GRA 6839 Data Analysis in Python

GRA 6839 Data Analysis in Python

Course code: 
GRA 6839
Course coordinator: 
Alfonso Irarrazabal
Course name in Norwegian: 
Data Analysis in Python
Product category: 
MSc in Business - Elective course
2021 Autumn
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course provides the tools to use Python programming language to extract knowledge from data. We will start with an introduction of basic concepts in programming. You will learn how to write short codes, read other people’s codes, automate tasks etc. Then, we will use the Pandas package to work with data collection, statistical and graphical analysis. We will learn the power of dataframes, which allows you to work productively with data. You will learn techniques for loading, cleaning, combining, slicing, and transforming data. Finally, you will be able to combine your data to statistical models and present the results in tables and graphs.

Learning outcomes - Knowledge

Students should be able to

  • Understand the basic elements of programming in Python.
  • Gain basic knowledge of data analysis and visualization techniques.
  • Learn how to implement econometric analysis.
Learning outcomes - Skills

Students should be able to 

  • Develop analytical and digital skills associated with programming and data management. 
  • Read, implement and create new codes in Python.
  • Apply techniques to prepare, transform and analyse data.
General Competence
  • Appreciation of details in the process of data analysis using advanced programming techniques.
  • Demonstrate abilities of analytical and critical thinking.
  • Critical reflection and thinking about translating analysis into programming codes. 
  • Explore the value of data in relation to corporate social responsibility and sustainability goals.
Course content

The course covers the following topics

  • Introduction to programming.
  • Computational tools for scientific programming.
  • Basic data analysis and visualization
  • Applications to economics and finance
Teaching and learning activities

All software is open source and therefore free. We will use Jupyter notebooks and Python.

Software tools
No specified computer-based tools are required.
Additional information

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.


All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have spesific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.


Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.


Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.

Required prerequisite knowledge

Knowledge of basic calculus and statistics.

Exam categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
Grading scale:
Grading rules:
Internal and external examiner
Examination when next scheduled course
100No72 Hour(s)Group/Individual (1 - 3)Computational project
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Group/Individual (1-3)
Duration:72 Hour(s)
Comment:Computational project
Exam code:GRA68391
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 3 ECTS credit corresponds to a workload of at least 80 hours.