STKD6060 Research Methods in Data Science Course description

What is Data Science, how do we approach problems in Data Science and how can Data Science contribute towards a sustainable future? In this course, we will try to answer these questions. Initially, we will briefly discuss some common ideas we may have about what science is and how we do scientific research. What makes a research method suitable or not suitable? Focusing on specific cases, we will consider successes and disasters in the history of Data Science, with different protagonists, some working at research centers, others in the industry and the business sector.

We will teach the rules, methods and limits of Data Science as well as how to apply them to real world challenges. For example, for predicting the likelihood of the end of virus pandemics or of the next financial crisis, for approaching a sustainable future with renewable energy and for improving the knowledge of our brain.

The course is designed for both students working towards an academic research career, as well as for students aiming at the industry and business sector, where skills in data science are important.

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.

One half year of university studies (30 ECTS), in addition to the international summer schools general requirement. The requirement needs to be met by the application deadline.

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.

This course will feature lectures and lab work to provide both theoretical and hands-on content. Students will work in groups of up to five students or individually to complete assignments given to them. The students will supplement the lectures and lab with their own reading. All classes are prepared to be taught on campus, but can be moved online at the discretion of the instructor.;

The following coursework is compulsory and must be approved before the student can take the exam:

Students will select a research paper of their preference from a list of papers, or chosen by them (under the guidance of the teacher), and study it carefully according to the contents learned in the theoretical classes.

The examination will evaluate how well is the student able to explain the content of a research paper, particularly in what concerns the identification of its most important parts (research problem, methods and take-home messages) as well as a critical view on the paper. To this end, the examination will be done in two steps:

• An individual oral exam where the student presents a critical perspective of his/her selected article, out from a list of articles provided at the beginning of the semester, presenting the main content of the paper. For the oral presentation the student may use slides, notes or other material, but should be able to show acquaintance on the matters he/she is presenting as well as some knowledge about the technical details behind the main content. The oral examination counts for 50% of the final grade.
• An individual written summary with 1 to 2 pages (weight=50%) on the selected article, where the student should prove his/her ability in extracting what is fundamental in the paper, separating it from complementary details.

Each exam must be assessed to E or better for the course as a whole to be given a final grade.

All support materials are allowed for both the oral presentation and for the individual written summary.

The final assessment will be graded on a grading scale from A to E (A is the highest grade and E the lowest) and F for fail.

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.