2024/2025 KANCDSCO2403U Linear Algebra and Applied Statistics
English Title  
Linear Algebra and Applied Statistics 
Course information 

Language  English 
Course ECTS  7.5 ECTS 
Type  Mandatory 
Level  Full Degree Master 
Duration  One Quarter 
Start time of the course  First Quarter 
Timetable  Course schedule will be posted at calendar.cbs.dk 
Study board 
Master of Science (MSc) in Business Administration and Data
Science

Course coordinator  


Main academic disciplines  


Teaching methods  


Last updated on 10042024 
Relevant links 
Learning objectives  
To achieve grade 12, students should meet the
following learning objectives with no or only minor mistakes or
errors:


Prerequisites for registering for the exam (activities during the teaching period)  
Number of compulsory
activities which must be approved (see section 13 of the Programme
Regulations): 2
Compulsory home
assignments
Each assignment is 35 pages in a group of 24 students. The students have to get 2 out of 3 assignments approved in order to go to the exam. There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot hand in due to documented illness, or if a student does not get the activity approved in spite of making a real attempt to pass the activity, then the student will be given one extra attempt before the reexam. Before the reexam, there will be one individual home assignment (max.15 pages), which will cover 2 mandatory assignments. 

Examination  


Course content, structure and pedagogical approach  
This course's primary focus is to provide students with the essential mathematical tools from linear algebra and applied statistics needed by the students in the Data Science programme. Linear algebra forms the foundation for many data science concepts and algorithms, while applied statistics equips the students with techniques to analyze and interpret the results effectively to draw meaningful insights from data.
Furthermore, this course provides knowledge about,
The course will combine lectures, handson exercises, and mandatory assignments on the topics covered to solidify your understanding. The students will have ample opportunities to apply these learned concepts in realworld data science problems.


Description of the teaching methods  
The course consists of lectures, exercises, and
mandatory assignments. Both the lectures and the handson exercise
sessions will be conducted on campus. There will be a teaching
assistant/instructor providing support for the handson exercise
sessions.
The presented theories, concepts, and methods should be applied in practice during the exercise sessions. The students will work on the mandatory assignments to consolidate their understanding of the concepts and the application of the concepts using the practical skills obtained from the handson exercises. 

Feedback during the teaching period  
In this course, feedback to the students will be
provided in the following ways.
1) During the handson exercises following each lecture, we will review the solutions to the exercises, discuss various techniques and alternative methods for solving them, and clarify any questions from the students. 2) Students will receive Feedback on the mandatory assignments as part of their grading. Since the mandatory assignments are at the group level, the students will receive collective feedback on their group submissions. 

Student workload  

Last updated on
10042024