Johdatus data-analytiikkaan (5 op)

Arviointiasteikko

H-5

Sisällön jaksotus

Topics include, but are not limited to:
- Quick Python recap
- NumPy
- pandas
- seaborn and matplotlib
- Data formats and management
- EDA - Exploratory Data Analysis
+ other relevant topics

Tavoitteet

The student understands basics of data analytics in data engineering and machine learning. The student acquires knowledge on, and experience with, selected environments and libraries for data analytics. The student is able to utilize them, in e.g., data preparation for machine learning algorithms.

Sisältö

- Data preparation, pre-processing
- Data exploration, analysis; e.g., visual, numerical
- Use of data analytics environments and libraries

Aika ja paikka

Lapland University of Applied Sciences, Rantavitikka Campus, 8.1.2024 - 15.5.2024.

Oppimateriaalit

Lecture materials and exercises will be placed in the Moodle workspace.

Opetusmenetelmät

Lectures, workshops, examples, exercises and self-supervised work.

Tenttien ajankohdat ja uusintamahdollisuudet

The course will be graded based on personal work and exercises.

Arviointikriteerit, tyydyttävä (1)

Grade 1: The student knows basics of data analytics in data engineering and machine learning. The student is able apply basic data analytics techniques in data engineering and machine learning.

Arviointikriteerit, hyvä (3)

Grade 3: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, suitably.

Arviointikriteerit, kiitettävä (5)

Grade 5: The student understands basics of data analytics in data engineering and machine learning. The student is able apply a variety of data analytics techniques in data engineering and machine learning, most suitably.