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Syllabus:

Data Analytics with R, 4.5 Credits

Swedish name: Dataanalys med R

This syllabus is valid: 2024-10-28 and until further notice

Course code: 2ST068

Credit points: 4.5

Education level: First cycle

Main Field of Study and progress level: Statistics: First cycle, has less than 60 credits in first-cycle course/s as entry requirements

Grading scale: Pass, Fail

Responsible department: Department of Statistics

Established by: Dean of Umeå School of Business, Economics and Statistics, 2024-10-24

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.
 
A student that has failed an examination on two occasions has a right to have another examiner or grading teacher appointed unless there are special reasons against it. A written request addressed to the Director of Studies should be made no later than two weeks before the next examination opportunity.

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.

Adaptations
Examiners may decide to deviate from the modes of assessment in the course syllabus. Individual adaptation of modes of assessment must give due consideration to the student's needs. The adaptation of modes of assessment must remain within the framework of the intended learning outcomes in the course syllabus. Students who require an adapted examination - and have received a decision on the right to support from the coordinator at the Student Services Office for students with disabilities - must submit a request to the department holding the course no later than 10 days before the examination. The examiner decides on the adaptation of the examination, after which the student will be notified.

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

Other regulations

The content of this course corresponds to the content of the course 2ST065 Data Analytics with R, part 1 (4.5 Credits) and cannot therefore be credited in a degree together with that course.

Literature

Valid from: 2025 week 44

R for data science : import, tidy, transform, visualize and model data
Wickham Hadley., Grolemund Garrett
2016 : 492 s. :
ISBN: 9781491910399
Mandatory
Search the University Library catalogue
Reading instructions: Available free online: https://r4ds.had.co.nz/ (1st Edition) https://r4ds.hadley.nz/ (2nd Edition)