- 20F-;Ex.class 1;;Introduction to programming and numerical analysis
- 20F-;Ex.class 2;;Introduction to programming and numerical analysis
- 20F-;Ex.class 3;;Introduction to programming and numerical analysis
- 20F-;Ex.class 4;;Introduction to programming and numerical analysis
- 20F-;Ex.class 5;;Introduction to programming and numerical analysis
AØKA08232U Introduction to Programming and Numerical Analysis
This course introduces you to programming and enables you to numerically solve simple economic models and perform basic data analysis. This will e.g. allow you to both visualize solutions, easily test assumptions with respect to functional forms and parameters, and consider more realistic models, which are solvable numerically but not algebraically.
The first part of the course introduces you to programming using the general-purpose Python language. You will learn to write conditional statements, loops, functions, and classes, and to print results and produce static and interactive plots. You will learn to solve simple numerical optimization problems, and draw random number and run simulations. You will learn to test, debug and document your code, and use online communities proactively when writing code.
The second part of the course gives you a brief introduction on how to import data from offline and online sources, structure it, and produce central descriptive statistics. You will learn to estimate simple statistical models on your data.
The third part of the course introduces you to the concept of a numerical algorithm. You will learn how to write simple searching, sorting and optimization algorithms. You will learn to solve linear algebra problems, solve non-linear equations numerically and symbolically, find fixed points, and solve complicated numerical optimization problems relying on function approximation.
You will get hands-on experience with applying the above techniques to solve well-known microeconomic and macroeconomic problems from the core bachelor courses. Specifically, you will work with both a small data analysis project, and a larger model analysis project based on a well-known economic model.
While the course only focus on programming in Python, you will also be equipped to start learning other programming languages (such as MATLAB, R, Julia or even C/C++) on your own.
After completing the course, the student is expected to be able to:
- Describe the differences between data types (e.g. strings, booleans, integers and floats)
- Describe the differences between data containers (e.g. lists, dicts and arrays)
- Explain the use of conditionals (if-elseif-else)
- Explain the use of loops (for, while, continue, break)
- Explain the use of functions, methods and classes
- Describe the difference between views and copies of objects
- Explain how to use (pseudo) random numbers
- Explain the notation of numerical algorithms
- Setup a Python enviroment
- Write Python scripts, functions and notebooks
- Apply error handling and debugging techniques
- Use and write code documentation
- Print results and make static and interactive plots
- Import and export of data from and to csv, excel and online databases
- Perform simple descriptive analysis of numerical data
- Use numerical equation solvers and symbolic equation solvers
- Use numerical optimizers
- Formulate numerical algorithms from mathematical problems
- Solve mathematical problems numerically
- Solve well-known economic problems numerically
- Perform numerical simulation of stochastic models
- Work collaboratively on code projects
- Use online communities to find existing code and get help
- Present and discuss results of a numerical analysis
- Learn new programming tools and languages
The course requires no prior experience with programming.
2 hours lectures once a week from week 6 to 20 (except holidays)
2 hours of execise classes once a week from week 6/7 to 20/21 (except holidays)
The overall schema for Master courses can be seen at KUnet:
MSc in Economics => "courses and teaching" => "Planning and overview" => "Your timetable"
BA i Økonomi/KA i Økonomi => "Kurser og undervisning" => "Planlægning og overblik" => "Dit skema"
Timetable and venue:
To see the time and location of lectures and exercise classes please press the link/links under "Se skema" (See schedule) at the right side of this page (F means Spring).
You can find the similar information in English at
-Select Department: “2200-Økonomisk Institut” (and wait for respond)
-Select Module:: “2200-F20; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”
- Class Exercises
The student will receive:
- written and oral feedback from the teaching assistants on all the projects
- written peerfeedback on the data and model analysis projects
For foreign students not enrolled: Admission requirements, registration etc: Study Economics.
For gæste- og enkelfagsstuderende: Tilmelding via Uddannelse i Økonomi.
- 7,5 ECTS
- Type of assessment
- Portfolio, 48tChanges of 25-03-2020:
The final exam is a written assignment consisting of four parts:
-The first three parts are based on the three projects worked with during the semester. Students can use the peer feedback they receive during the semester to improve the ”model analysis” and ”data analysis” projects for the final exam and the studenst are allowed to improve the ”inaugural project” for the final exam. This can be done before the exam period begins.
- The fourth part of the exam is a new assignment given when the exam period begins.
All four parts of the final exam must be uploaded to the Digital Exam portal and uploaded in one file.
The written exam can be handed in individually or by groups of maximum four students. The plagiarism rules must be complied and please be aware of the rules for co-writing assignments. The exam assignment is given in English and must be answered in English.
- Exam registration requirements
To qualify for the the exam the student must during the course and not later than the deadlines have:
- Completed a basic programming test.
- Approved the inaugural project
- Approved the data analysis project.
- Provided useful peer feedback on at least two data analysis projects.
- Approved the model analysis project.
- Provided useful peer feedback on at least two model analysis projects.
The projects can be handed in individually or by groups of up to three students. However, the peer feedback must be given individually. The plagiarism rules must be complied and please be aware of the rules for co-writing assignments.
The assignments and the peer feedback must be answered in English.
- All aids allowed
Grading of the exam:
Exchange students must be aware, that the assessors and the University are not allowed in any way to reward a student with a grade of numerically or alphabetically value. The course and the exam will only be rewarded with a grade of
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
- Exam period
The exam takes place:
from 16 May 10 AM to 18 May at 10 AM
It takes approximately 24 working-hours to answer the new assignment.
Note: In special cases, the exam date can change to another day and time within the exam period.
Further information about the exam will be available in the Digital Exam portal from the middle of the semester.
The reexam takes place:
31 August from 10 AM to 2 September 2020 at 10 AM
NOTE: If only few students register for the written re-exam, the re-exam might change to a 20 minutes oral examination with 20 minutes preparation time. All written aids allowed during the preparation time, no aids allowed during the examination.
If changed to an oral re-exam, the exam date, time and place might change as well. The Examination's Office then inform the students by KU e-mail.
Info is available in Digital Exam early August.
Criteria for exam assesment
Students are assessed on the extent to which they master the learning outcome for the course.
The final exam tests the students' knowledge, skills, and competencies as described in the course learning outcomes. In order to obtain the grade “Pass”, the student must demonstrate that the knowledge, skills and competencies listed in the learning outcomes are met in a satisfactory way.