Analytics in a Big Data World
We aim to bridge the gap between technical concepts and business applications of analytics techniques and big data. Participants will gain an insight in challenges and issues related to the storage and processing of large data sets, will be able to indicate which technologies, approaches and techniques are applicable for different types of data and objectives, will gain an insight in how advanced analytics can be used to optimize business decision in e.g. marketing, finance, logistics, HR, etc. We do so by taking a deep dive into “advanced analytics” on the one hand, i.e. advanced supervised and unsupervised modelling techniques as well as exploring "big data platforms and technologies" on the other hand, including Hadoop, Spark, stream processing engines, etc.
Praktische info:
Inschrijven?
- Voorwaarden: background knowledge of basic modeling techniques (clustering, regression, decision trees) is helpful. Some programming skills (R, Python and/or Java) are also helpful for participants willing to join in the assignments.
- Prijs: (PhD) students KU Leuven and Association KU Leuven : € 275 - Staff KU Leuven and Association KU Leuven, other (PhD) students : € 380 - Non profit/social sector : € 690 - Private sector : € 1500
georganiseerd door:
Leertraject
The following topics will be discussed during the course:
- General introduction to data science, AI, predictive modeling, and everything in between
- The data science process
- Supervised modeling: classification and regression
- Unsupervised modeling: clustering, association rules and anomaly detection
- Model evaluation
- Advanced techniques: ensemble modeling, SVMs, artificial (deep) neural networks
- The data science tools ecosystem
- Hadoop and MapReduce
- Spark and SparkSQL
- Spark streaming and other stream processing engines
- NoSQL, Neo4j and Cypher
- Applications and use cases (special topics): text mining, social network mining, recommender systems, web mining
- Assignments will be provided throughout the course which can optionally be executed by the participants
Lesgever/spreker
Seppe vanden Broucke
Seppe vanden Broucke received a PhD in Applied Economics at KU Leuven, Belgium in 2014. Currently, Seppe is working as an assistant professor at the department of Business Informatics at UGent (Belgium) and is a lecturer at KU Leuven (Belgium). Seppe’s research interests include business data mining and analytics, machine learning, process management, process mining. His work has been published in well-known international journals and presented at top conferences.
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