- 21F-;Ex.class 1;;Introduction to programming and numerical analysis
- 21F-;Ex.class 2;;Introduction to programming and numerical analysis
- 21F-;Ex.class 3;;Introduction to programming and numerical analysis
- 21F-;Ex.class 5;;Introduction to programming and numerical analysis
- 21F-;Ex.class 6;;Introduction to programming and numerical analysis
- 21F-;Ex.class 7;;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.
We focus on you getting hands-on programming experience right from the start of the course. To this end, you will get access to the online learning platform DataCamp. On DataCamp, you will solve programming exercises and be able to view additional instructional videos, which will be helpful for your advancement in the learning outcomes.”
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 also draws on material from "Probability Theory and Statistics", Microeconomics I and Macroeconomics I, which therefore all courses at the Study of Economics, University of Copenhagen, (or similar courses) should either be followed simultanously or have been followed befor taken the programming course.
The course requires no prior experience with programming.
In case of a pandemic like Corona the teaching in this course may be changed to be taught either fully or partly online. For further information, see the course room on Absalon.
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 "Timetable"/"Se skema" 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-F21; [Name of course]”
-Select Report Type: “List – Weekdays”
-Select Period: “Forår/Spring – Week 5-30”
Press: “ View Timetable”
Please be aware of the rules regarding exercise classes:
- The schedule of the exercise classes is only pre-planned and the schedule can change until the teaching begins without the participants´ acceptance. If this happens, you can see the new schedule in your personal timetable at KUNet, in the app myUCPH and at the links in the right side/the link above.
- That the study administration allocates the students to the exercise classes according to the principles stated at KUnet.
- If too many students have wished a specific class, students will be registered randomly at another class.
- It is not possible to change class after the second registration period has expired.
- The exercise classes may be jointed, if there is not enough registered students or available teachers to teach the classes.
- The student is not allowed to participate in an exercise class not registered.
- The teacher of the exercise class cannot correct assignments from other students than the registered students in the exercise class except with group work across the classes.
- That it is the students´s own responsibility to continuously update themselves about their studies, their teaching, their schedule, their exams etc. through the study pages, the course description, the Digital Exam portal, Absalon, KUnet, myUCPH app, the curriculum etc.
- Class Instruction
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 gæste- og enkelfagsstuderende: Tilmelding via Uddannelse i Økonomi.
- 7,5 ECTS
- Type of assessment
- Portfolio, 48tThe exam is a written assignment consisting of two parts:
• Part 1: The first part is based on the three mandatory assignments worked with during the semester. Students can use the peer feedback received during the semester to improve these assignments. This can be done before the exam period begins.
• Part 2: The second part is a new assignment given in English. It takes approximately 24 hours to answer the new assignment.
Please be aware that:
• The new assignment can be written individually or by groups of maximum four students.
• The plagiarism rules must be complied and please be aware of the rules for co-written assignments.
• All parts must be answered in English and all parts must be uploaded to Digital Exam in one file.
- Exam registration requirements
To qualify for the exam the student must no later than the given deadlines during the course:
- Complete a basic programming test.
- Hand in 3 out of 3 mandatory assignments to be appoved.
- Provided useful written peer feedback based on specific criteria for a minimim of 2 out of the 3 mandatory assignments from other groups.
Please be aware of:
- The teaching assistants and/or the lecturer control the feedback.
- The assignments can be written individually or by groups of maximum three students. The peer feedback must be written individually.
- The plagiarism rules must be complied and please be aware of the rules for co-written assignments.
- The assignments and the peer feedback must be written in English.
- The mandatory assignments and the peer feedback are part of a portfolio exam. See “Type of assessment”
- All aids allowed
for the regular written exam.
In case of an oral reexam, please go to the section "Reexam" for further information about allowed aids.
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
for the written exam.
- Exam period
The regular exam takes place:
from 28 May 2021 at 10 AM to 30 May at 10 AM
Note: In special cases, the exam can change to another day.
Further information about the exam will be available in Digital Exam from the middle of the semester.
The reexam takes place:
30 August 2021 from 10 AM to 1 September at 10 AM
NOTE: If only few students register for the written re-exam, the 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 date, time and place might change as well, which will be informed by KU e-mail.
In case of a pandemic situation the form of the exam and allowed aids may change as well.
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.