BAN405 Python Programming for Data Science

• Topics

  This course teaches the fundamental programming skills necessary for efficiently handling and analyzing data. Python is an open source, general programming language, that is widely used in both scientific research and business. It has become especially popular in data science due to its versatility, simplicity and large number of powerful third-party packages.

  In today’s data-driven business world, the ability to analyze and interpret data is a critical skill for making informed decisions and building data-driven business strategies. This course equips students with the essential programming skills needed to carry out data science projects in Python.

  The course is intended for students without prior experience with Python or programming in general. The course is split into two main parts. In the first part, students will learn general programming in Python:

  • Setting up a Python environment on your personal computer
  • Write, execute and modify Python code and scripts
  • Use programming techniques such as loops, conditionals and functions to write efficient code

  In the second part, students will learn how to efficiently work with data in Python using the techniques covered in the first part of the course. Students will learn how to extract and handle real-world data, and how to execute simple data visualization and analysis.

  After the successful completion of the course, students will be able to use Python as an analytical tool to solve a wide variety of problems encountered in both scientific research and business.

• Learning outcome

  Upon successful completion of the course, the student can:

  Knowledge

  • Recognize the usefulness of Python in data science
  • Explain the importance of reproducibility and code documentation in data analysis
  • Identify strengths and weaknesses of different data structures in Python

  Skills

  • Set up a Python environment using Anaconda
  • Use git for version control and to create online repositories
  • Write, modify and execute Python code
  • Use basic data structures in Python (e.g., lists, dictionaries, arrays)
  • Create functions and loops
  • Implement control structure to handle errors and exceptions
  • Load, manipulate and save data files
  • Perform simple data analysis (e.g., summary statistics)
  • Create convincing tables and figures for use in reports and presentations
  • Extract data through APIs and web scraping

  General competence

  • Apply Python to empirical business and economics problems
  • Conduct reproducible research and communicate their findings
  • Use package documentation and online sources for help with coding

• Teaching

  Weekly interactive lectures with exercises that are solved in class.

• Recommended prerequisites

  Basic statistical competence equivalent to MET2.

• Credit reduction due to overlap

  There is a full credit reduction between BAN405 and the 7,5 ECTS course BAN438. Note that this also applies to the 2,5 ECTS seminar BAN436, which is the first part of BAN438. This means that if you have passed both BAN405 and BAN438 (or only BAN436), you will be awarded a total of 7,5 ECTS for the two courses combined.

• Compulsory Activity

  There will be two assignments given during the semester that must be completed and approved for course approval.

• Assessment

  6 hour digital school exam with access to Python.

• Grading Scale

  A-F

• Computer tools

  Python, Jupyter Notebook, git. Students are recommended to download the Anaconda distribution for Python. More details regarding the required software will be provided at the beginning of the course.

• Literature

  To be announced in Canvas.

• Permitted Support Material

  One bilingual dictionary (Category I)

  Calculator

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