Students will be signed up for this course by the study administration. If you have taken the course before and need to be signed up again please contact inm-exams@ruc.dk

Natural science deals with the real world, and hence empirical data from experiments or observations play a central role. Basic Course 1 presents various types of empirical data as well as how they can be described, presented, analyzed, and interpreted using quantitative and qualitative methods. In the course, you will also be introduced to relevant IT tools and key notions such as statistical descriptors, probability, random sampling, simple statistical distributions and tests, and basic concepts and techniques of computer science.

This course runs intensively in the first 5 weeks of the semester. It is not recommended to sign up for more than one course in this intensive period

Regarding the use of generative AI at the exam

In this course, generative AI tools (GAI) are allowed in the work on the exam if their use is declared. You must clearly indicate how you have used generative artificial intelligence (GAI). This can, for example, be included as part of a methodology section or as a brief statement at the end of your exam paper or submitted as an appendix to your assignment. This means that you must describe how you have used GAI, for example, for preparatory work on the assignment, to ask questions, search and process information, receive feedback and critique on your text, perform proofreading, or improve language and readability. It is important that you actively consider your choice of tools in this way, as it is part of the entire creation process of the assignment and thus part of your scientific method and academic communication.

The use of any specific text that is GAI-generated requires citation, just like the use of any other sources from which direct quotes are taken.

In the library's guide, you can see more about how to cite AI and how you can declare your use of GAI - find the guide here.

Regular spell check and other language suggestions, as known from Word or other word processing programs, as well as programs for writing minutes and transcription, are allowed in all written exams and do not need to be declared.

Lectures: 14 h, team exercises: 28 h, preparation for lectures and exercises: 77 h, preparation for test: 15 h, test: 1 h. In total 135 hours.

Read more about expected work efford at Natbach here

• Knowledge of different types of empirical data

• Knowledge of different types of graphical presentation of data

• Knowledge of static descriptors and simple static distributions and test

• Skills to be able to present, analyze and interpret empirical data using quantitative and qualitative methods

• Skills to be able to read and use specialist symbolic language and other formal representations

• Skills to be able to use relevant IT tools when working with empirical data

• The competenc to be able to undertake simple mathematical problem-solving

• The competence to be able to communicate academic issues, both orally and in writing through presentation, analysis and interpretation of empirical data

Mandatory course

Introduction to data types, data presentation techniques as well as descriptive statistics. Introduction to basic concepts and techniques within Computer Science. An introduction to the relationship between sample and population, probability, binomial and normal distribution, confidence intervals and linear regression.

All course sessions begin with a lecture. Following the lecture, the students work on assignments in small groups.