Data Analytics with R, part 1, 4.5 Credits

Contents

The course introduces data analytics using the programming language R. The focus of the course is preparing data for analysis, analyzing data using statistical methods, as well as communicating results.

When dealing with data it is first important to understand the data material at hand. For this purpose, exploratory data analysis (EDA) which includes summary statistics and visualization, is used. The next step is to answer different questions based on the data material. During the course, students will learn common statistical methods (e.g., hypothesis testing, linear and logistic regression) for comparing groups, finding relationships between variables, making predictions, or classifying data points.

To help decision-makers make the right decisions, it is important to communicate the results of a data analysis in an accessible way. This means, for example, a clear description of how the data was prepared and the analysis was performed to ensure transparency and reproducibility, and well thought out visualization of the results in the form of graphs and tables. All this is also covered in the course.

Expected learning outcomes

Knowledge and understanding

Students must be able to
1.    Explain what analysis is suitable for a given data set and problem

Skills and ability

Students must be able to
2.    Inspect, clean, and transform data to prepare it for analysis in R
3.    Identify and apply relevant statistical methods in R to answer specific questions.
4.    Present analysis results using graphs and tables
5.    Present how the data was prepared, and the analysis carried out in a transparent and reproducible way

Students must be able to

6.    Critically evaluate results and conclusions from data analysis

Required Knowledge

At least 45 ECTS. Proficiency in English equivalent to Swedish upper secondary course English B/6).

Form of instruction

Learning is mainly supported by material provided through a learning platform. The material provided will consist of recorded lectures, practical online exercises, and code examples. There will be mandatory assignments with tutoring.

Examination modes

The examination consists of individual written assignments.  The grades used are G (Pass), and U (Fail). To obtain the grade G (Pass) the student needs to pass all mandatory assignments.

A student who has passed an examination is not allowed to take another examination in order to get a higher grade. For students who do not pass, an additional test will be held according to a set schedule.
 
Exceptions from examination form as stated in the syllabus can be made for a student who has a decision on pedagogical support for disabilities. Individual adaptations of the examination form should be considered based on the student's needs. The examination form shall be adapted within the framework of the expected learning outcomes stated in the course syllabus. At the request of the student, the course's responsible teacher, in consultation with the examiner, must promptly decide on the adapted examination form. The student must then be informed of the decision.

After two failed examinations in a course, or part of a course, the student has the right to request another examiner unless there are special reasons against it. The request for a new examiner is made to the Director of studies at the Department of statistics.

Examinations based on the same course syllabus as the ordinary examinations are guaranteed to be offered up to two years after the date of the student's first registration for the course.

Academic credit transfer
Academic credit transfers are according to the University credit transfer regulations.