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

Stata Winter School


22 Jan 2018 - 26 Jan 2018


Timberlake (Portugal) and FEP (U. Porto) are jointly organizing a set of applied Econometrics courses using Stata. The aim of these courses is to familiarize the participants with key econometric tools commonly used in applied research. The courses include a quick discussion of the relevant econometric theory as well as an in-depth discussion of empirical applications using real data. The course will take place at FEP (U. Porto) on January 2018. All participants are invited to wine tasting and special dinner on thursday, 25th. This event is included in the price of the Winter School.

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QTEM summer school

Using artificial intelligence to detect trends in the fashion industry


21 Aug 2017 - 27 Aug 2017


This course will walk you through the latest developments in artificial intelligence, data science and their impact on the fashion industry. We introduce basics of web scraping, NoSQL databases and machine learning.

By focusing on the business case of apparel industry that used to rely heavily on intuition and feelings, students get a quick sense of the shift data are making on our economy.

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QTEM summer school

From transfer prices of football players to sushi boxes: understanding pricing models through hedonic price methods


21 Aug 2017 - 25 Aug 2017


  • Professor: Michele Cincera (mcincera@ulb.ac.be) and Julien Ravet (jravet@ulb.ac.be)
  • 5 ECTS - 24 hours
  • Min. 15 students - max. 40 students


What is 'Hedonic Pricing'?
Hedonic pricing is a model identifying price factors according to the premise that price is determined both by internal characteristics of the good being sold and external factors affecting it.
The most common example of the hedonic pricing method is in the housing market: the price of a property is determined by the characteristics of the house (size, appearance, features, condition) as well as the characteristics of the surrounding neighbourhood (accessibility to schools and shopping, level of water and air pollution, value of other homes, etc.). The hedonic pricing model is used to estimate the extent to which each factor affects the price.

Program outline:

  • 18 August, 20h: Welcome speech by Bruno van Pottelsberghe followed by a barbecue
  • 19-20 August: optional cultural activities

Courses (21-25 August):

  • Day1:
    • AM 9-13h: the hedonic price model (theory)
      PM 14-18h: the hedonic regression: tests and hypothesis (theory)
  • Day2:
    • AM 9-13h: introduction to STATA + preparing the data and descriptive stats
      PM 14-18h: the hedonic regression: tests and hypothesis (implementation)
  • Day3: 
    • AM 9-13h: preparation of the report
    • PM 14-18h: preparation of the report
  • Day 4:
    • AM 9-13h: preparation of the report
    • PM 14-18h: visit of a company
    • PM 19h: dinner
  • Day 5: 
    • AM 9-13h: students defence
    • PM 14-18h: students defence
  • 25 August,19h: Graduation ceremony and farewell party

Example of topics (with available datastets of 2017): Football players price transfers; Sushi delivery services; Television prices; Airbnb rentals; MBAs fees; Green tea prices; Champagne prices; Ryanair fares; ...

Practical information

  • Admission requirement: basic course in econometrics
  • Language of the course: English
  • Tuition fees: 1500€ (including teaching material, accommodation, breakfasts, lunches, welcome and farewell + one dinner) NB: QTEM’s students get a 20% discount!
  • Registration: mcincera@qtem.org (deadline 30/7/2017)

Visa: Applicants who need a visa to enter Belgium will receive an Acceptance Letter which declares their registration

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Deadline for subscription: 15 Jul 2017
QTEM summer school

Crash Course in Experimental Economics


17 Jul 2017 - 22 Jul 2017


This one-week crash course is an introduction to the field of experimental economics. At the end of the course you will have the skills needed to design and run an experiment as part of your thesis. In 2016, we offered a new feature at the end of the crash course: an additional one-day module on Statistical Data Analysis.

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Deadline for subscription: 15 Jun 2017
QTEM summer school

 ISU Frankfurt - European Finance


16 Jul 2017 - 12 Aug 2017


The Hessen International Summer University Frankfurt consists of academic tracks in European Finance and European Studies, German language courses, and a cultural program. The program is unique in that it takes place at two different universities in Frankfurt - at the Frankfurt University of Applied Sciences and Goethe University Frankfurt am Main. You will spend two weeks at each university (for a total of four weeks) and participate in a cultural program with extracurricular activities to help you discover Frankfurt and Europe!



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Deadline for subscription: 31 May 2017
QTEM summer school

QTEM International Summer School of Business Analytics


2 Jul 2017 - 7 Jul 2017



  • July 2: Registration
  • July 3:
    • Morning- Opening ceremony
    • Speech by Mr. Yu LIU from Getui Company: Mobile device data and precision marketing
    • Afternoon- Seminar by Prof. Guoming LAI: Big data and contemporary business operations
  • July 4:  
    • Seminar by Prof. Sha YANG: Methods in empirical modeling of consumer behavior
  • July 5:  
    • Seminar by Prof. Chenhui GUO: Econometric models for business research
  • July 6:  
    • Seminar by Prof. Daning HU: Business network analysis and applications
  • July 7:  
    • Morning- Seminar by Prof. Alvin LEUNG: Empirical research methods to analyze online investors' digital footprints
    • Afternoon- Speech by Dr. Ziming ZHUANG from Alibaba Company: Opportunities and challenges for content-based e-commerce
    • Closing ceremony
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Deadline for subscription: 20 May 2017
QTEM summer school

Data Analysis in Business


29 Jun 2017 - 14 Jul 2017


This three-week study program gives you the chance to learn about the dynamic field of data analysis. You will study at a master’s level with MBA and graduate students from across Monash Business School. Covering a range of topics in data analysis around descriptive statistics, data visualisation, regression, time series and forecasting, the Data Analysis in Business unit will give you a solid foundation for data analytics within business contexts. The program is designed by leading academics and data scientists. It draws on current research and trends in description and summation, data mining, confidence intervals and hypothesis testing, regression analysis, time series and forecasting, and decision making under uncertainty


The first objective of this unit is for students to understand which data analysis technique is appropriate to address a business problem and then, with the support of the relevant software, to apply that technique. The second objective is for students to learn how to interpret the results and extract useful business insights. An applied approach will be taken. Topics covered include data description and summation, data mining, confidence intervals and hypothesis testing, regression analysis, time series and forecasting and decision making under uncertainty. Software used will be Microsoft Excel.


The learning goals associated with this unit are to:

  1. build the analytical skills of the student through their understanding and applying a range of data analysis techniques
  2. develop an understanding of how to interpret statistical findings and draw relevant insights in a business context
  3. enhance report writing skills by developing an understanding of the process of preparing a business report based on insight gained from statistical analyses
  4. enhance communication, interpersonal, problem solving and critical thinking capabilities
  5. develop capabilities to be future generators of sustainable economic, social and environmental value for business.
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