Machine Learning and Data Engineering Pathway Studies, Daytime studies, Rovaniemi, Autumn 2025-Spring 2026

Education name: Machine Learning and Data Engineering Pathway Studies, Daytime studies, Rovaniemi, Autumn 2025-Spring 2026
Education type: Bachelor level
Scope of studies: 50 ECT
Implementation time: 01.08.2025 - 31.05.2026
Enrollment time: 30.07.2025 08:00 - 08.08.2025 16:00
Seats free: 5/5
Implementation type: Full time day studies
Themes: Digitalisation, Technology
Price: 300€
Teaching language: English
Teacher: Pauliina Koskela

General information

Are you interested in university studies? Are you aiming for a bachelor’s degree?
Open UAS gives you an opportunity to study towards a degree.
After completing these studies, you are eligible to apply to our Machine Learning and Data Engineering ICT degree programme and continue studies from 2nd year. The studies take place in Rovaniemi, Jokiväylä Campus and in our specialized laboratories. You will study in a multinational student group with working language of English, in an innovative learning environment that combines theory, practice and modern facilities with advanced technologies that support effective learning.

First year:
1st semester: Conceiving the basics of machine learning engineering profession.
2nd semester: Conceiving the basics of data engineering
This study path consists of first year Machine Learning and Data Engineering Studies, 60 ECTS. The studies include 10 ECTS of language studies. The final language studies are defined on the basis of student's educational background. If you have Finnish educational background, you will study Swedish and optional 3rd foreign language studies instead of Finnish. Students with foreign background education will study Finnish 1 and Finnish 2 courses. The pathway studies include 50 credits and in addition to these two 5-credit language courses, which will be determined together with the study counselor.

Implementations

Introduction to Programming
Basic information
Identifier: R504D130-3001
Learning Objectives

You can implement various small-scale programs by using a selected programming language.
You can understand programming logic, structured programming and common programming structures.
You can effectively use different features, libraries and modules available for the selected programming language.
You can apply your existing programming knowledge while studying new programming concepts.

Content

Computational thinking
Programming environments and version control tools
Basic concepts of structured programming
Programming libraries and modules
Problem-solving and programming

Probability and Statistics
Basic information
Identifier: R504D134-3001
Learning Objectives

You learn fundamental concepts of probability and statistics, which are crucial in the field of machine learning and data engineering.

You are familiar with combinatorics and basic theorems and rules of probability and you can apply them. You can handle random variables, and use discrete and continuous variables and distributions to present data and solve problems related to your professional field.

With descriptive statistics, you can characterize data in the form of diagrams and calculate statistical parameters and quantiles. With inferential statistics you come to conclusions and can make predictions based on your data.

You become familiar with applications used to handle large amounts of data.

Content

Combinatorics
Theorems and rules of probability
Discrete and continuous variables and distributions
Descriptive statistics, common diagrams and statistical parameters, quantiles
Inferential statistics and common statistical significance tests such as t-test, ANOVA and chi-square
Correlation and linear regression
Use of applications, such as Excel and Python

Algebra and geometry
Basic information
Identifier: R504D159-3001
Learning Objectives

You understand the fundamental principles and methods of algebra, trigonometry and geometry and are able to apply them to problems related to your field. You develop logical thinking and problem-solving skills and recognize the importance of mathematics as a tool and its role in the development of machine learning and data engineering. You learn to utilize calculators and computational software to solve mathematical problems.

After completing the course, you are able to handle arithmetic and algebraic expressions and solve first- and second-degree equations and inequality by hand. These skills are fundamental to problems relevant to your field. You can identify various functions as mathematical models and use them to analyze phenomena and make predictions. You master the calculation routines related to the trigonometry of right-angled and general triangles and are able to calculate areas and volumes of different shapes and objects.

Content

Set theory and number sets
Expressions, polynomials and factoring
Linear and quadratic equations, inequality, forming equations from word problems
Linear, quadratic, exponential and logarithmic functions
Domain and range of functions, extrema of quadratic functions
Triangles: Pythagorean theorem, trigonometric functions of a right triangle, the laws of sine and cosine
Plane and solid geometry

Web Programming
Basic information
Identifier: R504D135-3001
Learning Objectives

You can create simple websites that focus on sharing information and visualizations.
You know the basic commands and features of HTML and CSS
You can create common website layout structures by using HTML and CSS according to instructions
You know the basic principles how web applications work
You know basic features of JavaScript
You know how to share your results and exercises via a version control system

Content

Request-response method
Basic principles of networking, internet and servers
Basics of HTML, CSS and JavaScript
Creating basic information and visualization web applications
Introduction to API usage and development

Start Your UAS Studies
Basic information
Identifier: AMKO045-3002
Learning Objectives

Having completed this study unit
• You know Lapland University of Applied Sciences as a study environment, and you know the services and systems that support your studies.
• You develop your higher education readiness and skills as well as your information acquisition skills.
• You identify the factors related to your own study ability and wellbeing and consider how you can maintain your own study ability and how you can take care of your own wellbeing.
• You know how to use digital learning tools and make the most of them in your studies.
• You are familiar with the study guidance system, and you know the university's guidance actors.
• You get to know the curriculum of your education and create your own curriculum under guidance. You know the students and teachers in your group.
• You have the essential writing, speaking and presentation skills needed in managerial settings in business.
• You are able to communicate in a variety of business situations and identify and adjust to the ethical aspects of business communication.
• You are able to work as part of a multicultural team.
• You can use Lapland UAS templates.

Content

New degree students at Lapland University of Applied Sciences are oriented to their studies by this course. Course is divided to two parts. The first part of the course (2 ects), you will do independently in Moodle- environment, during the first month of study. In this part of course, you will get to know Lapland University of Applied Sciences as a learning environment. You will consider your learning abilities and acquire the skills and systems needed for your future studies.

The second part of the course (3 ects), will be hold on campus with your own educational fields’ teacher. In the Study Guidance and Curriculum section, you will get to know the other students in your starting group and start planning your own studies. Your own teacher will give the instructions for this part of the course.

Content:
• introduction to Lapland University of Applied Sciences
• studying at a university and developing university study skills and abilities
• studying in a multicultural group
• means of taking care of study ability and wellbeing
• developing knowledge acquisition skills
• services and systems that support learning
- roles of teachers and other guidance actors
- welfare services
- study administration services and systems
- IT, library and e-learning services
• digital tools for studying
- e.g. Moodle, Zoom, Teams, Cloud Services, Outlook , AI
- Microsoft Office tools
• studies and curriculum, planning your own studies, ISP
• writing, speaking and presentation skills
• communication skills

Computer Technology
Basic information
Identifier: R504D132-3001
Learning Objectives

You can install and use modern operating systems.
You can configure operating systems to work as required by the operating environment.
You can define the settings of network devices and build a functioning data network utilizing the basic components of data networks.
You can create scheduled scripts and maintain the operating system.
You know how computers and operating systems work

Content

Building and installing a computer, including selection of components and an operating system
Basic usage, configuration and maintenance of common operating systems
Networking and connectivity
Scripts and scheduled tasks
Essential command line usage

Software Development Tools
Basic information
Identifier: R504D161-3001
Learning Objectives

You can use a common version control system for your software development projects
You can use existing technical documentation in your software development work and create your own documentation
You know how to apply a common software development workflow within a technical project management environment
You have the common and necessary ICT skills for software development work
You can use a common version control environment to create your own technical portfolio

Content

Version control theory, usage and practices
Technical project management tools
Software development workflows in working life
Applying technical documentation and searching information for software development
Common ICT skills required in software development (command line/terminal -interface, file systems and types, computer component knowledge, networking etc.)

Data Analytics and Visualization Project
Basic information
Identifier: R504D138-3001
Learning Objectives

You will be able to conduct data analytics and visualization tasks in a real-world project, using the necessary tools and technologies effectively.
You will gain a solid understanding of project management fundamentals and the skills to create essential project documentation.
Working collaboratively in a team to achieve shared goals will come naturally, as will navigating various project-related situations with confidence.
Additionally, you will develop strong project communication skills to ensure seamless collaboration and success

Content

Basics of project management: process and documentation
Information and public relations,
Teamwork skills,
Work-related presentation and interaction situations

The project includes 1 ECTS of communication

Introduction to Data Management
Basic information
Identifier: R504D136-3001
Learning Objectives

You know the basic principles of databases, how they work and how they are used in software development.
You can search, use and organize data in common relational and NoSQL-databases.
You can use databases as a source of data for other applications.

Content

Basics of relational databases and NoSQL databases
Common database operations: CRUD – Create, Read, Update, Delete
Querying and using data
Integrating databases into other applications

Data Analytics
Basic information
Identifier: R504D137-3001
Learning Objectives

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

Additional information

Information will be updated later

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