Data-Analysis and Machine Learning Basics (4 cr)

Evaluation scale

0-5

Objective

You understand the practices of data analytics and machine learning and the structure and flow of the project. You understand how a data-based project is designed, built and implemented. You will also recognize the key terminology and most common practices of data-based projects. You understand the importance of data visualization. You know the concepts of the teaching and test dataset and the most common ways of splitting them. You will get basic information about the data analytics and machine learning tools used.

EUR-ACE Competences:
Knowledge and Understanding
Engineering Practice

Content

- Structure and implementation of a data-based project
- Data analytics and machine learning practices
- The concepts of the teaching and test data set and the most common ways of splitting them
- Documentation and visualization of the data-based project
- Introduction to data analytics and machine learning's most common tools and practical skills needed

Location and time

Marraskuu 2021.

Materials

Verkkomateriaali: teksti ja videot.

Teaching methods

Opiskelija tutustuu aineistoihin. Mahdollisuus esittää kysymyksiä vastaanotolla.

Student workload

Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.

Assessment criteria, satisfactory (1)

Satisfactory 2: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project. Additionally, the student knows their implementation at a cursory level and is able to validate their conclusions.

Sufficient 1: The student knows the various phases of a data analytics and machine learning project. The student is able to design the phases of a data analytics and machine learning project at a cursory level. Additionally, the student is able to assess their implementation and conclusions.

Assessment criteria, good (3)

Very good 4: The student knows the various phases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of data analytics and machine learning project regardless of the problem to be solved. In addition, the student is able to assess their implementation and validate the conclusions.

Good 3: The student knows the variousphases of a data analytics and machine learning project and is able to proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess their implementation in a versatile manner and to validate the conclusions.

Assessment criteria, excellent (5)

Excellent 5: The student knows the various phases of a data analytics and machine learning project and is able to systematically proceed step by step. The student is able to design the phases of a data analytics and machine learning project regardless of the problem to be solved. Additionally, the student is able to assess critically their implementation and validate the conclusions.