BD-2-002

• Big data analytics 2016 - Big data analytics
• Big data analytics 2017 - Big data analytics
• Big data analytics 2018 - Big data analytics
• Big data analytics 2019 - Big data analytics
• Big data analytics 2020 - Big data analytics
• Big data analytics 2021 - Big data analytics - specialised professional studies

• 3 (2022-01-01 to 2022-03-13)
• 4 (2022-03-14 to 2022-07-31)

English

Compulsory

Second

1

5 cr

The aim of the course is to provide the student with the necessary tools for handling big data sources for machine learning modeling.

At the end of the course, the student is expected to understand when it is needed to use supercomputer facilities for solving analytical problems. The student will be able to run machine learning algorithms in supercomputer facilities. Moreover, the student will be able to run machine learning models using spark and dask frameworks.

The students get an overview of machine learning to model using super computing facilities, and how to utilize big data. The areas of descriptive and predictive modeling are introduced for small data, and the students are then given an explanation for how similar models can be modified to work with big data. The students are introduced to the analytical process; data-related requirement handling, domain knowledge, modeling, and verification of results.

Basic python programming skills are required. Previous courses in Machine Learning for Predictive and Descriptive problems are recommended.

Hamstra, M., & Zaharia, M. (2013). Learning Spark: lightning-fast big data analytics. O'Reilly & Associates.

Daniel, J. (2019). Data Science with Python and Dask. Simon and Schuster.

https://docs.csc.fi/support/tutorials/ml-guide/ External link

• Lectures - 30 hours
• Small-group work - 70 hours
• Individual studies - 35 hours

• Total workload of the course: 135 hours
• Of which autonomous studies: 135 hours
• Of which scheduled studies: 0 hours

Multiform education

To pass this course, the student should present a final project in group or individually where they use big data facilities for machine learning modeling.

• Björk Kaj-Mikael
• Espinosa Leal Leonardo
• Majd Amin
• Scherbakov-Parland Andrej

Espinosa Leal Leonardo

https://arcada.itslearning.com/ External link

No limit (46 students enrolled)

12 months after course has ended

2021-12-24 to 2022-01-20