Data Science Concepts

The amount and variety of data in the domains of living environment, food, health, society and natural resources increases very rapidly. Data thus plays an ever more central role in these areas, and careful processing and analysis can help extract information and infer new knowledge, eventually leading to new insights and a better understanding of the problem at hand. Knowledge of core concepts in data science – acquisition, manipulation, governance, presentation, exploration, analysis and interpretation – and elementary data science skills have become essential for researchers and professionals in most scientific disciplines. This course is an introduction to data science concepts, combining computer science, mathematics and domain expertise: acquiring and manipulating raw data, obtaining information by processing and exploration, and finally reaching understanding by analysis and modelling. This will be complemented by elementary skills in data wrangling, exploration and analysis. The content of the course is strongly embedded in a number of provided domain-specific cases from biology, health and nutrition and the environment, allowing students from many disciplines to appreciate the relevance of data science in their domains.

After successful completion of this course students are expected to be able to:

• Explain the relevance of data and data science in research and application within their field of study
• Recognize key concepts as used in data science practice and elaborated in continuation courses
• Discuss the need for and describe approaches to data acquisition, manipulation, storage, governance, exploration, presentation, analysis and modeling
• Apply a number of basic techniques for data wrangling, exploration and analysis in use cases related to their field of study, including practicing elementary scripting skills

Assumed Knowledge:
This course assumes basic working knowledge on mathematics and statistics, as treated in Mathematics 1 and 2 (MAT14803/903), Mathematics for Social Sciences (MAT12806) and Statistics 1 and 2 (MAT15303/403).
It is not necessary to follow this course if the student had completed BSc minor in Bioinformatics or Data Science.