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| Title |
Big Data Analysis
|
| Semester |
F2026
|
| Master programme in |
Samfundsøkonomi
|
| Type of activity |
Course |
| Mandatory or elective |
Mandatory |
| Teaching language |
English
|
| Study regulation |
Read about the Master Programme and find the Study Regulations at ruc.dk |
| REGISTRATION AND STUDY ADMINISTRATIVE | |
| Registration |
You register for activities through stads selvbetjening during the announced registration period, which you can see on the Study administration homepage. When registering for courses, please be aware of the potential conflicts and overlaps between course and exam time and dates. The planning of course activities at Roskilde University is based on the recommended study programmes, which should not overlap. However, if you choose optional courses and/or study plans that goes beyond the recommended study programmes, an overlap of lectures or exam dates may occur depending on which courses you choose. |
| Number of participants |
|
| ECTS |
5
|
| Responsible for the activity |
Fuad Mehraliyev (fuadm@ruc.dk)
|
| Head of study |
Nina Torm (ninatorm@ruc.dk)
|
| Teachers |
|
| Study administration |
ISE Tilmelding & Eksamen (ise-eksamen@ruc.dk)
|
| Exam code(s) |
U60888GB
|
| ACADEMIC CONTENT | |
| Overall objective |
Massive amounts of data available in the modern world require new approaches to data analysis. The objective of the course hence is to complement Advanced Econometrics with new approaches suited to analyse big data. |
| Detailed description of content |
This course starts with foundational knowledge such as the definition and nature of big data, its types and sources, as well as its use in socio-economics and business. The course builds upon the coding and analytical skills gained in the Advanced Econometrics course and introduces Python, which is the main coding language used in the course. Some of the themes covered in this course are regression techniques, clustering, dimensionality reduction, classification trees, as well as deep learning and neural networks. The course follows a steady and progressive learning curve suitable for Master’s level study. Teaching consists of ten interactive lectures. The course expects students to do substantial reading and practice with exercises before and/or after the interactive lectures. During the lectures, students are expected to actively participate in discussions and other learning activities. |
| Course material and Reading list |
The course does has not have one specific textbook. A variety of book chapters and research articles will be used as the main learning materials. They will be shown on Moodle shortly before the course starts. |
| Overall plan and expected work effort |
The course is equivalent to 5 ECTS – that is 135 student working hours covering the following activities: Lectures in class: 20 hours; Other activities (exercises etc.): 25 hours; Preparation: 42 hours; Exam: 48 hours; Hours in total 135 hours |
| Format |
|
| Evaluation and feedback |
The course is evaluated regularly. At the start of the course, the course convenor is informed if the course is to be evaluated and notifies the students. The evaluation is carried out in accordance with the study board's evaluation practice. |
| Programme |
|
| ASSESSMENT | |
| Overall learning outcomes |
The student will be able to:
|
| Prerequisites |
|
| Form of examination |
Individual written invigilated exam
The duration of the exam is 3 hours. Permitted support and preparation materials for the exam: Computer without internet access during the exam, pocket calculator, course material and own notes. Assessment: 7-point grading scale |
| Form of Re-examination |
Samme som ordinær eksamen / same form as ordinary exam
|
| Type of examination in special cases |
|
| Examination and assessment criteria (implemented) |
The student's work will be assessed based on the degree to which they have:
GAI rules: In this course, the use of generative AI aids (GenAI) is NOT allowed when taking exams. Ordinary spell checking and other language suggestions as known from Word or other word processing programs are allowed without declaration. |
| Exam code(s) | |
| Last changed | 17/11/2025 |
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