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Data science

Individual course

Max amount of FITech students: 100

Persons without a valid study right to a Finnish university have preference to this course.

The course serves as an introduction to the topic of data science and related topics such as machine learning. You will be introduced to data science methods and tools to find interesting information from data.

Specific topics on the course include

  • processing of digital signals such as speech and images
  • statistical estimation of parametric distributions
  • classification
  • prediction
  • clustering
  • pattern mining
  • network analysis for developing search engines for hypertext collections such as the web.

After the course, you can describe how natural data such as images, natural language, speech and time series measurements can be represented as data in digital form. You can apply elementary statistical and algorithmic methods to process the digital data to yield insights to the data generating phenomenon. You will understand what processes constitute the data science pipeline in the analysis, starting from natural data and ending with actionable results.

NB! The course is online except for an exam organised in Aalto University campus. The lectures are streamed online.

More information on Aalto University’s WebOodi page.

You can get a digital badge after completing this course.

Further information about the studies

Aalto University
FITech ICT contact person
fitech-sci(at)aalto.fi

Contact person for applications

FITech
Pilvi Lempiäinen , Service designer
pilvi.lempiainen(at)fitech.io
Start the application process
Start the application process
Category:
ICT Studies
Topics:
AI and machine learning,
Data analytics
Course code:
CS-C3160
Credits:
5 ECTS
Level:
Teaching period:
28.10.–16.12.2019
Application deadline:
Application period has ended
Host university:
Aalto University
Study is open for:
Adult learner,
Degree student
Teaching methods:
Blended
Language:
English
General prerequisites:
Skills needed on the course are taught on introductory courses in mathematics, statistics and programming. Specifically, matrix algebra, derivatives of functions and statistical distributions will be needed on this course.
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