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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
DTNS23
Information 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.

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