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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
Course director and lecturers on Big Data, Artificial Intelligence and Machine Learning in Drug Safety
Course details about Big Data, Artificial Intelligence and Machine Learning in Drug Safety
Dates and examination
Course dates
To be announced.
Examination
Please find the exam dates in the exam plan.
You can find further information about the course and the exam in the course curriculum.
See an overview of all courses on Master of Industrial Drug Development.
Learning outcomes
After completion of the course, you will be able to:
- 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.
- Discuss different analytical approaches and limitations of data sources and methods.
- Understand the fundamentals of data science with a focus on pharmaceutical data science.
- Understand the roles of a pharmaceutical data scientist within the wider pharmaceutical research environment.
- Interpret and critically assess scientific studies and other types of information produced using big data and data science methods.
- 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.
Tuition fees
EU/EEA citizens: 30,000 DKK
Non-EU/EEA citizens: 33,475 DKK
The fee includes lunch/coffee.
Place
Atrium
Lersø Parkallé 101
2100 Copenhagen
Denmark
Contact
Annette Villumsen
Study Administration Officer
Tel.: +45 35 33 60 66