Data Science courses 120 ECTS
The following courses are included in the Data Science master's program.
Scientific Theory in Informatics A1N, 7.5 ECTS
In this course, you are trained to address scientific issues in informatics with well supported arguments. Informatics is a broad scientific area, which encompasses a number of subdisciplines. This course covers a broad selection of central scientific theories of the subdisciplines included in the University of Skövde´s definition of information technology. The course also includes activities in which the students are given the opportunity to penetrate a particular theory, based on their own interest. The course also includes elements in which the students are practicing their oral proficiency in English, by presenting and discussing relevant theories.
Advanced Programming A1N, 7.5 ECTS
Big Data analytics puts new demands on the programming needed to implement data mining, data analysis and visualization of results. The amount of data in itself makes even traditionally efficient algorithms unmanageable, which means that scalable algorithms become more and more important. This course gives an introduction to functional programming and can be seen as a basis for programming for Big Data. The main programming language within the course will be the "scalable language" Scala.
Advanced Artificial Intelligence A1N, 7.5 ECTS
This course is designed for you who want to gain an overview of the current challenges within the area of artificial intelligence and how its methods and techniques relate to different research and application areas within the area of data science, such as data mining, decision support systems, and information fusion. If you are studying, data science, computer science, information technology or informatics you will get an overview about artificial intelligence on an advanced level. You will also critically discuss ethical and philosophical aspects within artificial intelligence.
Big Data Programming A1F, 7.5 ECTS
In this course, the student will learn about common frameworks for data analysis on big data. The course will explain why traditional methods do not work for distributed big data analytics. Relevant parts of the ecosystem Hadoop will be presented. The student will learn to program and perform efficient big data analytics and machine learning using the different components within the Hadoop framework.
Data Mining A1F, 7.5 ECTS
In this course you will study fundamental concepts, algorithms and techniques within data mining. The course covers classification, e.g. decision trees, nearest neighbor, Bayesian classifiers, neural networks, support vector machines and ensemble methods. The course will also cover association analysis, e.g. the Apriori algorithm and the FP-growth algorithm, as well as cluster analysis, e.g. k-means clustering, hierarchical clustering and the DBSCAN algorithm.
Visual Data Analysis A1N, 7.5 ECTS
Analyzing large data sets in order to find patterns, trends or extract new knowledge can be a challenging process. Data and information visualization methods help supporting this process and allow decision makers to derive analytical results from the data. This course serves as an introduction to the science and technology of data and information visualization, visual data mining and visual analytics. The course contents include both theoretical foundations as well as practical applications of integrated visual analysis techniques to large real-world data intensive problems.
Data Science Project A1F, 15 ECTS
In this course, the student has the opportunity to further advance knowledge within one of the areas in data science. Based on the courses during the first year, the student should identify and formulate a problem of interest and one or several approaches for solving the problem. Cooperation between students is encouraged; however, each student should be able to declare the contribution to the project and write an individual report for examination. One important aspect in this course is the student's choice and motivation of evaluation method for the solution.
Data Driven Decision-Making A1F, 7.5 ECTS
The course highlights usability aspects when designing data-driven decision support systems. The central issue is how autonomous decision support systems can be made more transparent to its users. The course thus incorporates disciplines such as theories of human decision-making, interaction and information design, information visualization, visual data analysis and human-computer interaction. The course comprises both theoretical and practical parts where you will be given an opportunity to immerse within a chosen subject.
Analysis of Complex Data A1F, 7.5 ECTS
The course focus on methods for analysis of data where the data is more complex than attribute-value data, often use within traditional data mining and machine learning. The course covers methods within areas such as text mining and graph analysis, and how these can be applied to different types of problem, e.g., sentiment- and network analysis. Methods within the area of data privacy are also part of the course. The course consist of lectures, a computer assignment, and a project where one has the opportunity to learn more about some specific problem related to analysis of complex data.
Information Fusion A1F, 7.5 ECTS
This course is for you who want to gain knowledge within the research area information fusion and the different methods and techniques that are used to fuse information (data) from different sources with the purpose to support human decision making in different application areas. The information that has to be fused is often subject to different types of uncertainty (missing data, wrong or noisy data). Therefore, uncertainty handling is an important part of information fusion. Within this course different methods for uncertainty handling are discussed.
Business Intelligence A1F, 7.5 ECTS
In this course, the student will learn how organizations can utilize business intelligence and exploratory analysis of complex data for the purpose of efficient analysis of internal and external data. The course contains a part a part where the student has the opportunity to apply knowledge and skills from the course to an application area of interest. The course also contains a seminar part where different problems and issues within business intelligence will be discussed.
Master Degree Project in Informatics with a Specialisation in Data Science A2E, 30 ECTS
In this course, the students have the opportunity to utilize their knowledge in data science on a problem-oriented individual master degree project. The student will identify a problem (in consultation with the supervisor), appropriate methods for the problem, and perform analysis of the result from these methods.