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2023/2024  BA-BIBAV1014U  Introduction to Business and Social Data Science

English Title
Introduction to Business and Social Data Science

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Bachelor
Duration One Semester
Start time of the course Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 65
Study board
Study Board for BSc in Business, Asian Language and Culture
Course coordinator
  • Zoltan Fazekas - Department of International Economics, Goverment and Business (EGB)
Main academic disciplines
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 14-02-2023

Relevant links
Learning objectives
Students will be able to:
  • Load, process, and transform data stemming from different sources and structured in different ways.
  • Summarize data and apply visualization techniques to explore and present data.
  • Identify, apply and compare suitable machine learning techniques to business and social data.
  • Effectively communicate your results, model assumptions, and share well documented and formatted code, following best practices.
  • Formulate data driven conclusions for social and business applications, while incorporating considerations related to uncertainty and causality.
Course prerequisites
Note on software use and prerequisites:

This is a very applied course. Students are expected to spend a substantial amount of time working with software, i.e. developing their coding skills. The course software will be R, but no prior knowledge of R is expected. The first part of the course will introduce the software step-by-step through various applications.
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): 1
Compulsory home assignments
Prerequisites for registering for the exam (activities during the teaching period):
Number of compulsory activities which must be approved: 1
Compulsory home assignments

There will be a total of two compulsory activities consisting of written home exercises. One out of two activities must be approved to qualify for the exam. Feedback on the assignments will be offered through video supporting material and/or individual written comments.

No further attempts to pass the mandatory activities will be provided before the ordinary exam. If a student has not had the required number of activities approved, the student will not be able to attend the ordinary exam. Should the student fail at the ordinary exam then no further activities are required to qualify for the retake.

If the student fails to qualify for the ordinary exam:
In order to qualify for an extra mandatory activity before the retake the student must have (either) 1) attempted both activities without succeeding in having them all approved; and/or 2) provided relevant documentation of illness or other extenuating circumstances.

In such cases s/he must, before the retake submit a paper covering the substance of the required number of mandatory activities. Specific requirements are provided by the course coordinator. When the paper is approved by the course coordinator, the student may be registered for the retake.
Examination
Introduction to Business and Social Data Science:
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 Written assignment
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 72 hours to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Make-up exam/re-exam Same examination form as the ordinary exam
A new exam assignment must be answered. This apply to all students (failed, ill, or otherwise)
Description of the exam procedure

Students will be required to carry out data analysis relying on the tools discussed in the course

 

 
Course content, structure and pedagogical approach

These are exciting times to work with data. Data science—bridging statistics, computer science, and substantive area expertise, has become an integral part of decision-making since the availability and diversity of data sources have recently increased at an unprecedented pace. The course provides students with a very applied introductory knowledge about working with data to understand and inform various business and social decisions. Upon completing the course, the students should be able to understand the techniques introduced in the course and apply them to new data and specific problems. The course content covers two broader areas:

 

  1. Working with data: data wrangling, transformations, summary, text-as-data, & visualization.
  2. Machine learning techniques and principles : such as bias-variance trade-off, out-of-sample prediction, regression, classification, regularization, and so on.

 

For our teaching, we will introduce a particular question or application and offer data driven solutions based on various techniques. Throughout the course we will follow an applied, hands-on approach, always working on implementation and coding (in R). Hence, students will spend a substantial amount of time working with software, including compulsory activities and final examination. All activities will be based on data (or type of data) often used in private and public organizations, from various country, firm and individual level sources.

 
Description of the teaching methods
Teaching will be carried out as a mix of lectures, exercises, and activities, allowing for coding together and code review. Many sessions will also have supporting video materials, which will help preparation and provide additional examples.
Feedback during the teaching period
Feedback will be offered for the compulsory activities during the course through solution videos (1) and/or individual, personalized feedback (2). Feedback regarding specific inquiries will be given during ‘office hours’ offered by full-time staff members, although these can never be a substitute for participation in the regular teaching activities. Generally, we encourage you to ask questions or make comments on the online forums used and during our sessions.

Student workload
Lectures, exercises and workshops 38 hours
Exam 36 hours
Course preparation. Includes: readings for lectures and exercises work on activities (homeworks) 145 hours
Expected literature

Expected example literature (parts) :

  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning. Second Edition. Springer.
  • Wickham, H., & Grolemund, G. (2017). R for Data Science. O’Reilly Media.
  • Healy, K. (2018). Data Visualization: A Practical Introduction. Princeton University Press.
  • Salganik, M. (2019). Bit by bit: Social Research in the Digital Age. Princeton University Press.
Last updated on 14-02-2023