QTEM summer school

Digital Methods and Transformation - Applied Social Network Analysis.

BI NORWEGIAN BUSINESS SCHOOL

24 Jun 2019 - 5 Jul 2019

Oslo (Norway)

Introduction

Digital technologies are increasingly permeating the way we work, live, and think. This summer school 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. In particular, during two weeks, we encourage students to reflect on the impact of digital technologies on the way we work, may it be through participating in new modes of virtual work, may it be through new forms of crowdworked creativity, or participating in new forms of collaboration that combine elements of work and play. We will try to uncover and find managerial points of action for instance for the sharing economy, to the practice of influential marketing, to new forms of algorithmic management, to other new forms of working.

Our search for solutions will be underpinned by learning about social network methods. Social network analysis is interested in the relational properties of organizations and individuals. While the method has been developed in the pre-digital era for small-scale data, it is especially suited for user-generated trace data that contains relational elements. By using social network analysis, communities and sub-communities can be identified and clustered based on core attributes. Moreover, social network analysis is a key method to identify influentials or important and noteworthy elements in a network. Finally, new developments in social network analysis in recent years, such as exponential random graph models (ERGM), allow to make statistical inference with network data and test for the presence of structural effects such as homophily, reciprocity, and transitivity. Thus, social network analysis is a versatile and widely used method with many benefits, especially in times of big data and user-generated data from social and digital media. A solid foundation in social network theory and methods will provide the students not only with a toolset to analyze communities effectively but also with a relational way of thinking through core concepts of the method.

Course outline

Course content

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 Visualization and Visual Analysis of Networks
 

7. Advanced Quantitative Social Network Analysis 
Regression and work with R
 

8. Project Presentation and Discussions 

The students will present their findings to the group and discuss them critically with the other participants of the course. The main findings, implications and challenges during the research project are addressed, trying to condense the key learning across the groups.

Perequisite

Learning process and requirements to students

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.

Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class that is not included on the course homepage/its learning or text book.

This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course starts.

At resit, all exam components must, as a main rule, be retaken during next scheduled course.

Computer-based tools: Gephi, Netlytic, Sentistrength, R

Software tools

Qualifications

All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.

Registration

https://www.bi.edu/programmes-and-individual-courses/international-summer-programme/

Registration deadline: 1 April 2019

Other information

https://programmeinfo.bi.no/nb/course/GRA-6847/2019-spring

ECTS equivalent

0.0000

Local credits

6.0000
Read more
Deadline for subscription: 1 Apr 2019
Contact: Dorothee Witte - dorothee.witte@bi.no
QTEM summer school

Data Science in the Fashion Industry

SOLVAY BRUSSELS SCHOOL OF ECONOMICS AND MANAGEMENT, ULB

16 Aug 2019 - 25 Aug 2019

Brussels (Belgium)

Business focus
Digitalization has proved to have a profound impact on fashion industry. It moved retailers from a brand 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.

Course outline

Develop your Data Science skills
This summer school is a unique opportunity to develop or improve your practical data science skills heavily demanded by most industries today.

This course will walk you through the latest developments in Artificial Intelligence, Data Science and their impact on the fashion industry. It will be articulated in five full-day courses: 2 hours theory and 5 hours practice on average per day.

We will introduce basics of web scraping, NoSQL databases, Data Mining, Machine Learning and Text Mining. All concepts will be developed through guided and practical sessions where you will get familiar with Python, one of the most intuitive, versatile and popular programming languages for Data Scientist, but also one of
the most prized by employers.

A certificate will be provided to the participants at the end of the summer school. This certificate should allow them to obtain 3 ECTS in their respective institutions

Why take this course

Learning outcomes All attendees will have the opportunity to improve their professional skills, apply their academic knowledge to real situations and build their professional network.

Perequisite

Entry Requirements
To be qualified for the Solvay Summer School Programme participants must be officially enrolled in a School or a University of higher education; inother words, being a current bachelor is required preferably in a faculty of Economics and Business, Master of science, Human Sciences, Natural Sciences or in a Master of Science programme in an other graduate school.

Minimum age: 18

English is the official language of the Solvay Summer School (an english certificate may be required).

Fees QTEM students

1000 euros

Registration

Selection Process
1. Application To apply for Solvay Summer school Edition 2019 on August 16th-25th, candidates have to send their CV to info.summerschool@solvay.edu. The final application deadline is June 30th 2019.

2. Selection After a thorough selection, all candidates will receive an e-mail to confirm their status. The selected student will have 5 days to fill in a special form.

3. Invoice It will be sent to the candidates who will, in return, have 10 working days to pay for the Solvay Summer School programme. Beyond that deadline, their application will be denied.

4. Finalization After receiving the payment, enrolled candidates will receive an official invitation letter from the President of the Solvay Summer School 2019 and the Dean of Solvay Brussels School of Economics and Management.

ECTS equivalent

0.0000

Local credits

3.0000
Read more
Deadline for subscription: 30 Jun 2019
Contact: info.summerschool@solvay.edu

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