Big data x business

Syllabus

A French version of the method is available -> here <- .

This content is designed by Clement Levallois, Associate Professor at emlyon business school.

These modules provide to managers an understanding of technical concepts which are now at the center of the business world: APIs, data visualization, machine learning, network analysis, text mining, big data, data protection, etc.

9 modules covering important topics in big data

Each module consists in lecture materials, a bibliography and a quiz. Some modules come with an accompanying video. Check the table of content below!

Group projects

A group project can be added to these lessons. It consists in creating a collective podcast. Each group of participants must identify an expert to interview and will record an episode.

-> Check the podcasts recorded by emlyon participants in 2017/2018.

Table of content

• Module 1. What is "data", "big data" and how is it relevant to business?
  • Essential readings
  • The definition of data ( web page, slides, pdf, source)
  • The definition of big data ( web page, slides, pdf, source)
  • The definition of the "cloud" ( video, web page, slides, pdf, source)
• Module 2. Data integration and APIs
  • Essential readings
  • The headache of data integration ( web page, slides, pdf, source)
  • APIs and their business relevance ( web page, slides, pdf, source)
  • Slideshare by John Musser on 20 business models for APIs.
• Module 3. CRMs and segmentation / clustering
  • Essential readings (same as previous week)
  • CRMs in the age of data ( web page, slides, pdf, source)
  • Segmentation: between marketing and data science ( web page, slides, pdf, source)
• Module 4. Primers on network analysis and text mining
  • Essential readings on text mining
    Essential readings on network analysis
    
  • A primer on network analysis ( web page, slides, pdf, source)
  • A primer on text mining ( web page, slides, pdf, source)
  Application: segmenting wine buyers with Gephi
  • What is Gephi? ( web, slides, pdf, source)
  • A simple Gephi project from A to Z ( web, slides, pdf, source)
• Module 5. Data-driven value creation and business models based on data
  • Essential readings
    
  • The 7 roads to data-driven value creation ( web page, slides, pdf, source)
  • 3 types of business models centered on data ( web page, slides, pdf, source)
• Module 6. IoT, personalization and localization
  a. The business relevance of IoT
  • Essential readings on IoT
  • Definitions of Internet of Things / connected & smart objects ( web, slides, pdf, source)
  • IoT in manufacturing and logistics by Julien Calviac (video).
    IoT and security by Romain Willmann (video).
  b. Personalization, localization and value creation
  • Essential readings on personalization and localization
  • Personalization and value creation ( web, slides, pdf, source)
  • Localization and value creation ( web, slides, pdf, source)
• Module 7. Data visualization
  • Essential readings
    
  • Data visualization: a personal view point on its past, present and future ( web page, slides, pdf, source)
  • L'intérêt de la visualisation des données pour le business (webinar) par Clément Levallois (video).
    
  • Creating a dataviz for a business app: an interview with Jan Willem Tulp (video).
    
• Module 8. Data Protection and compliance
  • Essential readings on data compliance and ethics
    Essential readings on algorithmic transparency
    Essential readings on algorithmic bias and discrimination
    Essential readings on GDPR
    Essential readings on surveillance and profiling
    
  • Privacy and the protection of personal data ( web page, slides, pdf, source)
  • GDPR and data protection globally ( web page, slides, pdf, source)
• Module 9. Data Science, Machine Learning and Artificial Intelligence
  • Introductory readings on data science
    General introductions to AI
    Essential readings on algorithmic transparency
    Business applications of artificial intelligence
    Essential readings on the future of data science and AI
    
  • Machine learning / data science / Artificial Intelligence explained ( web page, slides, pdf, source)