2020/2021 KANCDSCO1001U Foundations of Data Science: Programming and Linear Algebra
English Title  
Foundations of Data Science: Programming and Linear Algebra 
Course information 

Language  English 
Course ECTS  7.5 ECTS 
Type  Mandatory 
Level  Full Degree Master 
Duration  One Semester 
Start time of the course  Autumn 
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 10062020 
Relevant links 
Learning objectives  


Course prerequisites  
Even though there are no prerequisites for this course, it is recommended to have some experience with programming and understanding basic statistics. Moreover, this course is a demanding course as the students will learn new technologies, methods, Python programming language and therefore it requires an interest in and commitment to handson learning.  
Prerequisites for registering for the exam (activities during the teaching period)  
Number of compulsory
activities which must be approved (see s. 13 of the Programme
Regulations): 3
Compulsory home
assignments
Each assignment is 13 pages in group of 14 students. The students have to get 3 out of 5 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 fails the activity 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 home assignment (max.10 pages) which will cover 3 mandatory assignments. 

Examination  


Course content, structure and pedagogical approach  
This course provides an introduction to three main areas:
Furthermore, this course provides knowledge about
Furthermore, the course provides the students with practical handson experience on many of the topics listed above. After completing the course the students will be able to apply and use various programming constructs in Python language and also a good understanding of big data architectures and foundational mathematical/statistical theories that are required for data science courses.


Description of the teaching methods  
The course consists of lectures, exercises, and
mandatory assignments. The lectures will be delivered online and
the handson exercise sessions will be conducted on campus. There
will be a teaching assistant/instructors providing technical
support for the handson exercise sessions.
The presented theories, concepts and methods should be applied in practice in 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, the students will receive help and feedback in solving the practical handson exercises from the teacher and the instructors. 2) At the end of each exercise session, we will go through the solutions to the exercises and discuss various techniques and alternative methods to solve the exercises and also clarify any questions from the students. 3) Feedback on the mandatory assignments will be provided to students as part of the grading for the mandatory assignments. Since the mandatory assignments are at the group level, the students will receive collective feedback on their group submission. 

Student workload  


Expected literature  
The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy the books.Notes, scientific articles, chapters and webpages will be handed out/made available during the course
Textbooks:
Journal papers:
