Academic year 2024 - 2025

•     Academic Writing OTH8HM117-3001 24.03.2025-16.05.2025  2 cr  (MAAIBUME, ...) +-
  Responsible person

  Lili Aunimo

  Teachers

  Anna Kimberley

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  24.03.2025 - 16.05.2025

  Enrollment

  07.01.2025 - 10.01.2025

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 2 cr

  Unit

  Master’s Degrees

•     Applied Design Research 1 RDI2HM104-3001 24.03.2025-16.05.2025  3 cr  (MAAIBUME, ...) +-
  Learning objectives

  The course is the first part in a series of three consecutive courses on applied design research. The overall aim of the courses is to provide the students with the methodological skills and knowledge to plan and carry out their Master’s thesis. Special focus is on applied research.
  
  Upon the successful completion of the course, the student:
  
  1. understands the main differences between different research methods
  2. is able to evaluate and select research method(s) for given case projects
  3. understands the main principles of qualitative and quantitative methods, as well as essential applied sciences approaches, such as design science and design thinking.
  4. knows general aspects of research ethics.
  5. is able to utilize his/her research method knowledge in interpreting scientific literature in his/her field.

  Contents

  1. An introduction to applied research. Typical research questions and methods in applied research and design science research.
  2. Overview of different research methods
  3. Data collection and analysis strategies
  4. Research ethics
  5. Example cases of applied research in the field of AI for Business Transformation.

  Assessment criteria

  Assessment criteria - grade 1

  The student demonstrates some understanding of research methods, but is not able to systematically compare them and to apply the knowledge in selecting a method for a case project. The student has some, but limited, understanding of research ethics.

  Assessment criteria - grade 3

  In addition to the skills and competences mentioned for Grade 1, the student
  
  demonstrates an understanding of research methods, and is able to systematically compare them and to apply the knowedge in selecting a method for a case project. The student has an understanding of research ethics.
  
  The student understands the main principles of qualitative and quantitative methods, as well as essential applied sciences approaches, such as design science and design thinking. The student is able to utilize his/her research method knowledge in interpreting scientific literature in his/her field.

  Assessment criteria - grade 5

  n addition to the skills and competences mentioned for Grades 1 and 3, the student thoroughly understands the differences between various research methods, and is able to systematically compare them and to apply the knowedge in selecting a method for a case project. The student has a good understanding of research ethics.
  
  The student understands well the principles of qualitative and quantitative research methods, as well as essential applied sciences approaches, such as design science and design thinking. The student is proficient in applying his/her research method knowledge in interpreting scientific literature in his/her field.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Responsible person

  Lili Aunimo

  Teachers

  Lili Aunimo

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  24.03.2025 - 16.05.2025

  Enrollment

  07.01.2025 - 21.03.2025

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 3 cr

  Unit

  Master’s Degrees

•     Artificial Intelligence in Modern Business DIG4HM106-3021 13.01.2025-14.03.2025  5 cr  (MAAIBUME, ...) +-
  Learning objectives

  The overall learning objective of the course is to give the students insight into what artificial intelligence is and the impact it will have on the economy and business processes, as well as a hands-on knowledge and examples of how to apply artificial intelligence in different business cases.
  Upon completion of the course, the student is able to
  • Understand and describe the role of artificial intelligence in today’s business environment
  • Gain in-depth knowledge of why artificial intelligence is vital for different business processes
  • Apply artificial intelligence strategy framework
  • Understand how companies can use and leverage chatbots, cloud-based AI services and robotic process automation
  • Understand what kind of opportunities and challenges AI will create today and in the future
  • Understand and describe what skills and competences are needed in the future
  • Create a simple artificial intelligence strategy plan for any business

  Starting level and linkage with other courses

  The student will focus on the business aspects of artificial intelligence. The course does not require programming skills and the main focus is how artificial intelligence could be applied in business and how AI will change business processes. Skills and interest in a logical and systematic way of working are required.

  Contents

  - Practical examples of what artificial intelligence is and the impact it will have on the economy.
  - Benefits and challenges that AI presents to companies.
  - How artificial intelligence will transform different industries and business processes.
  - How to use the AI Strategy Framework, which will guide you on how to incorporate AI pilot programs into your overall strategy plan.
  - Examples of the five elements of the AI Strategy Framework and how to apply them.
  - Practical example of how to use the AI Strategy Framework for creating a chatbot.
  - Obtain knowledge about chatbot communication, particularly how text-only and voice-enabled chatbots work and how businesses can use them.
  - Learn how cloud-based AI services work and how companies can use them.
  - Gain a basic understanding of the business purposes for which robotic process automation can be used.
  - See examples of how AI will transform the way we work, and the skills needed for the future.
  - Develop a practical understanding of how to hire freelance AI talent.
  - Obtain knowledge of technical and non-technical AI-based jobs that will be in high demand in the future.

  Assessment criteria

  Assessment criteria - grade 1

  The student understands the basic concepts of artificial intelligence. S/he knows how AI can be used to create value for business. S/he can name different ways companies can use AI in their business processes

  Assessment criteria - grade 3

  The student has a good understanding of artificial intelligence and its application for creating value for business. S/he can create a simple artificial intelligence strategy plan

  Assessment criteria - grade 5

  The student has an excellent understanding of artificial intelligence and its application in creating value for business. S/he can create excellent artificial intelligence strategy plan and knows how to successfully apply AI

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Responsible person

  Kari Hiekkanen

  Learning material and recommended literature

  Artificial Intelligence: 101 Things You Must Know Today About Our Future. Lasse Rouhiainen. Amazon, 2018.
  
  The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. Thomas H. Davenport. 2018
  
  Online videos, workbook and AI related business articles provided in the course.

  Teachers

  Lasse Rouhiainen

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  13.01.2025 - 14.03.2025

  Enrollment

  07.01.2025 - 10.01.2025

  Groups

  • MAAIBUME
  • CONTACT
  • BLENDED

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  4 cr

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Data Analytics RDI2HM102-3029 20.01.2025-16.05.2025  5 cr  (MAAIBUME, ...) +-
  Learning objectives

  Upon completion of the course, the student will:
  • Identify data sources and assess their suitability for business needs.
  • Understand the stages of data preparation, modeling, and forecasting.
  • Understand the fundamental concepts of machine learning and artificial intelligence.
  • Master methods of descriptive and explanatory analytics.
  • Be able to utilize various visualization and reporting techniques.
  • Understand the concept of information design.

  Starting level and linkage with other courses

  No prerequisites.

  Contents

  Content
  • Data analytics based on process thinking (CRISP-DM).
  • Methods for descriptive and explanatory analytics.
  • Analysis and forecasting of time series data.
  • Models for predictive analytics and machine learning.
  • Applied examples using Python.
  • Tools for visualization and reporting.

  Assessment criteria

  Assessment criteria - grade 1

  The student understands the data analytics process and can apply it, instructed by the teacher, to a business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions, guided by the teacher.
  
  The student is able to derive and visualize dashboards, scorecards and publish those using related digital tools. The students is able to apply one or some forecasting algorithms to a business problem, instructed by the teacher. The students is able to assess the reliability and relevance of business reports.

  Assessment criteria - grade 3

  The student understands the data analytics process and can apply it independently to a simple business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions independently.
  
  The students is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently one or some forecasting algorithms to a business problem. The students is able to assess the reliability and relevance of business reports.

  Assessment criteria - grade 5

  The student understands the data analytics process and can apply it to a slightly complicated business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve demanding business questions.
  
  The student is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently several forecasting algorithms to different business problems. The student is able to assess the reliability and relevance of business reports.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teaching methods and instruction

  The course has lessons every week. The lessons cover the basics of data analytics applications using Python coding.
  
  The course implementation welcomes independent study of topics with the help of ready-made examples and videos, within the guidelines of the Haaga-Helia University of Applied Sciences.
  
  No previous experience in coding is required.

  Responsible person

  Juha Nurmonen

  Learning material and recommended literature

  The learning material is mainly distributed through the Moodle learning environment. The course brings together the main techniques needed for the fundamentals of data analytics. It also provides tools for applied analytics research or thesis work. Such issues are constantly changing and the course will mainly make use of teachers’ and otherwise up-to-date material.

  Teachers

  Juha Nurmonen

  Working life connections

  The course implementations are designed for students who are about to enter work life or are already working there. The course content takes into account the content used in the field.

  Campus

  Pasila Campus

  Exam dates and re-exam possibilities

  The organisations of exam and retake will be agreed together with the course participants.
  
  The exam will be held at the end of the course on a date to be agreed with the participants in the implementation. The date of the re-examination will also be mutually agreed with the participants of the implementation.
  
  The exam and retakes are organised in Pasila and participants are expected to be present; particularly the identity of the examiner will be verified.
  
  In the tests, the author has extensive access to materials and to the Internet. It is therefore not possible to conduct the test as an Exam.

  Teaching language

  English

  Internationality

  Data analytics skills are international skills. Methodological expertise is international.

  Timing

  20.01.2025 - 16.05.2025

  Learning assignments

  The topics presented in the lectures are learned by doing related exercises. Assignments will be returned regularly during the course, every one or two weeks. Assignments will be given and returned via Moodle.
  
  Assignments will cover the following topics: descriptive analytics, explanatory analytics, time series and time series forecasting, predictive analytics and basics of machine learning models.

  Enrollment

  07.01.2025 - 17.01.2025

  Content scheduling

  The course has weekly lessons according to the timetable. A detailed weekly timetable will be made available on the Moodle learning environment if the Director of Studies decides to start this implementation. The timetable for the returnable learning assignments will follow the lecture timetable.

  Groups

  • MAAIBUME
  • CONTACT

  Alternative learning methods

  There are no shortcuts to learning, and doing the work is essential. Other ways of obtaining a grade in the Haaga-Helia University of Applied Sciences credit register should be requested from the teacher responsible for the course.

  Seats

  31 - 50

  Further information

  The determination of the implementation grade will be announced at the beginning of the implementation.

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Digital Ethics DIG4HM108-3003 13.01.2025-14.03.2025  5 cr  (MAAIBUME, ...) +-
  Learning objectives

  Upon successful completion of the course, the student is able to
  - understand and analyze various ethical issues related to digitalisation and data
  - assume a critical attitude towards promises of moral improvement through digitalisation
  - see digitalisation and related technologies in their wider social, environmental and political contexts.

  Starting level and linkage with other courses

  None

  Contents

  The course will pursue a cross-disciplinary investigation of the socio-ethical implications of digital technologies. We will combine theoretical viewpoints with concrete issues facing people who are implementing and using digital systems. We will cover a variety of themes, including data ownership and control, digital economics and surveillance capitalism, power and governance of algorithmic decision making, humanization of digital technology, ethics of robotics and autonomous systems and sustainablity of digital development in general.
  
  We will take up issues that have recently come into focus and are being approached in different fields, such as health, media, games, retail, manufacturing.

  Assessment criteria

  Assessment criteria - grade 1

  The student has a basic understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve.

  Assessment criteria - grade 3

  The student has a good understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve. They are able to analyze and predict the ethical problems of different solutions in different industries

  Assessment criteria - grade 5

  The student has an in-depth understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve. They will be able to analyse and anticipate the ethical problems of different solutions in different industries and apply different types of ethical frameworks and critical thinking widely.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teaching methods and instruction

  The course consists of four lectures / workshops (one online, three onsite), and a virtual part where students will work independently according to set schedule.

  Learning material and recommended literature

  The course material is combination of recent academic articles, online webinars, white papers and other relevant and timely sources.

  Teachers

  Kari Hiekkanen

  Campus

  Pasila Campus

  Exam dates and re-exam possibilities

  No exam

  Teaching language

  English

  Timing

  13.01.2025 - 14.03.2025

  Learning assignments

  Four short essays (peer reviewed); group discussions.

  Enrollment

  02.01.2025 - 10.01.2025

  Content scheduling

  Wed 22.1.2025 @ 14.00 - 17.00: Introduction & Practicalities (online, Zoom)
  Wed 5.2.2025 @ 14.00 - 17.00; Workshop (onsite)
  Wed 26.2.2025 @ 14.00 - 17.00; Workshop (onsite)
  Wed 12.3.2025 @ 14.00 - 17.00; Workshop (onsite)

  Groups

  • MAAIBUME
  • CONTACT
  • BLENDED

  Seats

  15 - 15

  Further information

  The grading will be based on four peer-reviewed short reflection essays on selected themes.

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  3 cr

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Digital Ethics DIG4HM108-3002 24.03.2025-16.05.2025  5 cr  (EVENING, ...) +-
  Learning objectives

  Upon successful completion of the course, the student is able to
  - understand and analyze various ethical issues related to digitalisation and data
  - assume a critical attitude towards promises of moral improvement through digitalisation
  - see digitalisation and related technologies in their wider social, environmental and political contexts.

  Starting level and linkage with other courses

  None

  Contents

  The course will pursue a cross-disciplinary investigation of the socio-ethical implications of digital technologies. We will combine theoretical viewpoints with concrete issues facing people who are implementing and using digital systems. We will cover a variety of themes, including data ownership and control, digital economics and surveillance capitalism, power and governance of algorithmic decision making, humanization of digital technology, ethics of robotics and autonomous systems and sustainablity of digital development in general.
  
  We will take up issues that have recently come into focus and are being approached in different fields, such as health, media, games, retail, manufacturing.

  Assessment criteria

  Assessment criteria - grade 1

  The student has a basic understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve.

  Assessment criteria - grade 3

  The student has a good understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve. They are able to analyze and predict the ethical problems of different solutions in different industries

  Assessment criteria - grade 5

  The student has an in-depth understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve. They will be able to analyse and anticipate the ethical problems of different solutions in different industries and apply different types of ethical frameworks and critical thinking widely.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teaching methods and instruction

  The course consists of online lectures and independent work according to set schedule.

  Responsible person

  Kari Hiekkanen

  Learning material and recommended literature

  The course material is combination of recent academic articles, online webinars, white papers and other relevant and timely sources.

  Teachers

  Kari Hiekkanen

  Campus

  Pasila Campus

  Exam dates and re-exam possibilities

  No exam

  Teaching language

  English

  Timing

  24.03.2025 - 16.05.2025

  Learning assignments

  Four short essays (peer reviewed)

  Enrollment

  02.01.2025 - 21.03.2025

  Content scheduling

  First lecture: Mar 27th, 2025 @ 17.40 (online, Zoom)

  Groups

  • EVENING
  • BLENDED
  • MADIGF
  • MADIGE
  • ONLINE
  • EXCH
  • MAGGU

  Seats

  31 - 60

  Further information

  The grading will be based on four peer-reviewed short reflection essays on selected themes.

  Degree Programme

  LUJOM Degree Programme in Business Development and Leadership, BUTEM Degree Programme in Business Technologies, LITEM Degree Programme in Business Technologies, PAKEM Degree Programme in Service Business Leadership and Development, AIBUM Degree Programme in AI for Business Transformation, LEBUM Degree Programme in Leading Business Transformation, HOSBUM Degree Programme in Tourism and Hospitality Business

  Virtual proportion

  2 cr

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Management of Digital Service Development ICT4HM104-3006 13.01.2025-16.05.2025  5 cr  (MAICTE, ...) +-
  Learning objectives

  The course is an introduction to the development of digital services. The course is positioned between IT and business perspectives. Upon the successful completion of the course, the student:
  - understands the development models and principles of digital service development
  - understands technology diffusion and digital transformation (incl. AI transformation)
  - can identify new business roles and market changes in digital transformation
  - can identify potential value drivers, risks and measures in managing development
  - understands the principles of data governance as a part of data & AI project management
  - can reflect on future development opportunities and IT technology trends

  Starting level and linkage with other courses

  Master’s level studies. All material and communication is in English, but you may choose between English and Finnish for your report language.

  Contents

  - An introduction to digital business and digital services
  - The challenges of managing IT-based development projects
  - The development models, stakeholders, processes and measurement of digital service development
  - Digital transformation and technology diffusion models
  - New digital business models, roles and emerging technologies
  - Data governance and AI project management
  - Reflection of global IT trends and the digital future

  Assessment criteria

  Assessment criteria - grade 1

  The student partially understands development methods and principles but does not understand the role of IT in business development. Project work is limited.

  Assessment criteria - grade 3

  In addition to the skills and competences mentioned for Grade 1, the student has basic competence in planning and managing digital service development projects in a particular area. In addition, the student understands business and customer value in developing digital services. The project work demonstrates these skills and competences.

  Assessment criteria - grade 5

  In addition to the skills and competences mentioned for Grades 1 and 3, the student thoroughly understands the principles and success factors of digital service development and is able to adopt them in practice. The student can demonstrate these competences through professionally produced and completed project work. The student is able to reflect the development challenges and opportunities of digital service development in practice.

  Further information

  When the implementation type of the course is contact, online or blended, it is required that the student is present during those teaching hours/learning tasks that are marked in the study schedule/course content. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teaching methods and instruction

  The course is a blended learning course. The main task of the course focuses on project work (individual or group) related to the course topic, where students can select user, business, management or technological perspectives on the course topic.
  
  Depending on the implementation, the contact lessons are 1) kick-off for the course, 2) project work facilitation in the middle, and 3) the final presentation of the project work. Online tasks include orientation (e.g. video lecturers) to the given course content, learning material and online discussion tasks. There are no exams. The assessment of one’s own learning can be used.

  Responsible person

  Kari Hiekkanen

  Learning material and recommended literature

  Depending on the implementation, video presentations, articles, white papers, research papers, books, lectures, sharing one's won experiences, etc.

  Teachers

  Ari Alamäki

  Working life connections

  Connection to the workplace context and business life through project work and online group discussions

  Campus

  Pasila Campus

  Exam dates and re-exam possibilities

  No exams

  Teaching language

  English

  Internationality

  International material, examples, etc. Sharing experiences from international business context

  Timing

  13.01.2025 - 16.05.2025

  Learning assignments

  The course is a blended course, and its largest part is project work & report (75%). The course consists of project work (report) and five “online learning tasks”
  
  Project report. The goal is to create ”consultative report” related to the topic of digital service development. The aim is that you write a report from the perspective of ”external IT-business consultant” from the development method, development project, case, design, planning, requirement spec, UX/CX, some technology, etc. in the field of digital service development in your organization or some other context. It is not shared to others.
  
  Summary presentation. Create also 3-5 pages PPT-presentation that is a kind of summary from your project work (shared to others). If your content is confidential, describe theoretical framework or the development challenge in the general level.
  
  The five “monthly online learning modules” consists of video lecturing, reading material and collaboration task. The idea of collaboration task is to share your experiences or opinions related to the given topic (everybody needs to write a comment).

  Enrollment

  02.01.2025 - 10.01.2025

  Content scheduling

  January - May or August - December

  Groups

  • MAICTE
  • MAAIBUME
  • EVENING
  • BLENDED
  • ONLINE

  Alternative learning methods

  The recognition of prior learning (report)

  Seats

  31 - 60

  Further information

  Project report (75%) and online learning tasks (25%). Grade 1-5
  
  1 = The student partially understands development methods and principles but does not understand the role of IT in business development. Project work is limited.
  
  3 = In addition to the skills and competences mentioned for Grade 1, the student has basic competence in planning and managing digital service development projects in a particular area. In addition, the student understands business and customer value in developing digital services. The project work demonstrates these skills and competences.
  
  5 = In addition to the skills and competences mentioned for Grades 1 and 3, the student thoroughly understands the principles and success factors of digital service development and is able to adopt them in practice. The student can demonstrate these competences through professionally produced and completed project work. The student is able to reflect the development challenges and opportunities of digital service development in practice.

  Degree Programme

  BUTEM Degree Programme in Business Technologies, AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  3 cr

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Mathematical Principles of AI DIG4HM111-3001 13.01.2025-16.05.2025  5 cr  (MAAIBUME, ...) +-
  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teaching methods and instruction

  This course implementation has lessons every week. The lessons include relevant mathematical principles and the use of Python in calculations. Some of the lessons are intended to the guidance of course assignments. In particular, for each course assignment before the submission date there is an associated lecture.
  
  The course implementation welcomes independent study of topics, e.g. by means of examples in the course material, within the framework of the Haaga-Helia University of Applied Sciences' guidelines.
  
  No previous experience in coding is required.

  Responsible person

  Lili Aunimo

  Learning material and recommended literature

  The learning material is mainly distributed through the Moodle learning environment. The course brings together key mathematical concepts related to the applications of AI. It also provides tools for applied analytics research and thesis work. Such issues are constantly evolving and the course will make use of material produced by the course teachers and otherwise up-to-date material.

  Teachers

  Toni Ernvall, Juha Nurmonen

  Working life connections

  The course implementations are designed for students who are about to enter work life or are already working there. The course content takes into account the content used in the field.

  Campus

  Pasila Campus

  Exam dates and re-exam possibilities

  The exam will be held at the end of the course on a date to be mutually agreed upon participants during the course. The date of the re-examination will also be agreed together during the implementation.
  
  In all exams and retakes participant must be present in Pasila, where the identity of the examiner will be verified.
  
  In the case of examinations, the participant has restricted access to some materials and coding environment. It is therefore not possible to conduct the test as in electron exam facilities (Exam).

  Teaching language

  English

  Internationality

  Mathematical and methodological skills are international skills.

  Timing

  13.01.2025 - 16.05.2025

  Learning assignments

  The topics presented in the course lectures are learned by doing related exercises. Assignments will be returned regularly throughout the course.
  
  The learning tasks will practice the use of applications of mathematical concepts and their calculation using the Python programming language.

  Enrollment

  07.01.2025 - 10.01.2025

  Content scheduling

  The course has weekly lessons according to the timetable. A detailed weekly timetable will be made available on the Moodle learning environment if the Director of Studies decides to starts this implementation. The timetable for the returnable learning assignments will follow the lecture timetable.

  Groups

  • MAAIBUME
  • CONTACT

  Alternative learning methods

  There are no shortcuts to learning, and doing the work is essential. Other ways to get your grade in the Haaga-Helia University of Applied Sciences' register of credits should be inquired from the teacher responsible for the course.

  Seats

  15 - 30

  Further information

  The learning assignments account for 40% of the final grade and the exam for 60%.

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Orientation to Ulysseus AI for Business Transformation OTH8HM116-3001 07.01.2025-16.05.2025  (MAAIBUME, ...) +-
  Responsible person

  Lili Aunimo

  Teachers

  Lili Aunimo, Mari Austin

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  07.01.2025 - 16.05.2025

  Enrollment

  07.01.2025 - 10.01.2025

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Unit

  Master’s Degrees

Academic year 2025 - 2026

•     AI in Modern Business DIG705AS3YE-3002 12.01.2026-13.03.2026  5 cr  (CONTACT, ...) +-
  Learning objectives

  The overall learning objective of the course is to give the students insight into what artificial intelligence is and the impact it will have on the economy and business processes, as well as a hands-on knowledge and examples of how to apply artificial intelligence in different business cases.
  Upon completion of the course, the student is able to
  • Understand and describe the role of artificial intelligence in today’s business environment
  • Gain in-depth knowledge of why artificial intelligence is vital for different business processes
  • Apply artificial intelligence strategy framework
  • Understand how companies can use Ai tools like ChatGPT, Claude AI and Perplexity AI
  • Understand what kind of opportunities and challenges AI will create today and in the future
  • Understand and describe what skills and competences are needed in the future
  • Create a simple artificial intelligence strategy plan for any business

  Starting level and linkage with other courses

  The student will focus on the business aspects of artificial intelligence. The course does not require programming skills and the main focus is how artificial intelligence could be applied in business and how AI will change business processes. Skills and interest in a logical and systematic way of working are required.

  Contents

  - Practical examples of what artificial intelligence is and the impact it will have on the economy.
  - Benefits and challenges that AI presents to companies.
  - How artificial intelligence will transform different industries and business processes.
  - How to use the AI Strategy Framework, which will guide you on how to incorporate AI pilot programs into your overall strategy plan.
  - Examples of the five elements of the AI Strategy Framework and how to apply them.
  - Practical example of how to use the AI Strategy Framework for creating a chatbot.
  - Obtain knowledge about chatbot communication, particularly how text-only and voice-enabled chatbots work and how businesses can use them.
  - Learn how to use ChatGPT and other AI tools for business.
  - See examples of how AI will transform the way we work, and the skills needed for the future.
  - Obtain knowledge of technical and non-technical AI-based jobs that will be in high demand in the future.

  Assessment criteria

  Assessment criteria - grade 1

  The student understands the basic concepts of artificial intelligence. S/he knows how AI can be used to create value for business. S/he can name different ways companies can use AI in their business processes

  Assessment criteria - grade 3

  The student has a good understanding of artificial intelligence and its application for creating value for business. S/he can create a simple artificial intelligence strategy plan

  Assessment criteria - grade 5

  The student has an excellent understanding of artificial intelligence and its application in creating value for business. S/he can create excellent artificial intelligence strategy plan and knows how to successfully apply AI

  ---

  Teachers

  Lili Aunimo

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  12.01.2026 - 13.03.2026

  Groups

  • CONTACT
  • YAMK1AIBE
  • BLENDED

  Seats

  31 - 60

  Degree Programme

  LITEM Degree Programme in Business Technologies, AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  2 cr

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Academic Writing OTH8HM117-3002 12.01.2026-13.03.2026  2 cr  (MAAIBUME, ...) +-
  Responsible person

  Lili Aunimo

  Teachers

  Anna Kimberley

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  12.01.2026 - 13.03.2026

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 2 cr

  Unit

  Master’s Degrees

•     Applied Design Research 1 RDI2HM104-3002 23.03.2026-15.05.2026  3 cr  (MAAIBUME, ...) +-
  Learning objectives

  The course is the first part in a series of three consecutive courses on applied design research. The overall aim of the courses is to provide the students with the methodological skills and knowledge to plan and carry out their Master’s thesis. Special focus is on applied research.
  
  Upon the successful completion of the course, the student:
  
  1. understands the main differences between different research methods
  2. is able to evaluate and select research method(s) for given case projects
  3. understands the main principles of qualitative and quantitative methods, as well as essential applied sciences approaches, such as design science and design thinking.
  4. knows general aspects of research ethics.
  5. is able to utilize his/her research method knowledge in interpreting scientific literature in his/her field.

  Contents

  1. An introduction to applied research. Typical research questions and methods in applied research and design science research.
  2. Overview of different research methods
  3. Data collection and analysis strategies
  4. Research ethics
  5. Example cases of applied research in the field of AI for Business Transformation.

  Assessment criteria

  Assessment criteria - grade 1

  The student demonstrates some understanding of research methods, but is not able to systematically compare them and to apply the knowledge in selecting a method for a case project. The student has some, but limited, understanding of research ethics.

  Assessment criteria - grade 3

  In addition to the skills and competences mentioned for Grade 1, the student
  
  demonstrates an understanding of research methods, and is able to systematically compare them and to apply the knowedge in selecting a method for a case project. The student has an understanding of research ethics.
  
  The student understands the main principles of qualitative and quantitative methods, as well as essential applied sciences approaches, such as design science and design thinking. The student is able to utilize his/her research method knowledge in interpreting scientific literature in his/her field.

  Assessment criteria - grade 5

  n addition to the skills and competences mentioned for Grades 1 and 3, the student thoroughly understands the differences between various research methods, and is able to systematically compare them and to apply the knowedge in selecting a method for a case project. The student has a good understanding of research ethics.
  
  The student understands well the principles of qualitative and quantitative research methods, as well as essential applied sciences approaches, such as design science and design thinking. The student is proficient in applying his/her research method knowledge in interpreting scientific literature in his/her field.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Responsible person

  Lili Aunimo

  Teachers

  Lili Aunimo

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  23.03.2026 - 15.05.2026

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 3 cr

  Unit

  Master’s Degrees

•     Applied Design Research 2 RDI2HM105-3001 29.09.2025-12.12.2025  1 cr  (MAAIBUME, ...) +-
  Learning objectives

  The course is the second part in a series of three consecutive courses on applied design research. The overall aim of the courses is to provide the students with the methodological skills and knowledge to plan and carry out their Master’s thesis. Special focus is on applied research, design thinking and design science.
  
  Upon the successful completion of the course, the student:
  
  1) understands the main principles of essential applied sciences approaches, such as design science and design thinking.
  2) is able to utilize his/her knowledge on design science and design thinking in interpreting scientific literature in his/her field.
  3) is able to apply design science methods in the creation of a research plan

  Starting level and linkage with other courses

  Master’s level studies. All material and communication is in English. The course Applied Design Research 1 must be completed before this course.

  Contents

  1) An introduction to applied research. Typical research questions and methods in applied research and design science research.
  2) Key concepts and methods related to design science, design thinking and applied design research.
  3) Example cases of applied research using design science methodology.
  Research plan using the design science research approach.

  Assessment criteria

  Assessment criteria - grade 1

  The student demonstrates some understanding of research methods in applied sciences but is not able to systematically compare them and to apply them in a case project. The student has some, but limited, understanding of the core concepts of design science.

  Assessment criteria - grade 3

  In addition to the skills and competences mentioned for Grade 1, the student demonstrates an understanding of research methods used in applied sciences and is able to systematically compare them and to apply them in a case project. The student knows the core concepts of design science and design thinking and knows how to apply them in creating his/her own research plan.

  Assessment criteria - grade 5

  In addition to the skills and competences mentioned for Grades 1 and 3, the student thoroughly understands the differences between various research methods used in applied research. S/he, and is able to systematically compare them and to apply them in a case project.
  
  The student understands well the concepts and methods of design science and design thinking. The student is proficient in applying them when creating his/her own research plan.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teachers

  Lili Aunimo

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  29.09.2025 - 12.12.2025

  Groups

  • MAAIBUME
  • ONLINE

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  1 cr

  Evaluation scale

  H-5

  Credits

  • 1 cr

  Unit

  Master’s Degrees

•     Applied Design Research 3 RDI2HM106-3001 19.01.2026-15.05.2026  1 cr  (MAAIBUME, ...) +-
  Learning objectives

  The course is the third part in a series of three consecutive courses on applied design research. The overall aim of the courses is to provide the students with the methodological skills and knowledge to plan and carry out their Master’s thesis. Special focus is on applied research, design thinking and design science.
  
  Upon the successful completion of the course, the student:
  
  1) is able to formulate a thesis topic description based on practical company and/or societal challenge
  2) is able to apply his/her knowledge on research methods, data collection and analysis as well as research ethics in the creation of a thesis topic proposal and the research design of his/her thesis
  3) understands the thesis process and is able to follow it
  4) is ready to start working on his/her thesis together with the thesis commissioner and thesis supervisors

  Starting level and linkage with other courses

  Master’s level studies. All material and communication is in English. The course Applied Design Research 2 must be completed before this course. At the end of this course, the student must have a thesis topic approved by the academic head of the program.

  Contents

  1) presentation of possible thesis topics by research groups, companies and other organizations
  2) writing a thesis topic description based on a challenge presented by a possible thesis commissioner
  3) planning a possible research design for a master’s thesis
  4) the thesis process

  Assessment criteria

  Assessment criteria - grade 1

  The student demonstrates some understanding of how to formulate a thesis topic description based on practical company and/or societal challenges. The student has some, but limited, understanding of the thesis process.

  Assessment criteria - grade 3

  In addition to the skills and competences mentioned for Grade 1, the student
  
  demonstrates an understanding of how to formulate a thesis topic description based on practical company and/or societal challenges. The student is able to apply his/her knowledge on research methods, data collection and analysis as well as research ethics in the creation of a thesis topic proposal and in the research design of his/her master’s thesis. The student knows the thesis process and can apply it in his/her own master’s thesis project.

  Assessment criteria - grade 5

  In addition to the skills and competences mentioned for Grades 1 and 3, the student is proficient in applying his/her knowledge on research methods, data collection and analysis as well as research ethics in the creation of a thesis topic proposal and in the research design of his/her master’s thesis. The student knows thoroughly the thesis process and can apply it well in his/her own master’s thesis project.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teachers

  Lili Aunimo

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  19.01.2026 - 15.05.2026

  Groups

  • MAAIBUME
  • ONLINE

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  1 cr

  Evaluation scale

  H-5

  Credits

  • 1 cr

  Unit

  Master’s Degrees

•     Data Analytics RDI2HM102-3034 19.01.2026-15.05.2026  5 cr  (MAAIBUME, ...) +-
  Learning objectives

  Upon completion of the course, the student will:
  • Identify data sources and assess their suitability for business needs.
  • Understand the stages of data preparation, modeling, and forecasting.
  • Understand the fundamental concepts of machine learning and artificial intelligence.
  • Master methods of descriptive and explanatory analytics.
  • Be able to utilize various visualization and reporting techniques.
  • Understand the concept of information design.

  Starting level and linkage with other courses

  No prerequisites.

  Contents

  Content
  • Data analytics based on process thinking (CRISP-DM).
  • Methods for descriptive and explanatory analytics.
  • Analysis and forecasting of time series data.
  • Models for predictive analytics and machine learning.
  • Applied examples using Python.
  • Tools for visualization and reporting.

  Assessment criteria

  Assessment criteria - grade 1

  The student understands the data analytics process and can apply it, instructed by the teacher, to a business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions, guided by the teacher.
  
  The student is able to derive and visualize dashboards, scorecards and publish those using related digital tools. The students is able to apply one or some forecasting algorithms to a business problem, instructed by the teacher. The students is able to assess the reliability and relevance of business reports.

  Assessment criteria - grade 3

  The student understands the data analytics process and can apply it independently to a simple business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve simple business questions independently.
  
  The students is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently one or some forecasting algorithms to a business problem. The students is able to assess the reliability and relevance of business reports.

  Assessment criteria - grade 5

  The student understands the data analytics process and can apply it to a slightly complicated business problem. The student understands the following concepts: descriptive, predictive and prescriptive analytics as well as the characteristics of advanced data and is able to solve demanding business questions.
  
  The student is able to derive and visualize dashboards, scorecards and publish them using related digital tools. The student is able to apply independently several forecasting algorithms to different business problems. The student is able to assess the reliability and relevance of business reports.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Responsible person

  Juha Nurmonen

  Teachers

  Toni Ernvall, Juha Nurmonen

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  19.01.2026 - 15.05.2026

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  31 - 50

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Digital Ethics DIG706AS3YE-3001 12.01.2026-13.03.2026  5 cr  (CONTACT, ...) +-
  Learning objectives

  Upon successful completion of the course, the student is able to
  - understand and analyze various ethical issues related to digitalisation and data
  - assume a critical attitude towards promises of moral improvement through digitalisation
  - see digitalisation and related technologies in their wider social, environmental and political contexts.

  Contents

  The course will pursue a cross-disciplinary investigation of the socio-ethical implications of digital technologies. We will combine theoretical viewpoints with concrete issues facing people who are implementing and using digital systems. We will cover a variety of themes, including data ownership and control, digital economics and surveillance capitalism, power and governance of algorithmic decision making, humanization of digital technology, ethics of robotics and autonomous systems and sustainablity of digital development in general.
  
  We will take up issues that have recently come into focus and are being approached in different fields, such as health, media, games, retail, manufacturing.

  Assessment criteria

  Assessment criteria - grade 1

  The student has a basic understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve.

  Assessment criteria - grade 3

  The student has a good understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve. They are able to analyze and predict the ethical problems of different solutions in different industries

  Assessment criteria - grade 5

  The student has an in-depth understanding of the relationship between society and technology and what ethical risks new technologies and applications may involve. They will be able to analyse and anticipate the ethical problems of different solutions in different industries and apply different types of ethical frameworks and critical thinking widely.

  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Teachers

  Kari Hiekkanen

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  12.01.2026 - 13.03.2026

  Groups

  • CONTACT
  • YAMK1AIBE
  • BLENDED

  Seats

  31 - 60

  Degree Programme

  LITEM Degree Programme in Business Technologies, AIBUM Degree Programme in AI for Business Transformation

  Virtual proportion

  3 cr

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees

•     Mathematical Principles of AI DIG4HM111-3002 19.01.2026-15.05.2026  5 cr  (MAAIBUME, ...) +-
  Further information

  When the implementation type of the course is contact, online or blended it is required that the student is present during those teaching hours that are marked in the study schedule. If you are absent more than 25%, your grade will be lowered by one. If you are absent more than 50%, the course is failed.

  ---

  Responsible person

  Lili Aunimo

  Teachers

  Toni Ernvall, Juha Nurmonen

  Campus

  Pasila Campus

  Teaching language

  English

  Timing

  19.01.2026 - 15.05.2026

  Groups

  • MAAIBUME
  • CONTACT

  Seats

  15 - 30

  Degree Programme

  AIBUM Degree Programme in AI for Business Transformation

  Evaluation scale

  H-5

  Credits

  • 5 cr

  Unit

  Master’s Degrees