Our online and offline actions increasingly leave digital traces that are a treasure trove for analysing social behaviour, both for academics and companies. These traces are often in textual form, such as Facebook
and Twitter posts, product reviews, and online profiles; or in the form of large semi-structured data sets such as communication logs and purchasing records. The unstructured nature of these data poses a challenge to the social scientists or analyst, as new techniques such as text and network analysis are needed to explore, visualize, interpret, and test hypotheses using these data.
Each week, students will work in small teams on a specific challenge relating to text and data analysis. Near the end of the week, you present the results to your peers and give each other feedback. This results in a written research report submitted at the end of the week.
Course Description
Course objectives
After taking this course, you have acquired knowledge and understanding of:
- the formulation of research proposals, including design, methodology, procedure and data analysis
- advanced issues in computational methods, specifically: data modeling and visualization; machine learning; text analysis.
Additionally, you have acquired the competences to:
- conduct advanced analyses in computational research and analytical
methods, including: data modelling and visualization; text analysis; machine learning.
Moreover, you will be able to:
- reflect critically on the validity and scientific and societal relevance of text and data analysis results.
Finally, you will have acquired the skills to:
- Communicate the results of data analysis in a clear and accurate way
to an academic audience using appropriate visualizations in a written report and oral presentation.
Each week, students will participate in four meetings, for which attendance will be required:
- An interactive lecture introducing the main methodology taught in that week;
- Two computer practicals in which students practice the main techniques
and work on their assignments;
- A closing workshops where students present their (draft) assignments and give each other feedback.
See the daily schedule at the end of this document for more information.
Study Characteristics
- Discipline: Social Sciences
- Type of education: in class
- Academic skill: Methods
- Graduate School: Graduate School of Social Sciences
- Start date: 09.01.2023
- End date: 03.02.2023
- Minimum number of students: -
- Maximum number of students: 5
- Admission criteria: PhD candidate from the VU-GSSS
- Concluding assessment: yes
- Assessment type: Written assignments
- With Certificate: yes
- Schedule info: 16 sessions in total, schedule information available here.
- Registration deadline: 01.12.2022
- Available to: PhD students VU-GSSS only
- Name of teachers: prof. dr. W.H. van Atteveldt, dr. K. Welbers
-
Course Description & Study Characteristics
Course Description
Course objectives
After taking this course, you have acquired knowledge and understanding of:
- the formulation of research proposals, including design, methodology, procedure and data analysis
- advanced issues in computational methods, specifically: data modeling and visualization; machine learning; text analysis.
Additionally, you have acquired the competences to:
- conduct advanced analyses in computational research and analytical
methods, including: data modelling and visualization; text analysis; machine learning.
Moreover, you will be able to:
- reflect critically on the validity and scientific and societal relevance of text and data analysis results.
Finally, you will have acquired the skills to:
- Communicate the results of data analysis in a clear and accurate way
to an academic audience using appropriate visualizations in a written report and oral presentation.
Each week, students will participate in four meetings, for which attendance will be required:
- An interactive lecture introducing the main methodology taught in that week;
- Two computer practicals in which students practice the main techniques
and work on their assignments;
- A closing workshops where students present their (draft) assignments and give each other feedback.
See the daily schedule at the end of this document for more information.
Study Characteristics
- Discipline: Social Sciences
- Type of education: in class
- Academic skill: Methods
- Graduate School: Graduate School of Social Sciences
- Start date: 09.01.2023
- End date: 03.02.2023
- Minimum number of students: -
- Maximum number of students: 5
- Admission criteria: PhD candidate from the VU-GSSS
- Concluding assessment: yes
- Assessment type: Written assignments
- With Certificate: yes
- Schedule info: 16 sessions in total, schedule information available here.
- Registration deadline: 01.12.2022
- Available to: PhD students VU-GSSS only
- Name of teachers: prof. dr. W.H. van Atteveldt, dr. K. Welbers