Digital Methods and Transformation

Course details

• Master Level - 6 ECTS credits
• A variety of social and cultural activities included

Prerequisites

• The Digital Methods and Transformation course is open to students currently enrolled in a Master’s degree programme.
• Students should have a basic level of familiarity with spreadsheet software such as Microsoft Excel or 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.

Dates

• Welcome information 20 June 2020
• Two-week course from 22 June to 3 July 2020
• Final report due on 10 July 2020

Included in the programme

• Lectures in Digital Methods and Transformation, how new technologies are changing business and society, and on Applied Social Network Analysis, a set of methods to understand relational digital data, by 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

About the course 

Digital technologies are increasingly permeating the way we work, live, and think. A crucial aspect of the digital transformation lies in the ever increasing amount of data that is being produced about individuals and organizations alike. Big data is the keyword to characterize the unprecedented volume, velocity, and variety of data being produced in the digital age. Increasingly, organizations are harnessing the power of big data through data mining and analytics. Gaining insights from big data can be challenging and requires specialized knowledge, also in data and network visualization. This course will equip students with a set of analysis techniques to make sense of data in a visual way. A focus will be on social network analysis to study relational data, identify influential nodes in a network, and distinguish communities.

• See the course description for summer 2020.

Learning outcome

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

Acquired Skills 

• 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
• Have a first understanding of complex network modelling methods 

Reflection

• 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

 Topics

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 network analysis software such as 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.