STKD6060 Research Methods in Data Science Course description

The students shall develop skills in carrying out, in an independent and systematic manner, an extensive, practically oriented project based on a client’s requirements. The students shall demonstrate that they can translate their knowledge into practical solutions. They shall be capable of applying basic rules for user quality to analysis, design, implementation, interfaces and documentation. They shall be capable of producing satisfactory documentation for computer programs and systems concerning products, operation and use, adapted to the different recipients of this documentation, and of providing an expedient description of their own work process based on given standards.

It is recommended to have completed one full year of university studies (60 ECTS) before the program starts. Basic algebra, basic mathematical analysis and statistics are highly recommended, though a short overview on the fundamentals of these two disciplines will be provided. The course will have a practical part using codes in python. Acquaintance with python programming is not required, but some experience with a similar programming language is recommended.

Students must be registered in the third year and have completed at least 100 credits from the first and second years of the programme by 1 October, before they can write their bachelor’s thesis.

After completing this course, the student should have the following learning outcome:

Knowledge

Upon successful completion of the course, the candidate will have the knowledge of:

• the specific cautions and pitfalls that should be taken into account through the entire research process, particularly when using tools from statistical analysis.
• practical problems in different fields of science, ranging from fundamental and natural sciences to social sciences and engineering.
• how statistical analysis can be used for uncovering the features and properties of a specific set of data.
• the main features and techniques one should be aware of for data collection.
• programming languages applicable to data analysis and modelling.

Skills

Upon successful completion of the course, the candidate will be able to:

• translate problems into research questions and evaluate it is soundness
• propose a first design of experiments to approach specific research questions.
• have a critical insight about the quantitative analysis presented in a research question, approaching authors’ interpretation about the presented results, e.g. in what concerns the correlation between different variables, their possible functional relations and the statistical significance of the overall results.
• develop a computer framework to generate surrogate data sets with particular statistical features, as numerical experiments for testing specific data models.
• apply statistical analysis and mathematical modelling techniques on data from their field of study.

General competences

Upon successful completion of the course, the student

• will be able to construct and establish a research plan
• will be able to carry out the basic quantitative analysis of its results.
• will be able to read a research article with a critical perspective and identify its structure and quality from the scientific point of view.

Project work in groups. The students shall carry out a project corresponding to the scope of the course for an external client in a company or a research project at OsloMet.

Detailed information and deadlines for the different phases of the project work will be provided in the course Canvas room published at the start of the semester. In addition to project supervision from a supervisor employed at OsloMet, a selection of workshops and seminars, depending on availability, are offiered.

The following work requirement is mandatory and must be approved before you can take the exam:

• The project contract between OsloMet, students and clients
• The preliminary project report
• A poster for the IT-expo (spring semester only)

All shall use templates provided by OsloMet.

A project assignment in groups (3-5 students). The project assignment is divided into the project report and the project presentation. The final grade is given on the basis of an overall assessment of the project report and the project presentation.

The grade of the project report can be appealed. If the grade of the project report changes as a result of the appeal, a new presentation must be held. The presentation part of the exam can not be appealed.

All.

Two examiners (one internal and one external).

Two examiners will be used, one of which can be external. External examiner is used regularly.

The course does not overlap with any known courses.