Data Analytics (5cr)

Evaluation scale

H-5

Content scheduling

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

Objective

You understand the basics of data analytics in data engineering and machine learning.
You can use common data analytics environments and tools for machine learning purposes.
You learn to find insight in data by using explorative data analytics.
You learn methods on how to optimize dataset contents and distributions.
You know how to share your results and exercises via a version control system

Content

Data preparation and pre-processing
Exploratory Data Analysis (EDA): statistical, visual, and other common methods
Finding insight in datasets to optimize their structure
Use of data analytics environments and libraries/modules
Common data analytics tools regarding machine learning

Location and time

The course will be studied on-campus. Lapland University of Applied Sciences, Rantavitikka Campus, 12.1.2026 - 15.5.2026.

Materials

The study material of the course will be in the course's Moodle workspace. Additional material can be searched from the internet as needed. It's also recommended to independently search for more information during the course

Teaching methods

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

The Arene's AI recommendations and traffic light model for universities of applied sciences will be applied during the course. Whether AI is allowed during an exercise, will depend on the exercise itself.

Employer connections

The study unit has been connected to the semester project held during the same semester. The technologies and related examples during the course will be partially based on the needs of the involved company in the semester project.

Exam schedules

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

International connections

The study unit will be in English.

Completion alternatives

Alternative ways to study the course can be inquired from the instructor.

Student workload

The study unit is 5 ECTS, which amounts to 135 hours of work in total (approximately). ON average, your work will be distributed the following way:

- Lectures and workshops: 50 h
- Self-study and preparation: 45 h
- Graded tasks: 40h

Assessment criteria, satisfactory (1)

You know the basics of data analytics in data engineering and machine learning.
You are able to apply basic data analytics techniques in data engineering and machine learning tasks.
You can share your results and exercises via a version control system.

Assessment criteria, good (3)

You understand the basics of data analytics in data engineering and machine learning.
You are able to apply a variety of data analytics techniques in data engineering and machine learning tasks with a suitable approach, regarding the given dataset at hand.
You can share your results and exercises via a version control system.

Assessment criteria, excellent (5)

You understand the basics of data analytics in data engineering and machine learning.
You are able to apply a variety of data analytics techniques in data engineering and machine learning tasks with a suitable approach, regarding the given dataset at hand.
You are able to study and apply advanced tools and approaches regarding exploratory data analytics and dataset optimization with your data
You can share your results and exercises via a version control system.

Qualifications

Basics of Python programming, Basics of statistics