Advanced Programme in Business Analytics

In this course, you learn how to combine Natural Language Processing with Network Science.

Text-based unstructured data (NLP):

• Understand the importance of processing and analyzing unstructured data,
• Understand pro/cons of text mining approaches,
• Transform textual data into structured data for data science analysis,
• Analyze large scale textual documents for detecting patterns and build predictive models,
• Solve practical business questions through the analysis and modeling of textual data.

Network-based unstructured data (Network Science):

• Understand the networks mathematical framework to represent large scale unstructured data,
• Understand advantages of networks for detecting patterns in large scale data,
• Analyze large scale data through network science tools and prediction algorithms,
• Solve practical business cases through the use of network science techniques.

This course gives (future) business leaders insight into the practice of data engineering, so they can make better decisions and drive the data teams in their business to be more successful.

Data engineering is the practice of building tools, infrastructure, frameworks, and services for data teams. As such, data engineering is the foundation for their success. Proper data engineering will allow your scientists and analysts to work and deliver insights to the business.

Key topics that will be discussed:

• The data product development life cycle,
• Data infrastructure,
• The overall data technology landscape,
• Implementation of analytics at scale,
• Industrialization of analytics and machine learning use cases.

In this course, we examine how Machine Learning models & AI systems can be used (or not) in various real-world problems.

Machine learning and more generally Artificial Intelligence have recently emerged as one of the cornerstone technologies of the so-called Fourth Industrial Revolution. Their popularity is at the scale of their potential impact on our Society: they are extremely promising but very dangerous at the same time. Today, these domains of data science are not yet very well understood by most organizations and hence not very broadly and deeply applied.

The Machine Learning & AI course is intended for those who need to understand this advanced domain of data science and those who want to practice and be capable to translate a business problem into a ML/AI-driven solution.

In order to understand what this really means today and what efforts and risks it represents, the participants are guided step by step through the modern Machine Learning development lifecycle until the operationalization and monitoring of an AI system.

Key practices & learnings of this course:

• Participants "code" Machine Learning pipelines in Python or R as programming languages, and also use "best-of-breed" open source and commercial ML workbench platforms for such developments.
• Multiple aspects of Machine Learning & AI, from the theoretical perspective to the practical ones: we gradually study a set of foundational concepts, methods and best practices as well as a set of popular algorithms that is explained with examples or used properly by the participants themselves in exercises.
• Supervised learning and unsupervised learning including a good introduction to deep learning. In order to broaden our view on the type of data that Machine Learning & AI can exploit, we also introduce Natural Language Processing.
• The class meetings are a combination of lectures on the fundamental material, discussions of business applications from various industries, machine learning workshops and exercises.