Academic year 2021 - 2022

• Leadership Dynamics (YZWZ0220-3001),
       01.08.2021 – 31.12.2021,  5 cr  (YTF21S1, ...) — Online learning +-
  Learning outcomes of the course

  You will understand different approaches to leadership by exploring theories and models, and you will be able to expand and apply them to practical work. You can learn key leadership practices and describe them from the perspective of your job role or interest of study. You understand the importance of an ethical attitude to leadership. You will identify and assess your leadership and emotional intelligence and interaction skills and benefits, and identify your key areas for development. You will experience & test practical communications skills through interaction and discussions.

  Prerequisites and co-requisites

  - (not translated)

  Course contents

  Leadership theories & styles
  Current phenomena of leadership
  Ethical attitude to leadership
  Emotional intelligence and emotions
  Communication and interaction tools & skills

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Adequate 1
  The theory of the subject matter as summarised by the student is appropriate for the subject matter or work role, but it does not form an overall view. The student describes and distinguishes essential concepts. The student outlines a development project. The student finds a partial solution to the problem but is unable to illustrate the results. Matters are discussed in a list-like and summarising manner. Independent thinking is lacking. The reporting follows JAMK’s reporting instructions partially.
  
  Satisfactory 2
  The student applies theory within the subject matter, limited from the perspective of the subject matter or work role. The student has selected and defined the essential concepts. The student designs a development project. The student finds a partial solution to the problem. They illustrate the results. The examples demonstrate understanding and independent thinking, but the connections between theoretical knowledge and the examples are insufficient. The report mostly follows JAMK’s reporting instructions.

  Assessment criteria - grade 3 and 4

  Good 3
  You present theory that is relevant and delineated. You are able to follow and analyse the development and/or research in your field of expertise. You are able to analyse and structure theoretical information and concepts. You lead or plan a research, development or innovation initiative, applying new methods. You develop the operations of the organisation by finding alternative solutions to a problem. Theory and practice are combined through your thinking. The report is illustrative and includes analysis. The report follows JAMK’s reporting instructions (may contain occasional errors).
  
  Very good 4
  You produce an overall view from the essential theories in the subject matter, which you reflect on in a critical manner. You are able to follow and analyse the development and international research in your field of expertise. You are able to consider the essential concepts in relation to each other. You lead or plan a research, development or innovation initiative that produces a solution for a practical problem. You demonstrate and justify the usability of your results. The report is written in a consistent, justified and illustrative manner. It contains reflection and criticism. The report follows JAMK’s reporting instructions. There may be minor deficiencies with some details.

  Assessment criteria - grade 5

  Excellent 5
  You are able to create new information. You are able to follow and analyse the development and international research in the fields of expertise. You reflect on the matter at a social level. You are able to define the essential concepts related to the special competence of the field in relation to each other. You lead or plan a research, development or innovation initiative that produces and analyses new information and reforms the practices in complex and unpredictable operating environments. You utilise methods from research and development operations, and display creative thinking. The report has created new perspectives or overall views by combining things in a new way. The text displays the ability for critical thinking and the skill of communicating the results. The report follows JAMK’s reporting instructions.

  ---

  Language of instruction

  English

  Location and time

  YZWZ0220-3001 Leadership Dynamics for 20 students Total ONLINE Course-Journey:
  
  - Check-in session duration: 45-60 min.
  On Zoom => Friday 15.10.2021 at 5 pm (Helsinki Time Zone)
  
  
  - WEBINAR & WORKSHOP (online on zoom)
  
  Friday the 29th of October 2021 from 2.00 pm to 7.00 pm
  Saturday the 30th of October 2021 from 10.00 am to 3.00 pm
  
  - Check-out session: Duration 45-60 min.
  On Zoom => Friday 26.11.2021 at 5 pm (Helsinki Time Zone)

  Planned learning activities, teaching methods and guidance

  The course will appear on your MOODLE space during Week 41
  
  The course is meant as an intensive webinar/workshop journey-experience where the student experience:
  
  1. Experiential learning /
  2. Individual & Collective Coaching /
  3. Network activation /
  4. Executive process and follow up /
  5. Awareness-based leadership & decision making.

  Learning materials and recommended literature

  As guiding books:
  
  - Daft, R. 2015-2018. The Leadership Experience, 6h or 7th Edition – CENGAGE Learning 2015-2018
  - Northouse, P. 2016. Leadership: Theory and Practice. Sage Publications.
  A topical non-fiction book on human leadership.
  
  Other:
  Teacher's publications & material.

  Lecturer(s)

  Marcella Zoccoli

  Timing

  01.08.2021 - 31.12.2021

  Enrollment

  02.08.2021 - 05.09.2021

  Content scheduling

  Leadership Dynamics
  
  Overview & ''Polaroid'' on Leadership theories & styles
  Leadership Total Awareness
  Emotional intelligence & other human intelligences
  Communication & Intra- and Interdependent relational skills
  Leading in the present Change towards a transformative Future

  Groups

  • YTF21S1
  • YTF20S1

  Alternative learning methods

  The passing of the course requires active participation on webinar days (check-in & check-out sessions included) + at least 60% of the assignments-project activities (detailed info will be offered during the check-in session)
  
  - Self-reflective individual paper (30%) period 15.10 - 26.11.2021
  - Project-base collective work or workshop training (30%)
  - Development plan (40%)

  Seats

  10 - 40

  Degree Programme

  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 Professional Project Management, Master's Degree Programme in Information Technology, Cyber Security

  Mode of delivery

  Online learning

  Share of R&D studies

  5 cr

  Share of virtual studies

  5 cr

  Credits

  • 5 cr

  Unit

  School of Technology
• Research-based Development (YZWZ0110-3013),
       01.01.2022 – 31.07.2022,  5 cr  (YTF21S1, ...) — Face-to-face +-
  Learning outcomes of the course

  Purpose of the course
  The purpose of this course is to understand the meaning of an research-based development and to get own toolkit for development activities
  
  Competences
  Ethical competence
  Learning and information management competence
  Entrepreneurship, innovation and working community competence
  
  Learning outcomes
  After completing this course, you will understand the difference between scientific research and research-based development. You understand the principles of research-based development,, its methodological starting points. You know the process of research-based development. You master and are able to apply some working life development methods in development activities at your filed of practice. You understand the importance of research ethics and good scientific practice in research-based development.

  Course contents

  The starting points and methodology of research-based development
  Research ethics and the responsible conduct of research
  Process and management of research-based development
  Research and development methods, data collection and data analysis
  Assessment as part of research-based development

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1
  The student understands the differences and similarities between research-based development and academic research. The student is able to describe the different stages of the development process and choose methods suitable for their research/development topic. The student understands the significance of research ethics in research-based development.
  
  Satisfactory 2
  The student understands the differences and similarities between research-based development and academic research. The student describes the stages of the development process and chooses methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 3 and 4

  Good 3
  The student is able to compare the differences and similarities between research-based development and academic research. The student is able to analyse development processes, methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.
  
  Very good 4
  The student compares the similarities and differences between research-based development and academic research in a diverse way. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and reflects on and assesses their suitability critically and with the help of literature.The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 5

  Excellent 5
  The student is able to critically assess research-based development. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and critically assesses their suitability for development work in the field. The student understands the significance of research ethics in research-based development.

  Further information

  The course can be studied by using studification combining study & work in accordance with the
  JAMK Degree regulation 17 § the guidelines for identifying and recognizing competence and the JAMK processes.

  ---

  Language of instruction

  English

  Location and time

  Online implementation.
  Sessions in Tuula's Zoom.

  Planned learning activities, teaching methods and guidance

  Individual learning tasks: e.g. Elevator speech, essays, small reports, videos
  Small group work online
  Webinars
  Online Discussions
  Recorded lectures or lectures in webinars

  Learning materials and recommended literature

  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
  
  Responsible conduct of research and procedures for handling allegations of misconduct in Finland. 2012. Finnish National Board on Research Integrity. www.tenk.fi/en

  Lecturer(s)

  Tuula Kotikoski

  Working life cooperation

  If possible, webinars with R&D experts from the working life

  Timing

  01.01.2022 - 31.07.2022

  Learning assignments and student workload

  -lectures/online lectures 12 h
  - webinars with guest speakers or course tutor/replacing assignments 6-12 h
  - learning tasks 50 h
  - studying the course material 61 h
  
  Total 135 hours of student work.

  Enrollment

  01.11.2021 - 09.01.2022

  Content scheduling

  Online Implementation:
  Presenting on the online discussion platform is a sign of your participation in the course. A video presentation should be made at the beginning of the course during the first two weeks.
  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 4-6 webinars in modules 1-3 if guest speakers can be arranged.

  Groups

  • YTF21S1
  • ZJA21STPYPP

  Alternative learning methods

  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.

  Assessment methods

  Avoin AMK 5 (not translated)

  Degree Programme

  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 Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Health and Social Studies
• Managerial Accounting (YZWZ0230-3006),
       01.08.2021 – 31.12.2021,  5 cr  (YTF21S1) — Online learning +-
  Learning outcomes of the course

  You are aware of the essential current theories of managerial accounting and are able to apply them to your work. You display an ability to critically follow and analyse the information in the field and area of study. You recognise the challenges of managerial accounting and are able to develop your own skills and practices in managerial accounting. You are able to evaluate, develop and reform managerial accounting in an organisation.
  
  Learning Outcomes: The purpose of the Management Accounting course is to enhance your knowledge and understanding, and intellectual skills principally in the field of accounting data analysis, which can be subsequently applied to make interpretations and draw inferences in the field of corporate finance, corporate governance and financial management.
  The course aims to develop the following Intended Learning Outcomes (ILOs):
  - 1. Applied Business Skills (IBBUS)
  Analyzing the financial statements of corporates to ascertain their financial health by applying various techniques and methods learned in the field of Management Accounting and Economics in different business situations to solve challenging business problems optimize planning and risk management strategies.
  -2 Skills in Critical and Analytical Understanding (IBCRI)
  Reviewing academic literature critically, analyzing and evaluating financial and other business data, deliberating diverse approaches, comparing the resultant outcomes, and making value additive informed business decisions and action plans. Understanding of the true, objective and unbiased financial information that helps managers to plan, revise and revisit their business strategies, financial targets, policy formulation, and planning process. The process of understanding how financial accounting information assists to make the corporate decision-making process is a core element of this course.
  -3 Communication Skills (IBCOM)
  Communicating the inferences made and interpretations drawn based on the financial data analysis responsibly, effectively and professionally in English, including in written reports, presentations, formal correspondence, and on social media.
  -4 Intercultural Collaboration Skills (IBCOL)
  Participating enthusiastically in inter- and multicultural settings, demonstrating team building and leadership skills, and implementing conflict resolution skills in numerous situations.
  -5 Ethical Conduct (IBETH)
  Promoting moral and ethical mindset and conduct to ascertain and disclose true and unbiased financial health of the companies. Safeguarding interests of different stakeholders in an unbiased manner to promote corporate value system which is humane, fair and equitable.
  The purpose of the Management Accounting course is to enhance your knowledge and understanding, and intellectual skills principally in the field of management accounting and more importantly their linkages with the issues pertaining to corporate finance, and strategy.
  According to above-mentioned Intended Learning Outcomes (ILOs) you will find exposure to numerous situations, whereby you record and present financial accounting data in such a manner that the users of financial accounting statements can know about the real financial health of the firm being analyzed. The true, objective and unbiased financial information help managers to plan and revise their business strategies, check financial health, policy formulation and planning process. The process of understanding how financial accounting information assists to make corporate decision-making process is a core element of this course. You are also expected to understand different management accounting functions and applications, diverse management accounting techniques, methods and tools in different business situations in order to solve challenging business problems. You will understand the relevance of the high-quality accounting information and their multiple interpretations in the business life, which ultimately optimize planning, strategies, risk management and business-decision making.
  You are further expected to understand that accounting is not isolation of overall business strategy of corporates. You will create and produce indicators describing the success of operations based on cause/consequence logic, as well as able to collect information pertaining to corporates needed for measurement. You will identify the economic requirements and the resource requirements related to personnel, necessary to achieve the abovementioned objectives. You will use budget information as a tool for examining and evaluating the results and analyze financial risks of a firm and manage them.

  Prerequisites and co-requisites

  Bachelor's degree

  Course contents

  Theoretical aspects of accounting, financial statement analysis, financial performance measurement, regulatory framework, corporate governance and ethical issues of accounting, financial ratio analysis, corporate valuation, balanced scorecard as a tool for strategic budgeting, economic value added (EVA), cost concepts, absorption costing, pricing, planning and budgeting, fundamental of financial management, capital investment appraisal, financial risk management, cash flow, forecasting, sensitivity analysis.

  Assessment criteria

  Assessment criteria - grade 1 and 2

  1 (Adequate) The theory of the subject matter is appropriate for the subject matter or work role, but it does not form an overall view. You describe and distinguish essential concepts. You outline a development project. You find a partial solution to the problem but are unable to illustrate the results. Matters are discussed in a list-like and summarising manner. Independent thinking is lacking. The reporting follows JAMK’s reporting instructions partially.
  
  2 (Satisfactory) You apply theory within the subject matter, limited from the perspective of the subject matter or work role. You have selected and defined the essential concepts. You design a development project. You find a partial solution to the problem. You illustrate the results. The examples demonstrate understanding and independent thinking, but the connections between theoretical knowledge and the examples are insufficient. The report mostly follows JAMK’s reporting instructions.

  Assessment criteria - grade 3 and 4

  3 (Good) You present theory that is relevant and delineated. You are able to follow and analyse the development and/or research in your field of expertise. You are able to analyse and structure theoretical information and concepts. You lead or plan a research, development or innovation initiative, applying new methods. You develop the operations of the organisation by finding alternative solutions to a problem. Theory and practice are combined through your thinking. The report is illustrative and includes analysis. The report follows JAMK’s reporting instructions (may contain occasional errors).
  
  4 (Very good) You produce an overall view from the essential theories in the subject matter, which you reflect on in a critical manner. You are able to follow and analyse the development and international research in your field of expertise. You are able to consider the essential concepts in relation to each other. You lead or plan a research, development or innovation initiative that produces a solution for a practical problem. You demonstrate and justify the usability of your results. The report is written in a consistent, justified and illustrative manner. It contains reflection and criticism. The report follows JAMK’s reporting instructions. There may be minor deficiencies with some details.

  Assessment criteria - grade 5

  5 (Excellent) You are able to create new information. You are able to follow and analyse the development and international research in the fields of expertise. You reflect on the matter at a social level. You are able to define the essential concepts related to the special competence of the field in relation to each other. You lead or plan a research, development or innovation initiative that produces and analyses new information and reforms the practices in complex and unpredictable operating environments. You utilise methods from research and development operations, and display creative thinking. The report has created new perspectives or overall views by combining things in a new way. The text displays the ability for critical thinking and the skill of communicating the results. The report follows JAMK’s reporting instructions.

  ---

  Language of instruction

  English

  Location and time

  This course starts on 7th October. The last day of this course is on 10th December.
  Zoom: https://jamk.zoom.us/j/3100848876
  
  The first webinar at Zoom on 7th October at 5 pm
  The second webinar at Zoom on 14th October at 5 PM
  
  Additional webinars:
  On 11th November, 4th November, and 2nd December

  Planned learning activities, teaching methods and guidance

  Individual studying with webinars and the help of the teacher. Distance learning includes material in the e-learning environment, videos, and learning assignments.
  
  There are one assignment (ca. 2500 words per assignment), two discussion rounds, a learning diary, and digital lessons with quizzes.
  
  If you prefer studying in the classroom, there will be another course next spring.

  Learning materials and recommended literature

  The following literacy is available via JAMK library services. This list of literacy is not exclusive, you may also utilize other applicable sources you find. Also note, that some of the sources can be found in both e-books and printed books.
  
  E-books:
  
  Epstein M. & Malina M. Advances in Management Accounting. Emerald 2016.
  
  Manzoni J-F. & Davila A. Performance Measurement and Management Control: Innovative Concepts and Practices Studies in Managerial and Financial Accounting Ser. Emerald Publishing Limited 2010.
  
  McNair-Connolly CJ., Polutnik L. & Silvi R. Value Creation in Management Accounting and Strategic Management: Using Information to Capture Customer Value. Business Expert Press 2013.
  
  Proctor, R. Managerial Accounting: A Decision Making and Performance Management. Pearson Education 2012. 4th Edition.
  
  Satoshi S., Nabyla D. & Sumitaka U. Value Creation in Management Accounting and Strategic Management. John Wiley & Sons, Incorporated in 2017.
  
  If you need back up in FINNISH: Järvenpää - Länsiluoto - Partanen - Pellinen: Talousohjaus ja kustannuslaskenta. Sanoma Pro 2017 or any other managerial accounting (=SISÄINEN LASKENTATOIMI or JOHDON LASKENTATOIMI) basic text book.
  
  Printed books:
  
  Bergstrand J. Management Accounting: A Cases Approach. Pearson 2012.
  
  Bhamani A., Horngren C T., Datar S M. & Rajan M V. Management and Cost Accounting. Pearson Education 2015. 6th Edition.
  
  Braun, K W. & Tietz W M. Managerial Accounting. Pearson 2013. 3rd Edition.

  Lecturer(s)

  Milla Welling

  Exam dates and re-exam possibilities

  No exam

  Timing

  01.08.2021 - 31.12.2021

  Learning assignments and student workload

  Webinars 4 h and 131 hours of independent online studying.

  Enrollment

  01.08.2021 - 06.09.2021

  Groups

  • YTF21S1

  Assessment methods

  The assessment in the course is based on the competence demonstrated in the learning diary and assignments.

  Degree Programme

  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 Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security

  Mode of delivery

  Online learning

  Share of virtual studies

  5 cr

  Credits

  • 5 cr

  Unit

  School of Business
• Research-based Development (YZWZ0110-3010),
       30.08.2021 – 17.12.2021,  5 cr  (YTI20S1) — Face-to-face +-
  Learning outcomes of the course

  Purpose of the course
  The purpose of this course is to understand the meaning of an research-based development and to get own toolkit for development activities
  
  Competences
  Ethical competence
  Learning and information management competence
  Entrepreneurship, innovation and working community competence
  
  Learning outcomes
  After completing this course, you will understand the difference between scientific research and research-based development. You understand the principles of research-based development,, its methodological starting points. You know the process of research-based development. You master and are able to apply some working life development methods in development activities at your filed of practice. You understand the importance of research ethics and good scientific practice in research-based development.

  Course contents

  The starting points and methodology of research-based development
  Research ethics and the responsible conduct of research
  Process and management of research-based development
  Research and development methods, data collection and data analysis
  Assessment as part of research-based development

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1
  The student understands the differences and similarities between research-based development and academic research. The student is able to describe the different stages of the development process and choose methods suitable for their research/development topic. The student understands the significance of research ethics in research-based development.
  
  Satisfactory 2
  The student understands the differences and similarities between research-based development and academic research. The student describes the stages of the development process and chooses methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 3 and 4

  Good 3
  The student is able to compare the differences and similarities between research-based development and academic research. The student is able to analyse development processes, methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.
  
  Very good 4
  The student compares the similarities and differences between research-based development and academic research in a diverse way. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and reflects on and assesses their suitability critically and with the help of literature.The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 5

  Excellent 5
  The student is able to critically assess research-based development. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and critically assesses their suitability for development work in the field. The student understands the significance of research ethics in research-based development.

  Further information

  The course can be studied by using studification combining study & work in accordance with the
  JAMK Degree regulation 17 § the guidelines for identifying and recognizing competence and the JAMK processes.

  ---

  Language of instruction

  English

  Location and time

  Online implementation.
  Sessions in Tuula's Zoom.

  Planned learning activities, teaching methods and guidance

  Individual learning tasks: e.g. Elevator speech, essays, small reports, videos
  Small group work online
  Webinars
  Online Discussions
  Recorded lectures or lectures in webinars

  Learning materials and recommended literature

  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
  
  Responsible conduct of research and procedures for handling allegations of misconduct in Finland. 2012. Finnish National Board on Research Integrity. www.tenk.fi/en

  Lecturer(s)

  Tuula Kotikoski

  Working life cooperation

  If possible, webinars with R&D experts from the working life

  Campus

  Lutakko Campus

  Timing

  30.08.2021 - 17.12.2021

  Learning assignments and student workload

  -lectures/online lectures 12 h
  - webinars with guest speakers or course tutor/replacing assignments 6-12 h
  - learning tasks 50 h
  - studying the course material 61 h
  
  Total 135 hours of student work.

  Enrollment

  02.08.2021 - 05.09.2021

  Content scheduling

  Online Implementation:
  Presenting on the online discussion platform is a sign of your participation in the course. A video presentation should be made at the beginning of the course during the first two weeks.
  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 4-6 webinars in modules 1-3 if guest speakers can be arranged.

  Groups

  • YTI20S1

  Alternative learning methods

  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.

  Seats

  0 - 35

  Assessment methods

  Avoin AMK 5 (not translated)

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology
• Deep Learning and Neural Networks (YTIP2400-3001),
       10.01.2022 – 22.05.2022,  5 cr  (YTI20S1) — Face-to-face +-
  Learning outcomes of the course

  The student understands the significance of deep learning in the digitalizing operational environment and knows the type and structure of neural network.
  
  The student knows about the most common methods of deep learning and how to desing and apply them in practice to the data and interpret the results of the methods. In addition, the student undestands the mathematic behind the neural networks.
  
  Course competences:
  EUYIV EUR-ACE: Investigations, Master's Degree
  EUYER EUR-ACE: Engineering Practice, Master's Degree
  EUYMJ EUR-ACE: Making Judgements, Master's Degree

  Course contents

  - Structure of neural network vs artificial neural network
  - Different types of neural networks and their applications: CNN, RNN, LST, autocorrelation, autoencoder (CAN), NLP etc.
  - Mathematic behind the neural network
  - Feature engineering
  - Training process of neural network
  - Estimate and interpret the results
  - Transfer and Active Learning
  - MLOps

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1: Student has sufficient knowledge of deep learning and neural networks. Student is able to apply the most common techniques of deep learning and has the sufficient knowledge of mathematic behind the technigues. Additionally, the student is able to estimate and validate implementation briefly.
  
  Satisfactory 2: Student has satisfied knowledge of deep learning and neural networks. Student is able to choose suitable deep learning tehnique and apply the technical know-how in practise. Student undestands the mathematic behind the technigues in satisfying level. Additionally, the student is able to estimate and validate implementation superficially.

  Assessment criteria - grade 3 and 4

  Good 3: Student has good knowledge of deep learning and neural networks. Student is able to choose appropriate deep learning tehnique and apply the technical know-how in practise. Student undestands well the mathematic behind the technigues in good level. Additionally, the student is able to estimate and validate implementation well.
  
  Very good 4: Student has very good knowledge of deep learning and neural networks. Student is able to choose and justify versatilely deep learning tehnique and apply the technical know-how in practise. Student undestands the mathematic behind the technigues in very good level. Additionally, the student is able to estimate and validate implementation critically.

  Assessment criteria - grade 5

  Excellent 5: Student has excellent knowledge of deep learning and neural networks. Student is able to choose and justify deep learning tehnique and apply the technical know-how in practise. Student undestands the mathematic behind the technigues in excellent level. Additionally, the student is able to estimate and validate implementation critically.

  ---

  Language of instruction

  English

  Location and time

  Hybrid meeting (classroom in Dynamo/Lutakko and Teams).

  Planned learning activities, teaching methods and guidance

  2 hybrid meeting during the course.

  Learning materials and recommended literature

  Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. The MIT Press.

  Lecturer(s)

  Mika Rantonen

  Campus

  Lutakko Campus

  Timing

  10.01.2022 - 22.05.2022

  Enrollment

  01.11.2021 - 09.01.2022

  Groups

  • YTI20S1

  Seats

  0 - 35

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology
• Artificial Intelligence (YTIP2100-3002),
       27.08.2021 – 17.12.2021,  5 cr  (YTI21S1, ...) — Face-to-face, Online learning +-
  Learning outcomes of the course

  The student understands the purpose of artificial intelligence and knows the importance and possibilities of data. The student understands the importance of data quality and the ethics of artificial intelligence. The student will know the steps of data analytics/machine learning/deep learning processes. The student has knowledge and skills to plan a data strategy.
  
  Course competences
  EUYKN EUR-ACE: Knowledge and Understanding, Master's Degree
  
  EUYCT EUR-ACE: Communication and Team-working, Master's Degree
  
  EUYLL EUR-ACE: Lifelong Learning, Master's Degree

  Course contents

  The key topics of the course are:
  The possibilities of data
  Ethics of Artificial Intelligence (AI)
  The importance of data quality
  Methodology of Data analytics (DA) and AI
  DA and AI project process and management
  Data strategy
  Open source tools to DA, ML and AI

  Assessment criteria

  Assessment criteria - grade 1 and 2

  **Assessment criteria, sufficient 1, satisfactory 2
  Sufficient 1: Student has sufficient knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors at a sufficient level.
  
  Satisfactory 2: Student has satisfactory knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors at a satisfactory level.

  Assessment criteria - grade 3 and 4

  Good 3: Student has good knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors well.
  
  Very Good 4: Student has very good knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors very well.

  Assessment criteria - grade 5

  Excellent 5: Student has excellent knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a comprehensive data strategy and describe the DA/AI process. In addition, student understands excellently the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors.

  ---

  Language of instruction

  English

  Location and time

  Contact instruction over 2 weekends: on Fridays at 2pm - 8pm, and on Saturdays at 9am - 3pm.
  Location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.

  Planned learning activities, teaching methods and guidance

  Contact instruction over 2 weekends during the semester.
  Exercises and distance learning between the contact sessions.
  Study project covering a topic based on individual needs.

  Lecturer(s)

  Mika Rantonen

  Learning materials and recommended literature

  Further defined during the first contact session and more can be found in the slide sets.
  
  Relevant parts of the following books:
  
  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).

  Lecturer(s)

  Tuula Kotikoski, Tuomo Sipola

  Working life cooperation

  It is advisable that the students use their own work environment as an object of study and as a data source.
  Possible guest lecturers from companies.

  Campus

  Lutakko Campus

  Timing

  27.08.2021 - 17.12.2021

  Learning assignments and student workload

  5 ECTS credits equals about 135 hours of study work.
  Contact instruction: 24 hours
  Exercises: 48 hours
  Study project: 63 hours

  Enrollment

  02.08.2021 - 05.09.2021

  Groups

  • YTI21S1
  • ZJA21STIPYIA

  Seats

  0 - 35

  Assessment methods

  Feedback on exercises and study project.
  Self evaluation.
  Peer evaluation.
  
  Open UAS 5

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face, Online learning

  Share of virtual studies

  4 cr

  Credits

  • 5 cr

  Unit

  School of Technology
• Data Analytics (YTIP2200-3002),
       10.01.2022 – 29.04.2022,  5 cr  (YTI21S1, ...) — Face-to-face, Online learning +-
  Learning outcomes of the course

  The student understands the significance of data analytics in the digitalizing operational environment. The student knows the most commonly used methods and theories of data analytics as well as how to apply them in practice to existing data and interpret the results of the methods.
  
  Course competences
  EUYEN EUR-ACE: Engineering Analysis, Master's Degree
  EUYEE EUR-ACE: Engineering Design, Master's Degree
  EUYIV EUR-ACE: Investigations, Master's Degree

  Course contents

  - Python data analytics libraries: NumPy, Pandas, Matplotlib, Seaborn, Scipy
  - Data visualization
  - Processing of missing values and outliers
  - Statistical terms: Average, standard deviation, correlation coefficient and their interpretations
  - The concept of probability distribution, confidence interval and hypothesis testing.
  - Bernoulli and Poisson processes
  - Linear/logistic regression, decision trees

  Assessment criteria

  Assessment criteria - grade 1 and 2

  **Assessment criteria, sufficient 1, satisfactory 2
  Sufficient 1:
  
  The student knows about the most commonly used techniques in data analytics in data analysis tasks. He/she is able to apply the most common techniques to analysing data and has sufficient knowledge of the mathematics behind the techniques. Additionally, the student is able to assess his/her implementation briefly.
  
  Satisfactory 2:
  The student knows the most commonly used techniques in data analytics in data analysis tasks. He/she is able to select the techniques for analysing data and apply his/her technical know-how in practice. Student understands the mathematics behind the techniques at a satisfying level. Additionally, the student is able to assess his/her implementation superficially.

  Assessment criteria - grade 3 and 4

  Good 3:
  The student is aware of the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques of data analytics in various data analysis tasks. Student understands well the mathematics behind the techniques at a good level. He/she is able to validate and select the techniques in data analysis and apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation and validate its development.
  
  Very good 4:
  The student recognizes the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques of data analytics and is able to extensively validate the use of implemented techniques in various data analysis tasks. Student understands the mathematics behind the techniques at a very good level. He/she is able to versatilely validate and select the correct techniques for the analysis of data and apply his/her technical know-how to practice. Additionally, the student is able to assess his/her implementation profoundly and validate its development.

  Assessment criteria - grade 5

  Excellent 5:
  The student recognizes the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques in data analytics and is able to critically validate the use of implemented techniques in various data analysis tasks. Student understands the mathematics behind the techniques in excellent level. He/she is able to critically validate and select the correct techniques in data analysis regardless of the data to be analyzed and apply the technical know-how to practice. Additionally, the student is able to critically assess his/her implementation and validate its development.

  ---

  Language of instruction

  English

  Planned learning activities, teaching methods and guidance

  5 ECTS credits equals about 135 hours of study work.
  Contact instruction: 36 hours
  Exercises/Study project: 99 hours

  Lecturer(s)

  Mika Rantonen

  Learning materials and recommended literature

  Joel Grus: Data Science from Scratch: First Principles with Python

  Lecturer(s)

  Tuula Kotikoski, Harri Varpanen

  Working life cooperation

  It is advisable that the students use their own work environment as an object of study and as a data source.
  
  Possible guest lecturers from companies.

  Campus

  Lutakko Campus

  Exam dates and re-exam possibilities

  The course does not have an exam. The assessment is based on the evaluation assignments.

  Timing

  10.01.2022 - 29.04.2022

  Enrollment

  01.11.2021 - 09.01.2022

  Content scheduling

  There will be one or few contact sessions on saturdays at 9am - 3pm. The exact times are announced by the end of the year 2021. The place will be JAMK / Lutakko campus, Piippukatu, Jyväskylä.
  
  5 ECTS credits equals about 135 hours of study work.
  Contact instruction: 36 hours
  Exercises/Study project: 99 hours

  Groups

  • YTI21S1
  • ZJA21STIPYIA

  Seats

  0 - 35

  Assessment methods

  Avoin amk 5 (not translated)

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face, Online learning

  Share of virtual studies

  4 cr

  Credits

  • 5 cr

  Unit

  School of Technology
• Machine Learning (YTIP2300-3001),
       30.08.2021 – 17.12.2021,  5 cr  (YTI20S1) — Face-to-face +-
  Learning outcomes of the course

  The student understands the significance of machine learning in digitalizing operational environment. The student knows the most common machine learning methods, is able to apply them in practice to existing data and interpret the results of the methods. In addition, the student understands the mathematic behind the most commonly used machine learning algorithms.
  
  Course competences
  EUYEE EUR-ACE: Engineering Design, Master's Degree
  EUYIV EUR-ACE: Investigations, Master's Degree
  EUYER EUR-ACE: Engineering Practice, Master's Degree

  Course contents

  - Mathematics behind the machine learning algorithms
  - Most common regression and classification models of supervised machine learning and their application in Python programming environment using NumPy, Pandas and Scikit-learn libraries.
  - Choosing a suitable machine learning algorithm model and estimation of results
  - Support Vector Machine
  - Clustering
  - K-nearest neighbor
  - Reinforcement learning

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1: Student has sufficient knowledge of machine learning algorithms and neural networks. Student is able to apply the most common techniques of machine learning and has the sufficient knowledge of the mathematics behind the techniques. Additionally, the student is able to estimate and validate implementation briefly.
  
  Satisfactory 2: Student has satisfactory knowledge of machine learning and neural networks. Student is able to choose a suitable machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at a satisfactory level. Additionally, the student is able to estimate and validate implementation superficially.

  Assessment criteria - grade 3 and 4

  Good 3: Student has good knowledge of machine learning algorithms and neural networks. Student is able to choose an appropriate machine learning technique and apply the technical know-how in practice. Student understands well the mathematics behind the techniques at a good level. Additionally, the student is able to estimate and validate implementation well.
  
  Very good 4: Student has very good knowledge of machine learning algorithms and neural networks. Student is able to choose and justify versatilely a machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at a very good level. Additionally, the student is able to estimate and validate implementation critically.

  Assessment criteria - grade 5

  Excellent 5: Student has excellent knowledge of machine learning algorithms and neural networks. Student is able to choose and justify machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at an excellent level. Additionally, the student is able to estimate and validate implementation critically.

  ---

  Language of instruction

  English

  Lecturer(s)

  Mika Rantonen

  Lecturer(s)

  Tomi Nieminen

  Campus

  Lutakko Campus

  Timing

  30.08.2021 - 17.12.2021

  Enrollment

  02.08.2021 - 05.09.2021

  Groups

  • YTI20S1

  Seats

  0 - 35

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology

Academic year 2022 - 2023

• Leadership Dynamics (YZWZ0220-3011),
       01.08.2022 – 31.12.2022,  5 cr  (YTF22S1) — Face-to-face +-
  Learning outcomes of the course

  You will understand different approaches to leadership by exploring theories and models, and you will be able to expand and apply them to practical work. You can learn key leadership practices and describe them from the perspective of your job role or interest of study. You understand the importance of an ethical attitude to leadership. You will identify and assess your leadership and emotional intelligence and interaction skills and benefits, and identify your key areas for development. You will experience & test practical communications skills through interaction and discussions.

  Prerequisites and co-requisites

  - (not translated)

  Course contents

  Leadership theories & styles
  Current phenomena of leadership
  Ethical attitude to leadership
  Emotional intelligence and emotions
  Communication and interaction tools & skills

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Adequate 1
  The theory of the subject matter as summarised by the student is appropriate for the subject matter or work role, but it does not form an overall view. The student describes and distinguishes essential concepts. The student outlines a development project. The student finds a partial solution to the problem but is unable to illustrate the results. Matters are discussed in a list-like and summarising manner. Independent thinking is lacking. The reporting follows JAMK’s reporting instructions partially.
  
  Satisfactory 2
  The student applies theory within the subject matter, limited from the perspective of the subject matter or work role. The student has selected and defined the essential concepts. The student designs a development project. The student finds a partial solution to the problem. They illustrate the results. The examples demonstrate understanding and independent thinking, but the connections between theoretical knowledge and the examples are insufficient. The report mostly follows JAMK’s reporting instructions.

  Assessment criteria - grade 3 and 4

  Good 3
  You present theory that is relevant and delineated. You are able to follow and analyse the development and/or research in your field of expertise. You are able to analyse and structure theoretical information and concepts. You lead or plan a research, development or innovation initiative, applying new methods. You develop the operations of the organisation by finding alternative solutions to a problem. Theory and practice are combined through your thinking. The report is illustrative and includes analysis. The report follows JAMK’s reporting instructions (may contain occasional errors).
  
  Very good 4
  You produce an overall view from the essential theories in the subject matter, which you reflect on in a critical manner. You are able to follow and analyse the development and international research in your field of expertise. You are able to consider the essential concepts in relation to each other. You lead or plan a research, development or innovation initiative that produces a solution for a practical problem. You demonstrate and justify the usability of your results. The report is written in a consistent, justified and illustrative manner. It contains reflection and criticism. The report follows JAMK’s reporting instructions. There may be minor deficiencies with some details.

  Assessment criteria - grade 5

  Excellent 5
  You are able to create new information. You are able to follow and analyse the development and international research in the fields of expertise. You reflect on the matter at a social level. You are able to define the essential concepts related to the special competence of the field in relation to each other. You lead or plan a research, development or innovation initiative that produces and analyses new information and reforms the practices in complex and unpredictable operating environments. You utilise methods from research and development operations, and display creative thinking. The report has created new perspectives or overall views by combining things in a new way. The text displays the ability for critical thinking and the skill of communicating the results. The report follows JAMK’s reporting instructions.

  ---

  Language of instruction

  English

  Location and time

  WEBINAR & WORKSHOP days are planned to happen on ZOOM platform
  The teacher zoom classroom link will be given close to the start of the course via Peppi's e-mail
  
  This course is ONLINE on ZOOM
  Please note seminar & workshop days are:
  
  Friday 07.10.2022 from 14.30 pm to 19.30
  Saturday 08.10.2022 from 10.00 am to 15.00
  
  and
  
  Friday 11.11.2022 from 14.30 pm to 19.30
  Saturday 12.11.2022 from 10.00 am to 15.00

  Planned learning activities, teaching methods and guidance

  The course is meant as an intensive seminar/workshop journey where the student experience:
  1. Experiential learning
  2. Individual & Collective Coaching
  3. Network activation
  4. Executive process and follow up
  5. Awareness-based leadership & decision making

  Learning materials and recommended literature

  As guiding books:
  
  - Daft, R. 2015-2018. The Leadership Experience, 6h or 7th Edition – CENGAGE Learning 2015-2018
  - Northouse, P. 2016. Leadership: Theory and Practice. Sage Publications.
  A topical non-fiction book on human leadership.
  
  Other:
  Teacher's publications & material

  Lecturer(s)

  Marcella Zoccoli

  Timing

  01.08.2022 - 31.12.2022

  Learning assignments and student workload

  The passing of the course requires active participation on seminar & workshop days
  
  - Individual work 30%
  - Project-base collective work or workshop training 70%

  Content scheduling

  Leadership Dynamics
  
  Overview & ''Polaroid'' on Leadership theories & styles
  Leadership Total Awareness
  Emotional intelligence & other human intelligences
  Communication & Intra- and Interdependent relational skills
  Leading in the present Change towards a transformative Future

  Groups

  • YTF22S1

  Seats

  0 - 30

  Degree Programme

  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 Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology
• Managerial Accounting (YZWZ0230-3010),
       01.08.2022 – 31.12.2022,  5 cr  (YTF22S1) — Online learning +-
  Learning outcomes of the course

  You are aware of the essential current theories of managerial accounting and are able to apply them to your work. You display an ability to critically follow and analyse the information in the field and area of study. You recognise the challenges of managerial accounting and are able to develop your own skills and practices in managerial accounting. You are able to evaluate, develop and reform managerial accounting in an organisation.
  
  Learning Outcomes: The purpose of the Management Accounting course is to enhance your knowledge and understanding, and intellectual skills principally in the field of accounting data analysis, which can be subsequently applied to make interpretations and draw inferences in the field of corporate finance, corporate governance and financial management.
  The course aims to develop the following Intended Learning Outcomes (ILOs):
  - 1. Applied Business Skills (IBBUS)
  Analyzing the financial statements of corporates to ascertain their financial health by applying various techniques and methods learned in the field of Management Accounting and Economics in different business situations to solve challenging business problems optimize planning and risk management strategies.
  -2 Skills in Critical and Analytical Understanding (IBCRI)
  Reviewing academic literature critically, analyzing and evaluating financial and other business data, deliberating diverse approaches, comparing the resultant outcomes, and making value additive informed business decisions and action plans. Understanding of the true, objective and unbiased financial information that helps managers to plan, revise and revisit their business strategies, financial targets, policy formulation, and planning process. The process of understanding how financial accounting information assists to make the corporate decision-making process is a core element of this course.
  -3 Communication Skills (IBCOM)
  Communicating the inferences made and interpretations drawn based on the financial data analysis responsibly, effectively and professionally in English, including in written reports, presentations, formal correspondence, and on social media.
  -4 Intercultural Collaboration Skills (IBCOL)
  Participating enthusiastically in inter- and multicultural settings, demonstrating team building and leadership skills, and implementing conflict resolution skills in numerous situations.
  -5 Ethical Conduct (IBETH)
  Promoting moral and ethical mindset and conduct to ascertain and disclose true and unbiased financial health of the companies. Safeguarding interests of different stakeholders in an unbiased manner to promote corporate value system which is humane, fair and equitable.
  The purpose of the Management Accounting course is to enhance your knowledge and understanding, and intellectual skills principally in the field of management accounting and more importantly their linkages with the issues pertaining to corporate finance, and strategy.
  According to above-mentioned Intended Learning Outcomes (ILOs) you will find exposure to numerous situations, whereby you record and present financial accounting data in such a manner that the users of financial accounting statements can know about the real financial health of the firm being analyzed. The true, objective and unbiased financial information help managers to plan and revise their business strategies, check financial health, policy formulation and planning process. The process of understanding how financial accounting information assists to make corporate decision-making process is a core element of this course. You are also expected to understand different management accounting functions and applications, diverse management accounting techniques, methods and tools in different business situations in order to solve challenging business problems. You will understand the relevance of the high-quality accounting information and their multiple interpretations in the business life, which ultimately optimize planning, strategies, risk management and business-decision making.
  You are further expected to understand that accounting is not isolation of overall business strategy of corporates. You will create and produce indicators describing the success of operations based on cause/consequence logic, as well as able to collect information pertaining to corporates needed for measurement. You will identify the economic requirements and the resource requirements related to personnel, necessary to achieve the abovementioned objectives. You will use budget information as a tool for examining and evaluating the results and analyze financial risks of a firm and manage them.

  Prerequisites and co-requisites

  Bachelor's degree

  Course contents

  Theoretical aspects of accounting, financial statement analysis, financial performance measurement, regulatory framework, corporate governance and ethical issues of accounting, financial ratio analysis, corporate valuation, balanced scorecard as a tool for strategic budgeting, economic value added (EVA), cost concepts, absorption costing, pricing, planning and budgeting, fundamental of financial management, capital investment appraisal, financial risk management, cash flow, forecasting, sensitivity analysis.

  Assessment criteria

  Assessment criteria - grade 1 and 2

  1 (Adequate) The theory of the subject matter is appropriate for the subject matter or work role, but it does not form an overall view. You describe and distinguish essential concepts. You outline a development project. You find a partial solution to the problem but are unable to illustrate the results. Matters are discussed in a list-like and summarising manner. Independent thinking is lacking. The reporting follows JAMK’s reporting instructions partially.
  
  2 (Satisfactory) You apply theory within the subject matter, limited from the perspective of the subject matter or work role. You have selected and defined the essential concepts. You design a development project. You find a partial solution to the problem. You illustrate the results. The examples demonstrate understanding and independent thinking, but the connections between theoretical knowledge and the examples are insufficient. The report mostly follows JAMK’s reporting instructions.

  Assessment criteria - grade 3 and 4

  3 (Good) You present theory that is relevant and delineated. You are able to follow and analyse the development and/or research in your field of expertise. You are able to analyse and structure theoretical information and concepts. You lead or plan a research, development or innovation initiative, applying new methods. You develop the operations of the organisation by finding alternative solutions to a problem. Theory and practice are combined through your thinking. The report is illustrative and includes analysis. The report follows JAMK’s reporting instructions (may contain occasional errors).
  
  4 (Very good) You produce an overall view from the essential theories in the subject matter, which you reflect on in a critical manner. You are able to follow and analyse the development and international research in your field of expertise. You are able to consider the essential concepts in relation to each other. You lead or plan a research, development or innovation initiative that produces a solution for a practical problem. You demonstrate and justify the usability of your results. The report is written in a consistent, justified and illustrative manner. It contains reflection and criticism. The report follows JAMK’s reporting instructions. There may be minor deficiencies with some details.

  Assessment criteria - grade 5

  5 (Excellent) You are able to create new information. You are able to follow and analyse the development and international research in the fields of expertise. You reflect on the matter at a social level. You are able to define the essential concepts related to the special competence of the field in relation to each other. You lead or plan a research, development or innovation initiative that produces and analyses new information and reforms the practices in complex and unpredictable operating environments. You utilise methods from research and development operations, and display creative thinking. The report has created new perspectives or overall views by combining things in a new way. The text displays the ability for critical thinking and the skill of communicating the results. The report follows JAMK’s reporting instructions.

  ---

  Language of instruction

  English

  Planned learning activities, teaching methods and guidance

  Lectures
  Literature
  Video material
  Exercises and assignments
  Guidance and feedback

  Lecturer(s)

  Aki Laaksonen

  Exam dates and re-exam possibilities

  The course does not include an exam.

  Timing

  01.08.2022 - 31.12.2022

  Learning assignments and student workload

  Allocation of time on various tasks and student work load in total 135h
  Lectures 14h
  Independent familiarization with literature and videos 40h
  Independent exercises and assignments 81h

  Groups

  • YTF22S1

  Alternative learning methods

  Accreditation (replacement and inclusion)
  Recognition of informal learning
  Studification
  
  Further instructions can be found in the Degree Regulations and in the Master's Degree study guide.

  Assessment methods

  The assessment of the course is based on the following credits:
  1) exercises and assignments (100%, 5 ECTS)

  Degree Programme

  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 Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security

  Mode of delivery

  Online learning

  Share of virtual studies

  5 cr

  Credits

  • 5 cr

  Unit

  School of Business
• Research-based Development (YZWZ0110-3017),
       09.01.2023 – 28.04.2023,  5 cr  (YTI22S1) — Face-to-face +-
  Learning outcomes of the course

  Purpose of the course
  The purpose of this course is to understand the meaning of an research-based development and to get own toolkit for development activities
  
  Competences
  Ethical competence
  Learning and information management competence
  Entrepreneurship, innovation and working community competence
  
  Learning outcomes
  After completing this course, you will understand the difference between scientific research and research-based development. You understand the principles of research-based development,, its methodological starting points. You know the process of research-based development. You master and are able to apply some working life development methods in development activities at your filed of practice. You understand the importance of research ethics and good scientific practice in research-based development.

  Course contents

  The starting points and methodology of research-based development
  Research ethics and the responsible conduct of research
  Process and management of research-based development
  Research and development methods, data collection and data analysis
  Assessment as part of research-based development

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1
  The student understands the differences and similarities between research-based development and academic research. The student is able to describe the different stages of the development process and choose methods suitable for their research/development topic. The student understands the significance of research ethics in research-based development.
  
  Satisfactory 2
  The student understands the differences and similarities between research-based development and academic research. The student describes the stages of the development process and chooses methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 3 and 4

  Good 3
  The student is able to compare the differences and similarities between research-based development and academic research. The student is able to analyse development processes, methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.
  
  Very good 4
  The student compares the similarities and differences between research-based development and academic research in a diverse way. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and reflects on and assesses their suitability critically and with the help of literature.The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 5

  Excellent 5
  The student is able to critically assess research-based development. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and critically assesses their suitability for development work in the field. The student understands the significance of research ethics in research-based development.

  Further information

  The course can be studied by using studification combining study & work in accordance with the
  JAMK Degree regulation 17 § the guidelines for identifying and recognizing competence and the JAMK processes.

  ---

  Language of instruction

  English

  Location and time

  Online implementation.
  Sessions in Tuula's Zoom.

  Planned learning activities, teaching methods and guidance

  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

  Learning materials and recommended literature

  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
  
  Responsible conduct of research and procedures for handling allegations of misconduct in Finland. 2012. Finnish National Board on Research Integrity. www.tenk.fi/en

  Lecturer(s)

  Tuula Kotikoski

  Working life cooperation

  If possible, also webinars with R&D experts from the working life

  Campus

  Lutakko Campus

  Timing

  09.01.2023 - 28.04.2023

  Learning assignments and student workload

  - 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.

  Content scheduling

  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.

  Groups

  • YTI22S1

  Alternative learning methods

  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.

  Seats

  0 - 35

  Assessment methods

  Avoin AMK 5 (not translated)

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology
• Artificial Intelligence (YTIP2100-3003),
       26.08.2022 – 16.12.2022,  5 cr  (YTI22S1) — Face-to-face, Online learning +-
  Learning outcomes of the course

  The student understands the purpose of artificial intelligence and knows the importance and possibilities of data. The student understands the importance of data quality and the ethics of artificial intelligence. The student will know the steps of data analytics/machine learning/deep learning processes. The student has knowledge and skills to plan a data strategy.
  
  Course competences
  EUYKN EUR-ACE: Knowledge and Understanding, Master's Degree
  
  EUYCT EUR-ACE: Communication and Team-working, Master's Degree
  
  EUYLL EUR-ACE: Lifelong Learning, Master's Degree

  Course contents

  The key topics of the course are:
  The possibilities of data
  Ethics of Artificial Intelligence (AI)
  The importance of data quality
  Methodology of Data analytics (DA) and AI
  DA and AI project process and management
  Data strategy
  Open source tools to DA, ML and AI

  Assessment criteria

  Assessment criteria - grade 1 and 2

  **Assessment criteria, sufficient 1, satisfactory 2
  Sufficient 1: Student has sufficient knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors at a sufficient level.
  
  Satisfactory 2: Student has satisfactory knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors at a satisfactory level.

  Assessment criteria - grade 3 and 4

  Good 3: Student has good knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a partially comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors well.
  
  Very Good 4: Student has very good knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a comprehensive data strategy and describe the DA/AI process. In addition, student understands the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors very well.

  Assessment criteria - grade 5

  Excellent 5: Student has excellent knowledge of data analytics and artificial intelligence and the possibilities of data. Student is able to form a comprehensive data strategy and describe the DA/AI process. In addition, student understands excellently the restrictions of GDPR legislation and regulations and the ethical aspects affecting the operations of actors.

  ---

  Language of instruction

  English

  Location and time

  Contact instruction over 2 weekends: on Fridays at 2pm - 8pm, and on Saturdays at 9am - 3pm.
  Location: IT-Dynamo, Piippukatu 2, 40100 Jyväskylä.

  Planned learning activities, teaching methods and guidance

  Contact instruction over 2 weekends during the semester.
  Exercises and distance learning between the contact sessions.
  Study project covering a topic based on individual needs.

  Learning materials and recommended literature

  Further defined during the first contact session and more can be found in the slide sets.
  
  Relevant parts of the following books:
  
  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).

  Lecturer(s)

  Tuomo Sipola

  Working life cooperation

  It is advisable that the students use their own work environment as an object of study and as a data source.
  Possible guest lecturers from companies.

  Campus

  Lutakko Campus

  Timing

  26.08.2022 - 16.12.2022

  Learning assignments and student workload

  5 ECTS credits equals about 135 hours of study work.
  Contact instruction: 24 hours
  Exercises: 48 hours
  Study project: 63 hours

  Groups

  • YTI22S1

  Seats

  0 - 35

  Assessment methods

  Feedback on exercises and study project.
  Self evaluation of study project.
  Peer evaluation of study project.
  Teacher's evaluation of study project.
  
  Open UAS 5

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face, Online learning

  Share of virtual studies

  4 cr

  Credits

  • 5 cr

  Unit

  School of Technology
• Data Analytics (YTIP2200-3003),
       09.01.2023 – 28.04.2023,  5 cr  (YTI22S1) — Face-to-face, Online learning +-
  Learning outcomes of the course

  The student understands the significance of data analytics in the digitalizing operational environment. The student knows the most commonly used methods and theories of data analytics as well as how to apply them in practice to existing data and interpret the results of the methods.
  
  Course competences
  EUYEN EUR-ACE: Engineering Analysis, Master's Degree
  EUYEE EUR-ACE: Engineering Design, Master's Degree
  EUYIV EUR-ACE: Investigations, Master's Degree

  Course contents

  - Python data analytics libraries: NumPy, Pandas, Matplotlib, Seaborn, Scipy
  - Data visualization
  - Processing of missing values and outliers
  - Statistical terms: Average, standard deviation, correlation coefficient and their interpretations
  - The concept of probability distribution, confidence interval and hypothesis testing.
  - Bernoulli and Poisson processes
  - Linear/logistic regression, decision trees

  Assessment criteria

  Assessment criteria - grade 1 and 2

  **Assessment criteria, sufficient 1, satisfactory 2
  Sufficient 1:
  
  The student knows about the most commonly used techniques in data analytics in data analysis tasks. He/she is able to apply the most common techniques to analysing data and has sufficient knowledge of the mathematics behind the techniques. Additionally, the student is able to assess his/her implementation briefly.
  
  Satisfactory 2:
  The student knows the most commonly used techniques in data analytics in data analysis tasks. He/she is able to select the techniques for analysing data and apply his/her technical know-how in practice. Student understands the mathematics behind the techniques at a satisfying level. Additionally, the student is able to assess his/her implementation superficially.

  Assessment criteria - grade 3 and 4

  Good 3:
  The student is aware of the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques of data analytics in various data analysis tasks. Student understands well the mathematics behind the techniques at a good level. He/she is able to validate and select the techniques in data analysis and apply his/her technical know-how in practice. Additionally, the student is able to assess his/her implementation and validate its development.
  
  Very good 4:
  The student recognizes the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques of data analytics and is able to extensively validate the use of implemented techniques in various data analysis tasks. Student understands the mathematics behind the techniques at a very good level. He/she is able to versatilely validate and select the correct techniques for the analysis of data and apply his/her technical know-how to practice. Additionally, the student is able to assess his/her implementation profoundly and validate its development.

  Assessment criteria - grade 5

  Excellent 5:
  The student recognizes the advantages of data analytics in the era of digitalization. The student knows the most commonly used techniques in data analytics and is able to critically validate the use of implemented techniques in various data analysis tasks. Student understands the mathematics behind the techniques in excellent level. He/she is able to critically validate and select the correct techniques in data analysis regardless of the data to be analyzed and apply the technical know-how to practice. Additionally, the student is able to critically assess his/her implementation and validate its development.

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  Language of instruction

  English

  Planned learning activities, teaching methods and guidance

  Online lectures (recorded) and exercise sessions.
  Campus meetings upon agreement.

  Learning materials and recommended literature

  Online material.

  Lecturer(s)

  Harri Varpanen

  Campus

  Lutakko Campus

  Exam dates and re-exam possibilities

  No exams.

  Timing

  09.01.2023 - 28.04.2023

  Content scheduling

  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).

  Groups

  • YTI22S1

  Alternative learning methods

  Uni. Helsinki: Data Analysis with Python (MOOC), course code CSM90004

  Seats

  0 - 35

  Assessment methods

  The grade is determined by the number of completed exercises.
  Basic knowledge of python is assumed.

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face, Online learning

  Share of virtual studies

  4 cr

  Credits

  • 5 cr

  Unit

  School of Technology
• Deep Learning and Neural Networks (YTIP2400-3003),
       29.08.2022 – 16.12.2022,  5 cr  (YTI21S1) — Face-to-face +-
  Learning outcomes of the course

  The student understands the significance of deep learning in the digitalizing operational environment and knows the type and structure of neural network.
  
  The student knows about the most common methods of deep learning and how to desing and apply them in practice to the data and interpret the results of the methods. In addition, the student undestands the mathematic behind the neural networks.
  
  Course competences:
  EUYIV EUR-ACE: Investigations, Master's Degree
  EUYER EUR-ACE: Engineering Practice, Master's Degree
  EUYMJ EUR-ACE: Making Judgements, Master's Degree

  Course contents

  - Structure of neural network vs artificial neural network
  - Different types of neural networks and their applications: CNN, RNN, LST, autocorrelation, autoencoder (CAN), NLP etc.
  - Mathematic behind the neural network
  - Feature engineering
  - Training process of neural network
  - Estimate and interpret the results
  - Transfer and Active Learning
  - MLOps

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1: Student has sufficient knowledge of deep learning and neural networks. Student is able to apply the most common techniques of deep learning and has the sufficient knowledge of mathematic behind the technigues. Additionally, the student is able to estimate and validate implementation briefly.
  
  Satisfactory 2: Student has satisfied knowledge of deep learning and neural networks. Student is able to choose suitable deep learning tehnique and apply the technical know-how in practise. Student undestands the mathematic behind the technigues in satisfying level. Additionally, the student is able to estimate and validate implementation superficially.

  Assessment criteria - grade 3 and 4

  Good 3: Student has good knowledge of deep learning and neural networks. Student is able to choose appropriate deep learning tehnique and apply the technical know-how in practise. Student undestands well the mathematic behind the technigues in good level. Additionally, the student is able to estimate and validate implementation well.
  
  Very good 4: Student has very good knowledge of deep learning and neural networks. Student is able to choose and justify versatilely deep learning tehnique and apply the technical know-how in practise. Student undestands the mathematic behind the technigues in very good level. Additionally, the student is able to estimate and validate implementation critically.

  Assessment criteria - grade 5

  Excellent 5: Student has excellent knowledge of deep learning and neural networks. Student is able to choose and justify deep learning tehnique and apply the technical know-how in practise. Student undestands the mathematic behind the technigues in excellent level. Additionally, the student is able to estimate and validate implementation critically.

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  Language of instruction

  English

  Location and time

  Mandatory contact days:
  Sat 10.9.2022 klo 9-15
  Fri 18.11.2022 klo 15-20

  Lecturer(s)

  Mika Rantonen

  Campus

  Lutakko Campus

  Timing

  29.08.2022 - 16.12.2022

  Groups

  • YTI21S1

  Seats

  0 - 35

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology
• Machine Learning (YTIP2300-3003),
       29.08.2022 – 16.12.2022,  5 cr  (YTI21S1) — Face-to-face +-
  Learning outcomes of the course

  The student understands the significance of machine learning in digitalizing operational environment. The student knows the most common machine learning methods, is able to apply them in practice to existing data and interpret the results of the methods. In addition, the student understands the mathematic behind the most commonly used machine learning algorithms.
  
  Course competences
  EUYEE EUR-ACE: Engineering Design, Master's Degree
  EUYIV EUR-ACE: Investigations, Master's Degree
  EUYER EUR-ACE: Engineering Practice, Master's Degree

  Course contents

  - Mathematics behind the machine learning algorithms
  - Most common regression and classification models of supervised machine learning and their application in Python programming environment using NumPy, Pandas and Scikit-learn libraries.
  - Choosing a suitable machine learning algorithm model and estimation of results
  - Support Vector Machine
  - Clustering
  - K-nearest neighbor
  - Reinforcement learning

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1: Student has sufficient knowledge of machine learning algorithms and neural networks. Student is able to apply the most common techniques of machine learning and has the sufficient knowledge of the mathematics behind the techniques. Additionally, the student is able to estimate and validate implementation briefly.
  
  Satisfactory 2: Student has satisfactory knowledge of machine learning and neural networks. Student is able to choose a suitable machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at a satisfactory level. Additionally, the student is able to estimate and validate implementation superficially.

  Assessment criteria - grade 3 and 4

  Good 3: Student has good knowledge of machine learning algorithms and neural networks. Student is able to choose an appropriate machine learning technique and apply the technical know-how in practice. Student understands well the mathematics behind the techniques at a good level. Additionally, the student is able to estimate and validate implementation well.
  
  Very good 4: Student has very good knowledge of machine learning algorithms and neural networks. Student is able to choose and justify versatilely a machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at a very good level. Additionally, the student is able to estimate and validate implementation critically.

  Assessment criteria - grade 5

  Excellent 5: Student has excellent knowledge of machine learning algorithms and neural networks. Student is able to choose and justify machine learning technique and apply the technical know-how in practice. Student understands the mathematics behind the techniques at an excellent level. Additionally, the student is able to estimate and validate implementation critically.

  ---

  Language of instruction

  English

  Planned learning activities, teaching methods and guidance

  Virtual study, contact study.

  Learning materials and recommended literature

  Lecture notes, exercises, video examples
  Muller, Guido: Introduction to Machine Learning with Python

  Lecturer(s)

  Tomi Nieminen

  Campus

  Lutakko Campus

  Timing

  29.08.2022 - 16.12.2022

  Learning assignments and student workload

  Virtual study 110 h
  Contact study 15 h

  Groups

  • YTI21S1

  Seats

  0 - 35

  Degree Programme

  Master's Degree Programme in Artificial Intelligence and Data Analytics

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Technology
• Research-based Development (YZWZ0110-3022),
       29.08.2022 – 21.12.2022,  5 cr  (ZJAYSP22S1, ...) — Face-to-face +-
  Learning outcomes of the course

  Purpose of the course
  The purpose of this course is to understand the meaning of an research-based development and to get own toolkit for development activities
  
  Competences
  Ethical competence
  Learning and information management competence
  Entrepreneurship, innovation and working community competence
  
  Learning outcomes
  After completing this course, you will understand the difference between scientific research and research-based development. You understand the principles of research-based development,, its methodological starting points. You know the process of research-based development. You master and are able to apply some working life development methods in development activities at your filed of practice. You understand the importance of research ethics and good scientific practice in research-based development.

  Course contents

  The starting points and methodology of research-based development
  Research ethics and the responsible conduct of research
  Process and management of research-based development
  Research and development methods, data collection and data analysis
  Assessment as part of research-based development

  Assessment criteria

  Assessment criteria - grade 1 and 2

  Sufficient 1
  The student understands the differences and similarities between research-based development and academic research. The student is able to describe the different stages of the development process and choose methods suitable for their research/development topic. The student understands the significance of research ethics in research-based development.
  
  Satisfactory 2
  The student understands the differences and similarities between research-based development and academic research. The student describes the stages of the development process and chooses methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 3 and 4

  Good 3
  The student is able to compare the differences and similarities between research-based development and academic research. The student is able to analyse development processes, methods suitable for their development topic, and reflects on and assesses their suitability with the help of literature. The student understands the significance of research ethics in research-based development.
  
  Very good 4
  The student compares the similarities and differences between research-based development and academic research in a diverse way. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and reflects on and assesses their suitability critically and with the help of literature.The student understands the significance of research ethics in research-based development.

  Assessment criteria - grade 5

  Excellent 5
  The student is able to critically assess research-based development. The student describes the stages of the development process with justifications, applies methods suitable for their development topic, and critically assesses their suitability for development work in the field. The student understands the significance of research ethics in research-based development.

  Further information

  The course can be studied by using studification combining study & work in accordance with the
  JAMK Degree regulation 17 § the guidelines for identifying and recognizing competence and the JAMK processes.

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  Language of instruction

  English

  Planned learning activities, teaching methods and guidance

  Individual learning tasks
  Small group work online
  Webinars
  Online Discussions
  Recorded online lectures, podcasts

  Learning materials and recommended literature

  Dodd, S-J. & Epstein, I. 2012. Practice-based research in social work: a guide for reluctant researchers. London; New York: Routledge.
  OR
  Bryman, A. 2012. Social research methods. Oxford: Oxford University Press cop.
  OR
  Burns, N. & Grove, S. K. 2001. The practice of nursing research. Conduct, Critique, and Utilization. 5th edition or the latest. Philadelphia : Elsevier/Saunders cop.
  OR
  Polit, D. F. & Beck, C.T. 2014. Essentials of nursing research: appraising evidence for nursing practice. Philadelphia : Wolters Kluwer Health/Lippincott Williams & Wilkins cop.
  OR
  Yin, R. K. 2018. Case study research and applications : design and methods. Los Angeles: Sage.

  Lecturer(s)

  Ben Waller, Kaisa Lällä

  Timing

  29.08.2022 - 21.12.2022

  Learning assignments and student workload

  webinars 16 h
  - learning tasks 80 h
  - studying the course material 40 h
  Total 135 hours.

  Enrollment

  01.08.2022 - 04.09.2022

  Groups

  • ZJAYSP22S1
  • ZJAYSD22S1
  • YSD22S1
  • YSP22S1

  Seats

  0 - 20

  Degree Programme

  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 Professional Project Management, Master's Degree Programme in International Business Management, Master's Degree Programme in Information Technology, Cyber Security

  Mode of delivery

  Face-to-face

  Credits

  • 5 cr

  Unit

  School of Health and Social Studies