BUS476 Data Analysis for Business and Research (E)

• Topics

  This course introduces you to practical data analysis methods widely used by data analysts in accounting, auditing, and financial management. You will gain valuable hands-on experience working with financial and non-financial data, learning essential techniques to prepare you for careers in data-driven roles. The course also serves as an ideal preparation for writing a master's thesis. Each week features one teaching session and one interactive practical class. The focus will be on applied methods with minimal emphasis on theoretical concepts. By the end of the course, you will have the skills and confidence to carry out your own research project successfully.

  Key topics covered in this course:

  • Working with databases : Extracting and using financial data (WRDS, Børsprosjektet, XBRL).
  • Data cleaning and preparation : Handling real-world data issues effectively.
  • Exploratory data analysis (EDA) : Identifying patterns and insights in datasets.
  • Visualization techniques : Communicating clearly and effectively with R and Tableau.
  • Thesis writing and project management: Planning, structuring, and managing your thesis project effectively, including using AI-based tools.

• 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.

• 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://r4ds.hadley.nz/

  Peng, R., & Matsui, E. (2016). The art of data science . Retrieved from 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/

• 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/and https://www.nhh.no/en/for-students/examinations/examination-support-materials/