Study Guide

Objective

As a Student, you
- understand the basics, objectives, applications and effectiveness of advanced analytics
- know the importance of data-driven and machine learning in the implementation of advanced analytics (especially; predictive and prescriptive analytics)
- know and are able to apply advanced analytical design and implementation methods as well as various platforms and techniques
- are able to evaluate the implementation of advanced analytics, the suitability of models and the applicability of technologies for different applications.
- know and are able to apply principles and tools in the continuous development and lifecycle management of advanced analytics solutions.
- know the possibilities of quantum computing (espec. QML, quantum machine learning) solving the problems of advanced analytics.
- understand the importance of automation in connection with advanced analytics solutions.
- Understand the importance of data security in advanced analytics solutions.
- are able to apply security-enhancing solutions in the implementation of advanced analytics.

Content

Advanced analytics; basics of applying data and machine learning in analytics (eg predictive and guiding analytics)
Machine learning in analytics, data mining
Unsupervised learning and applications in data analysis; clustering, anomaly detection, natural language processing.
Supervised learning and applications in analysis; classification, regression, time series analyzes, association rules mining
Platforms and technologies for analytics solutions; services, templates, workflows,
Continuous development of advanced analytics (data, models, and lifecycle management.
In-depth learning and analytics; neural network.
Possibilities of quantum computing in analytics; quantum machine learning (QML), classical computing vs. quantum computing in analytics, quantum-classical hybrids, QaaS quantum computing services in analytics
Automation needs of advanced analytics.
Security in advanced analytics.

Evaluation scale

H-5