Data-Analysis and Machine Learning Basics (4 cr)
Code: TTC8020-3006
General information
Timing
16.01.2023 - 23.02.2023
Number of ECTS credits allocated
4 op
Mode of delivery
Face-to-face
Unit
School of Technology
Campus
Lutakko Campus
Teaching languages
- Finnish
Degree programmes
- Bachelor's Degree Programme in Information and Communications Technology
Teachers
- Juha Peltomäki
Groups
-
ZJA23KTIDA1Avoin amk, Data-analytiikka 1, Verkko
Objectives
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
Time and location
Opintojakso toteutetaan kevätlukukaudella 2023.
Learning materials and recommended literature
Materiaali harjoitustehtäviä ja opiskeltavia asiasisältöjä varten jaetaan kurssin aikana.
Teaching methods
Virtuaalinen opiskelu sisältäen harjoitustehtävien tekemisen sekä niihin liittyviin luento- ja esimerkkimateriaaleihin perehtymisen.
Harjoitustehtävät tehdään pääsääntöisesti ryhmätöinä.
Practical training and working life connections
Opintojakson sisältö pyritään kytkemään työelämässä esiintyviin ongelmiin.
Exam dates and retake possibilities
Opintojakso arvioidaan palautettujen harjoitustehtävien avulla. Palautukset tulee suorittaa annettuihin aikatauluihin mennessä.
Alternative completion methods
Hyväksilukemisen menettelytavat kuvataan tutkintosäännössä ja opinto-oppaassa. Opintojakson opettaja antaa lisätietoa mahdollisista opintojakson erityiskäytänteistä.
Student workload
Yhden opintopisteen työmäärä vastaa 27 tunnin opiskelutyötä. Yhteensä opiskelutyömäärä (4 op) kurssilla on 108 tuntia.
Further information for students
Arviointimenetelmät käydään läpi opintojakson alussa.
Evaluation scale
0-5
Evaluation criteria, satisfactory (1-2)
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.
Evaluation criteria, good (3-4)
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.
Evaluation 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.