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2023/2024  BA-BHAAI1109U  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 Summer
Start time of the course Summer, Summer, Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 100
Study board
Study Board for BSc in Economics and Business Administration
Course coordinator
  • Zoltan Fazekas - Department of International Economics, Goverment and Business (EGB)
Main academic disciplines
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 22-11-2023

Relevant links
Learning objectives
  • 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 introductory machine learning techniques to business and social data.
  • Effectively communicate your results, processes, 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.
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 Project
Release of assignment An assigned subject is released in class
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer, Course and exam timetable is/will be available on https:/​/​www.cbs.dk/​en/​study/​cbs-summer-university/​courses-and-exams.
Make-up exam/re-exam
Same examination form as the ordinary exam
1st retake: 72 hours home assignment, max. 10 pages.
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Description of the exam procedure

Students will be required to carry out data analysis using the tools discussed in the course in order to support substantive conclusions for business and social applications.

 

Home assignment written in parallel with 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. This course will provide you with a very applied introductory knowledge about working with data to understand and inform various business and social decisions. Upon completing the course, you 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, you will spend a substantial amount of time working with software. All course content and 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 in person as a mix of lectures, exercises, and activities, allowing for coding together and code review. Some sessions will also have supporting video materials, which will help preparation and provide additional examples.
Feedback during the teaching period
Feedback will be offered throughout the course in a continuous manner. In-class coding and data related activities, case studies, and polling sessions will take place regularly during the lectures.
Student workload
Lectures 38 hours
Preparation for classes 120 hours
Exam ( including exam preparation) 48 hours
Further Information

3-week course that cannot be combined with other courses.

 

Preliminary Assignment: The Nordic Nine pre-course is foundational for the summer university and identical for all bachelor courses. Students will receive an invitation with all details by the end of May. The assignment has two parts. 1.) online lectures and tutorials that student can access at their own time and 2.) one synchronous workshop which will be offered both online and in-person at several dates and times before the official start of the summer university courses. Sign-up is first come first serve. All students are expected to complete this assignment before classes begin.

 
 
Expected literature
  • James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning. Second Edition. Springer.
  • 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 22-11-2023