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2023/2024  BA-BINTV2006U  Programming and Data Analysis for Business

English Title
Programming and Data Analysis for Business

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Autumn
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 120
Study board
Study Board for BSc/MSc in Business Administration and Information Systems, BSc
Course coordinator
  • Sine Zambach - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 29-11-2023

Relevant links
Learning objectives
After the course the students should be able to:
  • Perform simple programming and data analysis tasks and work in a coding environment and editor as well as using AI-based language models.
  • Explain basic concepts within programming
  • Use basic models learned to analyse different kind of data such as survey data and numeric data
  • Understand programming methods that are used within different areas of businesses and governments
  • Analyze and understand output from simple data analysis methods in a business context
  • Reflect on data analytic tools such as regression and machine learning and artificial intelligence in their application context
Course prerequisites
None
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
The student must have approved at least 2 out of 3 activities in order to participate in the exam. The mandatory assignments are made in groups of 2-4 students.

Activity 1: Group assignment covering programming environment, basic programming concepts and business data problems (max.5 pages)
Activity 2: Group assignment covering models, prediction and inference (max.5 pages)
Activity 3: Group assignment covering all of curriculum (max.5 pages)

Retake of mandatory assignments:
There will not be any extra attempts provided to the students before the ordinary exam. If a student cannot participate in the compulsory assignments due to documented illness, or if a student does not have the activities approved in spite of making a real attempt, then the student will be given one extra attempt before the re-exam. Before the re-exam, there will be a home assignment (max.5 pages) that will make up for two assignments.
Examination
Programming and Data Analysis for Business:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Project
Release of assignment Subject chosen by students themselves, see guidelines if any
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter
Make-up exam/re-exam
Same examination form as the ordinary exam
The re exam will be 7 days home assignment based on a given new exam question. The report form is the same as the ordinary exam.
Description of the exam procedure

Graded home assignment based on data from the student's or common repositories.

The student will do an analysis as well as answering common questions about the data.

The code can be produced partly by using AI-based large language models if documented and reflected upon.
Course content, structure and pedagogical approach

Starting from scratch, the students will after the course have the basis for working with simple data analysis and programming, using the programming language R. It does so by introducing the students to 1. Basic principles in programming 2. Visualization and data wrangling, and 3. Data modelling using both basic statistical methods as well as simple machine learning methods.

 

In the exercise classes, there will be differentiated options for the students with a main focus on helping absolute beginners to get started on programming and computational thinking. In the end of the course, there will be extra time for supervision of the individual exam project by the instructors.

 

The course can be used as a basis for more advanced courses, where programming, mathematical modelling, and data handling is increasingly important, for instance finance, economy, information systems, communications, politics, etc. The course can also be used as a basis for more advanced courses on data science models or it-systems.

 

As part of the programming, AI-based large language models are introduced to generate code snippets as well as a reflection on their usage.
Description of the teaching methods
The course will use blended elements to introduce basic concepts and get hands on.
In addition, the students are encouraged to support each other’s learning, by working together in the mandatory assignments (although the final exam is individual).

There will be on site exercise hours in which the students can work with exercises and get help from instructor and peers. We try to differentiate the exercises such that students on various programming levels will gain from these.
Feedback during the teaching period
There will be feedback to the mandatory assignments, which covers roughly all of curriculum
Additionally, oral feedback on programming exercises will be given in class.
Finally, there will be feedback to the students' project exercise.
Student workload
Lectures/videos (mainly online) 27 hours
Excercises 33 hours
Reading at home 35 hours
Excercises at home 35 hours
Individual project exam 46 hours
Project group reports 30 hours
Further Information

Motivated in business, the programming course is relevant for students from bachelor programs such as BSc in Digital management, HA-almen, BSc SOC, SEM, IB, IBP, and other, who might have an interest in using high-end tools for data analysis.
Expected literature

The literature can be changed before the semester starts. Students are advised to find the final literature on Canvas before they buy any material. The chosen material for this course is freely available online.

 

  • Sine Zambach; Programming in R – Basic concepts, 2022 https:/​/​research.cbs.dk/​en/​publications/​programming-in-r-basic-concepts-version-10
  • Garrett Grolemund; Hadley Wickham: R for Data Science, O'Reilly, 2017  https://r4ds.had.co.nz/
Last updated on 29-11-2023