Doctoral Programme in Microdata Analysis

Use data-based methods in problem-solving research as you specialise within the field of microdata analysis. Doctoral studies will broaden your knowledge and understanding, while you develop as an independent researcher in a cross-disciplinary environment.

The objective of this programme is to educate doctoral students of Microdata Analysis so that they attain broad general knowledge and attractive specialist competence in the fields of Business Intelligence, Analytics, and Data Science that meet market and academic demands.

Students in this programme have diverse backgrounds – for example, business, computer/information science, engineering, and applied mathematics/statistics. The programme forms part of the reserach in Data and Information Management.

Microdata Analysis

Microdata analysis is a multidisciplinary field of knowledge that deals with the collection, modelling, compilation and interpretation of large amounts of data, as well as underlying algorithms, methods and techniques. Microdata analysis encompasses several interacting sub-areas such as artificial intelligence, decision support systems, management of limited resources, data modelling, experimental design, focus groups, geographic information systems, visualisation, measurement techniques, optimisation, forecasting, simulation and statistical inference. The subject is normative and its aim is data-driven decisions and actions.

Learning Outcomes

The first part of the programme comprises the collection of data and requires knowledge and understanding of various measurement techniques as well as the design of experiments.
The second and third parts comprise data capture, data processing and data storage, and require knowledge and understanding of advanced database methods as well as comprehension of the importance of metadata.
The fourth part is the analysis, often in the form of mathematical modelling of data that requires skills in statistical modelling, forecasting methods, simulation techniques, visualisation and data mining.
The fifth part comprises decision-making and action that require an understanding of techniques such as benchmarking and counterfactual analysis as well as economic decision-making and the dissemination of information within organisations.
The Doctoral Programme in Microdata Analysis is for students who wish to acquire skills in all parts of the process as well as expertise in a specific area.

Structure of the programme

In essence, it is the student supported by the supervisory team who defines the plan, the aim of which is graduation as researcher. However, a general curriculum frames the plan and at the start of the programme, the student and the supervisory team develop an individual study plan that serves both to define the student's research activities and to monitor student progress.

How to apply

We look for prospective students who want to develop as researchers in an interdisciplinary environment. You should have skills in data management and analysis with the objective of creating business value. To be eligible, you must hold at least a one-year master's degree in the field. Additional prerequisites are listed in the general programme curriculum.

You can apply in two ways.

  1. In the spring semester, doctoral students are recruited who will commence their studies in the autumn semester. Positions are advertised under Vacant Positions on the university website.
  2. There may also be doctoral student positions in specific research projects that are open for application throughout the year. These positions are also advertised under Vacant Positions on the university website.

Supervisors and Students

The primary supervisors guide the student towards the successful completion of the programme, which concludes with a thesis publicly reviewed at the thesis defence. Each student is assigned a primary supervisor, who establishes the supervisory team. This team holds complementary competencies to optimise the student's research support throughout the programme.

 

Doctoral Courses

The course has no instances planned right now.
5 Credits, Third Cycle
The course has no instances planned right now.
7,5 Credits, Third Cycle
The course has no instances planned right now.
Autumn 2024 (week 36), 50%
Distance, Flexible (Falun), in English
Autumn 2024 (week 36), 50%
Distance, Flexible (Falun), in English
Semester:
Autumn 2024
Start week:
Start: week beginning 2 Sep 2024
Study Rate:
50%
Location:
Flexible (Falun)
Time of Day:
Day
Teaching form:
Distance
Language:
English
Application Code:
HTVV3KRN
Entry Qualifications:
  • To be admitted, applicants must meet the general entry requirements for doctoral studies. Persons who have not been admitted to a doctoral programme at Dalarna University are admitted to the course depending on space availability.
Online Education
Computer Skills:

Surf on the Internet, read and send emails, make use of a word-processing programme (e.g. Word).

Computer Capacity:

Access to a computer (Not a mini pc, tablet or similar) with broadband connection, at least 0.5 Mbit/s both directions.

Means of Education:

Various communication, e.g. e-mail, and use of basic functions in our learning platform (Learn/Canvas), such as finding information and being able to submit files.

The course has no instances planned right now.
The course has no instances planned right now.
7,5 Credits, Third Cycle
Spring 2025 (week 4), 50%
Distance, Flexible (Falun), in English
Spring 2025 (week 4), 50%
Distance, Flexible (Falun), in English
Semester:
Spring 2025
Start week:
Start: week beginning 27 Jan 2025
Study Rate:
50%
Location:
Flexible (Falun)
Time of Day:
Day
Teaching form:
Distance
Language:
English
Application Code:
VTPA3KRS
Entry Qualifications:
  • General entry requirements for postgraduate studies.
Online Education
Computer Skills:

Surf on the Internet, read and send emails, make use of a word-processing programme (e.g. Word).

Computer Capacity:

Access to a computer (Not a mini pc, tablet or similar) with broadband connection, at least 0.5 Mbit/s both directions.

Means of Education:

Various communication, e.g. e-mail, and use of basic functions in our learning platform (Learn/Canvas), such as finding information and being able to submit files.

10 Credits, Third Cycle
The course has no instances planned right now.
Last reviewed:
Contacts
Mia Xiaoyun Zhao
Senior Lecturer Information and Communications Technology
Director of Doctoral Studies
Doctoral Programmes Coordinator
Last reviewed: