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.
Data Science Project, A1F, 15 credits
In this course you will study a problem within data science that is connected to ongoing research at the university. With the help of a supervisor, you will get a chance to investigate a problem from a theoretical and practical perspective as well as critically reflect upon chosen methods. The project can be conducted in collaboration with an external organization.
Explainable AI, A1F, 7,5 credits
In this course you will get an introduction to the field of Explainable AI. Why this field emerged, together with recent research and common methods and techniques within the field will be presented. You will get a chance to implement chosen methods and analyze as well as reflect on their strengths and limitations.
Predictive Analytics A1F, 7,5 credits
The course gives a deeper view upon methods and techniques that can be used for making predictions. You will learn principles and limitations within the field, as well as discuss which information an expert modeler ought to present to decision makers based on the prediction results.
Business Intelligence A1F, 7,5 credits
In this course, you will get an introduction to Business Intelligence (BI) and its application areas. Through a practical assignment, you will test to create a BI solution for unstructured data as well as reflect on the results. The course also problematizes ethical concerns that might arise when making decisions based on data analyses.
Information fusion A1F, 7,5 credits
The course presents various methods for information fusion and discusses their roles in the decision making process. Different types of uncertainty in information that is to be fused will be presented, and methods for uncertainty handling will be discussed.
Master Degree Project in Informatics with a Specialization in Data Science, A2E, 30 credits
In this course, you will get the opportunity to apply your knowledge of Data Science on an individual 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.