Introduction to Data Science, A1N, 15 credits
The course will give you an introduction to Data Science, both in terms of research and practice. You will get insight into its multidisciplinary nature and get practice of studying one aspect of Data Science of your own choosing in more detail. You will also practice programming for Data Science. Recent research within the field will be presented together with the challenges that the field poses, not the least by ethical and societal concerns that might arise within the development of the field.
Artificial Intelligence A1N, 7,5 credits
This course centers on recent developments and challenges within the field of Artificial Intelligence and how methods and techniques within the field can be used in different research areas and fields of application within data science, such as data mining, decision support systems and information fusion.
Data Mining A1F, 7,5 credits
In this course, you will study fundamental concepts, algorithms and techniques within data mining. The course discusses classification by, for example, decision trees, nearest neighbor, Bayesian classifiers, neural networks, support vector machines and ensemble techniques. The course will also discuss association analysis, for example through the Apriori and FP-growth algorithms, as well as cluster analysis through k-means, hierarchical clustering and DBSCAN.
Big Data Programming A1F, 7,5 credits
In the course, you will learn principles for common big data data analysis frameworks. You will learn why traditional approaches will not work well for large scale, distributed analysis solutions. Relevant parts of the Hadoop ecosystem will be presented, where the intention is that you will be able to work with big data analytics independently. You will program and use components in Hadoop common for efficient large-scale data analysis and machine learning.
Visual Data Analysis A1N, 7,5 credits
To analyze large amounts of data in order to find patters, trends or to extract new knowledge can be a demanding process. Data- and information visualization methods can be used to facilitate this process and will allow decision makers to derive analysis results from data. The course will give an introduction to information visualization as well as how humans can be aided in the knowledge extraction process.
Master Degree Project in Informatics with a Specialization in Data Science, 15 credits
In this course, you will get the opportunity to apply your knowledge of Data Science on a problem oriented thesis work. Together with a supervisor, the student is to identify a problem and suitable method for solving this problem, as well as analyze the results obtained.