ECON4170 – Data Science for Economists
This course is equivalent with ECON3170 – Data Science for Economists
Knowledge of computers and programming is becoming more important, also for economists. This course is aimed at introducing programming and computational tools useful for future careers as economists.
The first part of the course is an introduction to programming and common programming structures. The course goes on to cover manipulation of data, data analysis including an introduction to machine learning techniques, and basic numerical methods useful in economics.
Know how to use computers to analyze data
Basic knowledge of how computers work and what it implies for computation
Common components of computer algorithms such as conditionals, loops, and functions
How data can be visualized and some characteristics of good visualizations
Knowledge of how numerical problems can be solved using computers
Write a program in R to undertake analysis of data or numerical problems
Import data from various sources and in different formats and transform them into an analyzable format
Use the basic tools used in machine learning such as cross-validation as well as basic algorithms such as LASSO and random forests
Implement algorithms for solving numerical problems such as taking derivatives, solving equations, and maximizing functions
Knowledge of how computers and data science can be used to study economic and social phenomena
The limitations of data science approaches to studying human behavior
Students admitted to study programmes at UiO must each semester register which courses and exams they wish to sign up for in Studentweb.
Students not admitted to the Master’s programme in Economics or the Master’s programme in Economic Theory and Econometrics (Samfunnsøkonomisk analyse), can apply for admission to one of our study programmes, or apply for guest student status.
Recommended previous knowledge
Access to teaching
Lectures and seminars.
Assessment is based on
- a group assignment (counting 40% of the total grade)
- a 3-hour written examination (counting 60% of the total grade)
The topic for the group assignment is selected within some given categories, and must be approved by the course coordinator early in the semester. Deadline for submission will be before the written examination, at the end of the semester.
Both exams must be passed the same semester in order to receive a valid final grade.
If the Covid-19 situation makes it impossible to offer a written school exam in this course this fall, the exam will be a 5-hour digital home exam. The time for the exam might then be changed (but not the date). Information will be provided well in advance of the exam, if such changes are made.
The written examination is conducted in the digital examination system Inspera. You will need to familiarize yourself with the digital examination arrangements in Inspera.
Submit assignments in Inspera
You submit your assignment in the digital examination system Inspera. Read more about how to submit assignments in Inspera.
Use of sources and citation
Examination support material
Resources allowed: Open book examination where all printed and written resources are allowed. Some material will be available in Inspera. Further notice will be given.
Language of examination
The examination text is given in English.You may submit your response in Norwegian, Swedish, Danish or English.
Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. Read more about the grading system.
Explanations and appeals
Resit an examination
If you are sick or have another valid reason for not attending the regular exam, we offer a postponed exam later in the same semester.
See also our information about resitting an exam.
Withdrawal from an examination
It is possible to take the exam up to 3 times. If you withdraw from the exam after the deadline or during the exam, this will be counted as an examination attempt.
Special examination arrangements
Application form, deadline and requirements for special examination arrangements.
This is a master's level course, but still open for bachelor's students with sufficient background in mathematics and statistics.
ECON3170 – Data Science for Economists is the course code for the bachelor's students.
ECON4170 – Data Science for Economists is the course code for the master's students.