QTEM summer school

Digital Transformation: Applying Social Network Analysis


25 Jun 2018 - 6 Jul 2018



Digital technologies are increasingly permeating the way we work, live, and think. This summer school course intends to equip students with a set of analysis techniques to understand the antecedents, effects and outcomes of this digital transformation better. The focus will be on social network analysis as a method to study the social and economic implications of digital technology from an empirical point of view.


  • Master Level - 6 ECTS credits
  • A variety of social and cultural activities included
  • Eligible applicants must have a completed degree comparable to a bachelor’s degree. Students are expected to have taken classes in statistics and have working knowledge of MS Excel and SPSS. We expect students to have a solid grasp of the English language as well as a strong interest in the issues at hand, and to actively participate in class.


  • Two-week course from 25 June to 6 July
  • Welcome information 23 June


  • Lectures in Digital Transformation – Applying Social Network Analysisby highly qualified faculty members from BI Norwegian Business School
  • Social and cultural activities – including a weekend expedition, sightseeing and outdoors activities (hiking, climbing, barbecues)
  • Insight into Norwegian industries through guest lectures and company visits


After taking this summer school course, students will

  • understand digital transformation better
  • be equipped with the concepts to analyse relational dynamics of social and digital media effectively
  • have the methodological tools at hand to analyse social networks from a range of data
  • be able to interpret network visualisations and articles using social network analysis appropriately


  • Practical skills to collect social media data – APIs, SQL, etc.
  • Ability to conduct social network analysis on a range of data using software such as Gephi, Netlytic and R
  • Network visualisation skills using Gephi and other software (e.g., UCINET Netdraw)
  • Have a first understanding of complex network modelling methods (e.g., ERGM, Siena)


  • Developing a critical understanding of the managerial and social challenges of digital transformation
  • Understanding the relational nature of society
  • Interpreting the dynamic role of influence in social networks as expressed in phenomena like influentials and opinion leaders
  • Reflecting on the role of data, especially big data, and its role in transforming work and society
  • Integrating key concepts learned in the course such as social capital and social networks


The following topics will be covered during the two weeks: 

  1. Introduction: Why Social Networks?
    An overview of the social networks approach, and a showcase of current examples in the form of interesting research and company studies concerning challenges of digital transformation. Students will get a first grasp of practical questions and challenges related to new forms of production and work that come with digital technologies.
  2. Principles of Social Network Analysis I
    The scientific origins of social network analysis, introducing some fundamental concepts from graph theory. Introduction of concepts such as ego, group, and global networks, and their applicability to real-world challenges.
  3. Principles of Social Network Analysis II
    Core Concepts of Social Network Analysis will be introduced further, such as network structure, and network centrality.
  4. Practice-Oriented Social Network Analysis
    Introduction to software such as pajek and gephi
  5. Essentials of Data Gathering
    Sources for Gathering Social Network Data, from APIs to repositories to services. Introduction to working with SQL-Databases
  6. Visual and Qualitative Social Network Analysis
    Qualitative Gathering and Analysis of relational Data, Network Visualisation and Visual Analysis of Networks
  7. Advanced Quantitative Social Network Analysis
    Regression and work with R
  8. Project Presentation and Discussions
    Students will present their findings to the group and discuss them critically with the other participants in the course. The main findings, implications and challenges during the research project are addressed, trying to condense the key learning across the groups.
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Deadline for subscription: 1 Apr 2018
QTEM summer school

Your Data Driven Summer


9 Jul 2018 - 20 Jul 2018


Data Science is becoming more and more essential to find answers to the challenges of our era. To accelerate data science-based solutions, the Jheronimus Academy of Data Science (JADS) was formed. JADS is an unique concept in the Netherlands: At three different locations  - the JADS Mariënburg campus in ‘s- Hertogenbosch, Eindhoven University of Technology (TU/e) and Tilburg University - Data Science can be studied, researched and applied at the highest level.

  • Content:
    Over two weeks you take courses in five Data Science focus fields and at three different campuses. It’s a great introduction to the world of data science, and the multidisciplinary excellence of JADS.
  • Entry requirements:
    Students with an interest in the field of Computer Science and Data Science, currently pursuing a Bachelor’s of Master’s degree. Please be aware that English proficiency (spoken and written) is required.
  • Start date:
    Monday 9 July 2018
  • Duration | ECTS:
    2 weeks | 4 ECTS
  • Language of instruction:
  • School:
    Jheronimus Academy of Data Science
  • Course fee:

    Course fee: €1000

    • 10% early bird discount when registering before the 1st of April*
    • 10% discount for current Tilburg University students*
    • 10% discount for students from partner universities*

    *Combining discounts is not possible, discounts are not applicable for Language courses

PLEASE NOTE: This course is 4 ECTS and, therefore, cannot count towards a QTEM exchange replacement.

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Deadline for subscription: 31 May 2018
QTEM summer school

Data Science in the Fashion Industry


17 Aug 2018 - 26 Aug 2018


Digitalization has proved to have a profound impact on fashion industry. It moved retailers from abrand loyal customer base market to a strongly competitive landscape on which educated customers are comparing assortments to get the most value in their purchase. On the other hand, Big Data techniques are enabling fashion retailers to make smarter data-driven decisions on their assortments, pricing and discount to react quickly and stay competitive in this new environment.
In this summer school, active entrepreneurs from the Big Data and Analytics industry will introduce you to the business challenges raised by this data analytics (r)evolution. But also, how to tackle them through practical case studies.

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