BUS476 Data analysis and thesis development

• Topics

  The course description is under revision and will be published once it is approved.

• Learning outcome

  After completing the course, students will achieve these learning outcomes:

  Knowledge

  Students can:

  • Find and use important sources of financial and non-financial data (such as WRDS, Børsprosjektet, and XBRL databases).
  • Understand methods used by data analysts to explore cause-and-effect relationships in real-world business scenarios.
  • Recognize strengths and limitations of common data analysis methods in accounting and auditing contexts.
  • Know how to use software like R and Tableau to perform data analysis and create meaningful visualizations.

  Skills

  Students can:

  • Develop clear, practical data analysis questions and choose suitable methods to answer them.
  • Extract, clean, and organize financial and textual data, making datasets ready for analysis.
  • Use exploratory data analysis (EDA) and visualization tools (R and Tableau) to identify trends and insights in your data.
  • Manage and complete independent data analysis projects effectively.
  • Document your workflow transparently.
  • Present your analysis results clearly in visual, written, and spoken forms.

  General competence

  Students can:

  • Evaluate existing data analyses critically and use insights to inform your own analysis projects.
  • Confidently plan, organize, and conduct your master's thesis or a practical data-driven project.
  • Effectively incorporate new technologies (such as AI-based tools) into your data analysis workflow, ensuring professional standards and ethical practices.

• Teaching

  The course consists of 10 lectures/classes and 10 practical coding sessions where the students gain hands-on experience working with financial and non-financial data using R (and other data analysis tools). Students need to bring their own computer. Students will work on the assignment (thesis project proposal) in groups from mid-semester (session 6) until the end of the semester. The assignment should be submitted in groups of 2 students. Individual submissions may be permitted, but are subject to approval by the course responsible. Assignments must be written in English.

• Compulsory Activity

  None.

• Assessment

  Group-based thesis proposal (40%) and individual digital school exam (60%)

  Thesis proposal (40%):

  • Students will prepare a thesis proposal in groups of 2 students. Individual submissions may be permitted, but are subject to approval by the course responsible. Students will work on the project from mid-semester (session 6) until the end of the semester. The goal of the assignment is to create a proposal that can be used as a master thesis proposal.
  • Students will use R for data analysis. Both the assignment and the coding/notebooks with comments and explanations for each step of the analysis must be submitted two weeks after the last lecture.
  • Students must specify if they used AI tools and for what purpose in their assignment (e.g., for data processing, generating code, debugging code, or other tasks).
  • The assignment (thesis proposal) has to be written in English.

  Digital Exam (60%):

  • Two (2) hour individual digital school exam.

  • The exam text is in English, and the students must answer in English.

• Grading Scale

  Students will be evaluated based on the A-F grading scale.

• Computer tools

  R, RStudio.

• Literature

  Wickham, H., Cetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd ed.) . O'Reilly Media. Retrieved from https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fr4ds.hadley.nz%2F&data=05%7C02%7CJun.Nguyen%40nhh.no%7C957c181dde7e4b2a1d0908dd5b008c13%7C33a15b2f849941998d56f20b5aa91af2%7C0%7C0%7C638766780079349900%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=gagVf0DZUEXy2FpeABcLLo31Ck66CnLPFtGedho8FCE%3D&reserved=0https://r4ds.hadley.nz/

  Peng, R., & Matsui, E. (2016). The art of data science . Retrieved from https://bookdown.org/rdpeng/artofdatascience/https://bookdown.org/rdpeng/artofdatascience/

  Knaflic, CN (2015). Storytelling with data: A data visualization guide for business professionals (1st ed.) . Wiley.

  BENOIT, Kenneth. (2020). Text as data: An overview . In SAGE Handbook of Research Methods in Political Science and International Relations (pp. 1-55). London: SAGE.

  Gow, ID, & Ding, T. (2025). Empirical research in accounting: Tools and methods. CRC Press. Retrieved from https://iangow.github.io/far_book/https://iangow.github.io/far_book/

• Permitted Support Material

  Calculator.

  One bilingual dictionary (Category I).

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support materialhttps://www.nhh.no/en/for-students/regulations/https://www.nhh.no/en/for-students/regulations/and https://www.nhh.no/en/for-students/examinations/examination-support-materials/https://www.nhh.no/en/for-students/examinations/examination-support-materials/