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Pervasive data science

Individual course

Max amount of FITech students: 10

Persons without a valid study right at a Finnish university or university of applied sciences have preference to this course.

The course provides an advanced understanding of Pervasive Data Science (PDS) as an emerging research field resulting from the intersection of data science and pervasive computing. The course helps the students to learn the fundamentals, architecture and deployment of pervasive data science systems and deepen their knowledge about the applications and emerging challenges and opportunities of PDS.

The course provides a context for understanding how the knowledge obtained in diverse areas relates to pervasive data science and vice-versa. The development of further competencies depends on personal focus, as such:

  • Sensing: data science, mobile sensing, machine learning, internet of things, big data management
  • Pervasive computing: cloud and edge computing, distributed systems, advanced networking courses

Course contents

Topic 1: Introduction

  • Pervasive data science: What is PDS?, applications and current direction
  • The sensing pipeline

Topic 2: Data collection and feature engineering

  • Ground truth
  • Signal processing
  • PDS measurements

Topic 3: Modelling and evaluation

  • Data modelling: constraints for modelling, clustering
  • Evaluation: model performance, model evaluation, robustness
  • Deep learning and federated learning

Topic 4: PDS programming

  • Programming considerations for PDS applications
  • Considerations to implement deep learning and federated learning applications in Python

Topic 5: Other topics

  • Privacy and security

Course material

The course does not follow any certain course book or set of papers, but each lecture is prepared individually. The teaching materials give references for further readings at the end of each lecture slide set.

The exercises are performed on Python and Jupyter notebooks.

Teaching schedule

Lectures on Tuesdays and Thursdays at 16-18.

Completion methods

The course doesn’t have an exam. The completion of the course is primarily based on a project work and the weekly tasks (around 50 %). There are no attendance requirements, but active participation during the sessions will be taken into account.

More information in the University of Helsinki study guide.

You can get a digital badge after completing this course.

IoT esineiden internet asioiden internet tekoäly koneoppiminen ML hajautettu oppiminen syväoppiminen tietojenkäsittelytiede

Responsible teacher

University of Helsinki
Petteri Nurmi, Associate Professor

Further information about the course and studying

University of Helsinki
Agustin Zuniga, Doctoral student

Contact person for applications

FITech Network University
Fanny Qvickström, Student services specialist
Start here
Start here
ICT Studies
5G technology,
AI and machine learning,
Smart systems
Course code:
Study credits:
0 €
Course level:
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Host university:
University of Helsinki
Who can apply:
Adult learner,
Degree student
Teaching method:
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General prerequisites:
Basic courses in networking, data structures, programming and mobile sensing. Previous knowledge about internet of things, machine learning, artificial intelligence or data science is beneficial but not mandatory.
Course suitable for:
Everyone who wants to learn about specific topics about the interaction between pervasive computing and data science, and their application in the real world.
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