Content
Big Data Analytics problems are ubiquitous in scientific research, industrial production and business services. Developing and maintaining efficient tools for processing and analysing Big Data in powerful supercomputers is necessary for discovering patterns and gaining insights for data-intensive topics including biomolecular science, global climate change, accurate weather prediction, cancer research and cybersecurity among others. Building enough man-power (human resources) to be able to utilize the increasing computational power in High Performance Computing (HPC) infrastructure to process and analyse Big Data is of great importance in advancing Big Data Analytics and Machine Learning.
This course is divided into two parts to effectively fulfil its objectives. In the second part, learners will apply skills acquired from the first part to advance their knowledge on Machine and Deep Learning applied on scientific research and related topics. The course will involve necessary theoretical lectures, and hands-on and lab sessions. The course is generally geared towards efficient use of HPC resources for Big Data Analytics. The topics covered include Supervised and Unsupervised Machine Learning, and Convolutional and Recurrent Neural Network (CNN and RNN).
Learning Goal
Providing interested learners with foundational knowledge on emerging tools for Data Analysis in HPC systems.
Requirements
Course “Using the GWDG Scientific Compute Cluster – An Introduction“ • Course “High Performance Data Analytics I" or basic skills on Parallel Data Analytics • Basic programming skills in Python
