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Postgraduate Studies

Big Data & Analytics in Business and Management

6 okt. 2023 - 15 dec. 2023

Bridge the gap between technical concepts and business applications of big data and analytics techniques.

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Praktische info:

6 okt. 2023 - 15 dec. 2023
Leuven
Engels
Doelgroep: chief data officer, chief analytics officer, compliance and risk officers and managers, data architects, data project managers, junior industry data scientists, data-related programme managers, business analysts, product managers, IT project managers, etc.

Inschrijven?

  • Inschrijvingen: tot 15 sep. 2023
  • Voorwaarden: academic or professional bachelor degree with a min. of one year of practical experience in business analysis, data analytics, data anlysis, statistics, data management, project management...
  • Prijs: €5500
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georganiseerd door:

Aim of the programme

On the one hand, the programme will provide participants with a thorough understanding of the key technical aspects of big data and analytics technologies. On the other hand, the programme focuses on business and management applications, substantiating how big data and analytics techniques can create business value and providing insights on how to manage big data and analytics projects and teams. As such, this inherent interdisciplinary focus is the unique selling point of our programme.

The programme is founded on the lecturers' experience of advising and collaborating with industry and governmental organizations in Belgium and abroad. After successful completion of the programme, participants will be up-to-date with the latest developments related to big data and analytics. Furthermore, they will understand how to apply these techniques in practice and create value for their organization. The programme is relevant for participants from different sectors: business (marketing, finance, auditing, HR, CRM, consulting), government (fraud, open data, process optimization), healthcare (care flows, data-driven diagnostics), media (sentiment analysis, opinion mining, usability analytics), education (evidence-based performance management, learning analytics), energy & transport (smart data).

Programme Structure

1. Fundamentals of Data Analytics

  • Predictive analytics: supervised
    learning techniques
  • Descriptive analytics: unsupervised
    learning techniques
  • The analytics process model: data collection and preprocessing, model construction and evaluation

2. Analytics Business Applications

  • Marketing analytics
  • Recommender systems
  • Insurance analytics
  • Text mining
  • Web mining
  • Social network analytics
  • Fraud detection
  • Decision mining
3. Big Data Technologies
  • Hadoop and its ecosystem
  • The MapReduce paradigm
  • Big data analytics using Apache Spark
    and H2O
  • NoSQL databases
  • Graph databases

4. Process Analytics

  • Process discovery
  • Event log data
  • Conformance checking
  • Predictive process mining
  • Sequence mining
  • Case studies

5. Managing Big Data & Analytics Projects and Teams

  • Model deployment
  • Valorization of analytics
  • Analytics and IT integration
  • Data quality and security
  • Data privacy and compliance
  • Ethics and privacy