Please select the curriculum by the start year of studies and track.
English
Online implementation.
Sessions in Tuula's Zoom.
THIS IS AN ONLINE IMPLEMENTATION with webinars approx. one per month.
Individual learning tasks: e.g. Elevator speech, essays, small reports, videos
Small group work online
Webinars
Online Discussions
Recorded lectures or lectures in webinars
Bryman, A. 2012. Social research methods. Oxford: Oxford University Press cop.
Ethical principles and ethical review in human sciences. 2020. Finnish National Board on Research Integrity. www.tenk.fi/en
Kananen, J. 2011. Rafting through the Thesis Process. Jyväskylä: Jyväskylä University of Applied Sciences.
Responsible conduct of research and procedures for handling allegations of misconduct in Finland. 2012. Finnish National Board on Research Integrity. www.tenk.fi/en
Tang, H. 2021. Engineering Research. Design, Methods, and Publication. John Wiley and Sons Inc..
Tuula Kotikoski
If possible, also webinars with R&D experts from the working life
08.01.2024 - 30.04.2024
- six online webinars 12 h
- webinars with guest speakers if guest speakers available 0-4 h
- learning tasks 50 h
- studying the course material 72 h
Total 135 hours of student work.
20.11.2023 - 04.01.2024
Online Implementation:
Presenting on the online discussion platform is a sign of your participation in the course and it can be confirmed by completing the Ethics task.
The course is modular, which means it takes 2-3 weeks to complete the materials and learning tasks of each of the five modules.
The course has 2-3 webinars in modules 1 and 2 if guest speakers can be arranged.
You can complete the course by combining study and work, by studification. Agree on studification of the course with the lecturer in charge of the course. For more information on studification, see the degree regulations and study guide.
20 - 35
Avoin AMK 5 (not translated)
Master's Degree Programme in Artificial Intelligence and Data Analytics
Face-to-face
School of Technology
English
Contact lectures on campus:
* Sat 2.9.2023 from 9.00 to 15.00
* Sat 28.10.2023 from 9.00 to 15.00
Lectures are given at the following location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.
- contact lectures during two weekends per semester
- independent study
- distance learning
- assignments
- learning project (group work)
Learning material is defined during the first contact session and more can be found in the slide sets.
Relevant parts of the following books can be used as background material:
The Essential AI Handbook for Leaders
by Peltarion (59 pages).
Ethical Artificial Intelligence
by Bill Hibbard (2015, 177 pages).
The Quest for Artificial Intelligence: A History of Ideas and Achievements
by Nils J. Nilsson (2009, 707 pages).
A Brief Introduction to Machine Learning for Engineers
by O. Simeone (237 pages)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition (corrected)
by Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2017, 764 pages).
Python Data Science Handbook: Essential Tools for Working with Data
by Jake VanderPlas (2016 O'Reilly Media, 541 pages).
pandas: powerful Python data analysis toolkit
by Wes McKinney and the Pandas Development Team (2020, 3071 pages).
Juha Peltomäki
The aim is to connect the content of the course to problems that occur in working life.
28.08.2023 - 19.12.2023
One credit (1 Cr) corresponds to an average of 27 hours of work. Five (5) ECTS credits equals about 135 hours of study work.
- lectures 24 h
- learning project and seminar 40 h
- assignments 40 h
- independent study 31 h
Total 135 h
01.08.2023 - 08.09.2023
The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.
20 - 35
The evaluation is based on a set of the following assignment types:
- learning project in a group
- assignments
Master's Degree Programme in Artificial Intelligence and Data Analytics
Face-to-face, Online learning
4 cr
School of Technology
English
Online lectures (recorded) and exercise sessions.
Two mandatory campus meetings (Fri-Sat): Jan 12-13 and Mar 15-16, 2024
Online material.
Harri Varpanen
No exams.
08.01.2024 - 30.04.2024
20.11.2023 - 04.01.2024
Seven sets of theory / exercises:
1. Orientation (basics, numpy, matrices)
2. Data manipulation (pandas)
3. Data visualization (matplotlib & seaborn)
4. Time series (pmdarima)
5. Linear regression (scipy / sklearn)
6. Logistic regression (scipy / sklearn)
7. Dimension reduction (scipy / sklearn).
Uni. Helsinki: Data Analysis with Python (MOOC), course code CSM90004
20 - 35
The grade is determined by the number of completed exercises.
Basic knowledge of python is assumed.
Master's Degree Programme in Artificial Intelligence and Data Analytics
Face-to-face
School of Technology
English
Virtual study, contact study.
Lecture notes, exercises, video examples
Muller, Guido: Introduction to Machine Learning with Python
Tomi Nieminen
28.08.2023 - 19.12.2023
Virtual study 110 h
Contact study 15 h
01.08.2023 - 08.09.2023
20 - 35
Master's Degree Programme in Artificial Intelligence and Data Analytics
Face-to-face
School of Technology
English
Sanna Sihvonen
04.09.2023 - 20.12.2023
01.08.2023 - 06.09.2023
0 - 40
Master's Degree Programme in Sport and Exercise Physiotherapy, Master's Degree Programme in Artificial Intelligence and Data Analytics, Master's Degree Programme in Information Technology, Full Stack Software Development, Master's Degree Programme in Advanced Practice Nursing, Master's Degree Programme in Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security
Face-to-face
School of Health and Social Studies
English
Mandatory contacts days:
Friday 8.9.2023 15.00-20.00
Saturday 11.11.2023 9.00-15.00
Teams Meeting is not available.
Mika Rantonen
Lutakko Campus
28.08.2023 - 19.12.2023
01.08.2023 - 08.09.2023
20 - 35
Master's Degree Programme in Artificial Intelligence and Data Analytics
Face-to-face
School of Technology
English
Webinars (with Zoom):
September 27. 2023 from 9 am to 12 pm Finnish time
November 28. 2023 from 9 am to 15 pm Finnish time
November 29. 2023 from 15.30 pm to 17.30 pm Finnish time
E-learning.
Online teaching, assignments, small group work, independent work and literature.
Salla Grommi
- Daft, R. 2015-2018. The Leadership Experience, 6h or 7th Edition – CENGAGE Learning 2015-2018
- Northouse, P. 2016. Leadership: Theory and Practice. Sage
Suitable article from e.g. The Leadership Quarterly, Journal of Change Management, Journal of Organizational Change Management
Salla Grommi
No exam
-
20.09.2023 - 31.12.2023
Online meetings
Independent work and group work
Total 135 h
01.08.2023 - 24.08.2023
The first webinar instructs and stages the progress of the course.
The central learning task of the course is a work-based development task performed by the student in his / her own (or imagined) work organization during the course. The student can choose a topical development topic related to the themes of the course from his / her own organization. The aim is to apply theoretical knowledge to solve a practical problem and to write a project plan-type reflective essay or project report on it.
20 - 30
The assessment of the course is based on the assessment criteria derived from the learning objectives.
Tasks are assessed as accepted / rejected, except for the work-based development task (see above), which is assessed with grades 0-5.
The evaluation of the entire course (0-5) thus consists in practice of the grade of the development task.
Master's Degree Programme in Artificial Intelligence and Data Analytics, Master's Degree Programme in Information Technology, Full Stack Software Development, Diploma in International Business Management, Master's Degree Programme in Advanced Practice Nursing, Master's Degree Programme in Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security
Online learning
5 cr
School of Health and Social Studies
There is no implementations attached to this degree programme.
Student knowledge profile is dictated through his personal learning plan.
Artificial Intelligence and Data Analytics Master’s Degree programme student knows how about possibilities of data analytics and artificial intelligence, their applications, the importance of data quality, and the ethics of artificial intelligence. The student will know how to plan, develop, implement data analytics and artificial intelligence to the various real-world challenges of the project. The student have knowledge and skills to apply the methods to right data in real environments.
The student will have the ability for life-long learning professionally, make decisions and communicate effectively as part of a multinational team. The student knows how to conduct research ethically.
The professional growth of students is carried out with a tutoring process which starts at the very beginning of the studies and continues until graduation. In this process students complete their personal learning plans and discuss their needs for developing their target competences with their designated teacher tutors. The 60-ECTS programme is designed as obligatory core studies and optional studies. The discussions with the teacher tutor lead to selecting appropriate courses in students’ optional studies. The core studies icnludes 20 ECTS Artificial Intelligence and Data Analytics module and 30 ECTS Master thesis. Artificial Intelligence and Data Analytics module develop students' expertise in the Artificial Intelligence and Data Analytics field and the students can choose their topics for their 30-ECTS master thesis. The students are provided with personal supervision and guidance in master thesis seminars when writing their theses. The courses in the management module and the research and development module aim to develop students' shared master competences. In addition, students can take 5 ECTS of elective studies from management electives or other available master level courses and develop their complementary competence.
Students can take the studies part-time (in 3 years) based on their preference. Most of the courses are implemented by blending in-class activities with learning in virtual environments. Students can choose their electives from master level courses of other degree programmes or from CampusOnline, Edufutura, Yritystehdas, and Future Factory. Prior learning can be recognized via other master level studies or through work experience based on discussions with course instructors.
Working life cooperation is an ongoing practice in the programme's curriculum as part-time students continuously reflect on their learning in the courses in the light of their current and previous experiences with their work experiences. Interaction with organisations is intensified during the master thesis stage in the studies when students target to develop practices at their work places or other organisations. Guest lectures given by industry professionals further enhance the working life cooperation.
The students are able to carry out duties as data scientists, AI/ML specialists and managerial work in demanding artificial intelligence and data analytics projects. Developing as an AI/DA expert opens new and challenging positions, and an opportunity to progress in one’s career. The student will deepen his leadership and communication skills as well as networking skills in different roles in AI/DA projects and various roles within the organization. A postgraduate can work as an organizational developer and active producer of new knowledge and skills.
There are no specific degree-related or statutory qualification requirements in the field.
The graduate may apply to continue on to postgraduate studies in science or arts at universities (Act 558/2009, Section 37) and in the school of professional teacher education. The studies can be continued by applying e.g. to corresponding post-degree education at universities abroad. A university of applied sciences also provides opportunities for continuing education in the form of specialisation studies, open studies, an online study portal (CampusOnline) and working life based continuing education.
The learning outcomes laid down in the curriculum of the degree programme have been derived from the analysis of the operating environment , JAMK's own strategy, and the school's core competence areas. The planning has been carried out in cooperation with representatives from regional working life. The development proposals and course feedback submitted by the students of the degree programme have been considered in the development of the curriculum. International expertise takes place by comparing the contents of the educational offerings of partner universities and with the expertise of the visiting lecturers. Representatives of the degree programme are closely involved in the activities of regional and international industirial networks.