Entry requirements

52.5 credits in information systems or equivalent including 7.5 credits in databases.

Learning outcomes

In terms of knowledge and understanding, after completed course the student should be able to:

- explain fundamental terms within the areas data warehousing, big data analytics, data mining, and data science

- explain how data mining can support answering a research question in a data science project

- explain how different database systems, with emphasis on data warehouses and NoSQL technologies, can support decision making in organisations

- describe different data mining methods and how they differ

- explain under which conditions a particular data mining method can be used to answer a given question

In terms of skills and abilities, after completed course the student should be able to:

- plan a data science research process based the use of the data mining methods, including problem identification, question formulation, selection of data, preprocessing method, data mining method(s), and method for evaluation of results

- apply elementary data mining methods to perform analyses

In terms of evaluation and analysis, after completed course the student should be able to:

- interpret and analyse the results of a data mining process, as well as assess the effects of choices made during the process

- analyse and evaluate the social consequences of data mining and big data for society, taking into account ethical aspects

Content

The course introduces the student to data mining as a method för answering a research question within the overall framework of data science. Data science is a research-oriented approach that includes problem identification, question formulation, identification and preprocessing of data, choice and application of analysis method, and analysis and evaluation of results. During the course, data will be discussed extensively, including data types and characteristics, data transformation, data storage and database models, as well as how semi- or unstructured data, which characterize big data, kan be processed. Further, the data mining process and data mining methods such as classification, clustering, association analysis, deviation detection, and text mining will be presented and applied. Data mining applications, and strengths and weaknesses of different methods, will also be discussed. Finally, students will be exposed to social and ethical perspectives on data mining and big data.

Instruction

Lectures, laborations, seminars.

Assessment

Exam, assignments, laborations, seminars.