates and examination
Course dates
12–16 May 2025 – online (1 day's workload)
19–23 May 2025 – on-campus
Examination
Please find the exam dates in the exam plan.
Learning outcomes
After completion of the course, you will be able to:
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describe and explain the key sources of health data, and the context in which these data are collected, implications of the context on issues such as data quality, accessibility, bias, and the appropriateness of use to address specific research questions
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discuss different analytical approaches and limitations of data sources and methods
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understand the fundamentals of data science with a focus on pharmaceutical data science
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understand the roles of a pharmaceutical data scientist within the wider pharmaceutical research environment
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interpret and critically assess scientific studies and other types of information produced using big data and data science methods
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reflect on ethical, legal and regulatory aspects of big data and data science.
Admission criteria
You must meet the following criteria to be admitted to this course:
- Hold a relevant bachelor degree or equivalent.
- Have a minimum 2 years of relevant job experience in drug development.
- Be proficient in English.
Find detailed information in the admission criteria on Master of Industrial Drug Development.
Priority is given to enrolled students
This course is offered as an elective course in the Master of Industrial Drug Development and Master of Medicines Regulatory Affairs. Priority is given to students already enrolled in either of the two programmes. Once the enrolled students have been admitted to the course, the remaining seats are distributed on a first-come, first-served basis.
Partnership with Atrium
Atrium is a partner of Master of Medicines Regulatory Affairs. This course is one of 5 courses at the Master's programme, which are offered in collaboration with Atrium.
Atrium is one of Europe’s leading private course providers of practice-based courses in Regulatory Affairs. Their courses are based on collaboration with academia, industry experts and representatives from healthcare authorities.
International educational academy
Atrium is an international educational academy established by The Danish Association of the Pharmaceutical Industry (Lif). As a part of the Danish Association of the Pharmaceutical Industries, Atrium has a close connection to the experts in the Pharmaceutical Industry.
Tuition fees
EU/EEA citizens
30,000 DKK
Non-EU/EEA citizens
33,475 DKK
The fee includes lunch and coffee.
More information
All course information is available in the course curriculum:Place
Atrium
Lersø Parkallé 101
2100 Copenhagen
Denmark
Contact
Annette Villumsen
Study Administration Officer
master@sund.ku.dk
Tel: +45 35 33 60 66