Advanced Data Analytics (3 cr)
Code: DT10040-3002
General information
- Enrollment
- 01.04.2025 - 30.04.2025
- Registration for the implementation has ended.
- Timing
- 01.09.2025 - 30.11.2025
- The implementation has not yet started.
- Number of ECTS credits allocated
- 3 cr
- Local portion
- 3 cr
- Mode of delivery
- Contact learning
- Unit
- Tradenomi / Tietojenkäsittely (DD)
- Campus
- Wärtsilä Campus Karjalankatu 3
- Teaching languages
- Finnish
- Seats
- 10 - 70
- Degree programmes
- Degree Programme in Business Information Technology
- Teachers
- Jarmo Talvivaara
- Teacher in charge
- Jarmo Talvivaara
- Groups
-
DTNS23Information Technology (BBA), Full-time Studies, Fall, 2023
- Course
- DT10040
Evaluation scale
H-5
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.