Master Award in
Artificial Intelligence and Sustainability
Master Award could transfer 20 credits and full tuition fees to Master’s programs by SIMI and University Partners.
Master Award in Artificial Intelligence and Sustainability
This unit explores how AI supports sustainability and the UN Sustainable Development Goals (UNSDGs). Learners will study AI’s role in resource efficiency, environmental impact reduction, and sustainable development, covering concepts like energy efficiency, bias mitigation, trustworthy AI, and applications in clean energy, waste management, and smart manufacturing.
Could transfer 20 credits and full tuition fee to the Master of Artificial Intelligence of SIMI Swiss and University Partners.
Learning Outcomes:
1. Understand the role of AI in promoting sustainability
- 1.1. Describe the potential of AI in supporting the UNSDGs.
- 1.2. Explain the applications of AI in resource management.
- 1.3. Analyse the impact of AI on reducing environmental footprints.
- 1.4. Evaluate the opportunities and challenges of using AI for sustainability.
- 1.5. Assess the future potential of AI in supporting global sustainability efforts.
2. Be able to develop sustainable AI solutions
- 2.1. Develop AI models with a focus on energy efficiency.
- 2.2. Explain the importance of data efficiency in AI.
- 2.3. Critically analyse methods for identifying and mitigating bias in AI systems.
- 2.4. Evaluate the role of trustworthy AI in sustainable development.
- 2.5. Create a sustainable AI application for a specific industry or sector.
3. Understand the ethical implications of AI in sustainability
- 3.1 Describe the ethical challenges associated with AI deployment in sustainability initiatives.
- 3.2 Explain the importance of ethical AI governance in sustainability.
- 3.3 Critically analyse the societal impacts of AI-driven sustainability initiatives.
- 3.4 Evaluate strategies for ensuring ethical AI in sustainable development.
- 3.5 Develop recommendations for ethical AI use in sustainability projects.
4. Understand the application of AI in achieving specific UNSDGs
- 4.1 Describe the use of AI in clean energy initiatives.
- 4.2 Explain how AI can contribute to sustainable agriculture.
- 4.3 Analyse the role of AI in improving water management.
- 4.4 Critically evaluate the impact of AI on waste management and circular economy.
- 4.5 Critique the potential of AI in urba sustainability and smart cities.
Topics:
AI for Environmental Impact Reduction
Course Coverage:
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Overview of AI and Sustainability
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Definition and Scope of AI in Sustainability
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The Role of AI in Achieving the UNSDGs
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AI’s Potential in Driving Sustainable Development.
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AI in Environmental Monitoring and Protection
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AI in Sustainable Urban Development
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Challenges and Opportunities
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Opportunities for AI in Sustainability
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Challenges in Implementing AI for Sustainable Practices
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AI for Resource Efficiency
Course Coverage:
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Applications of AI in Resource Management
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AI in Energy Management: Smart Grids and Energy Efficiency
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AI in Water Resource Management: Monitoring and Optimization
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Enhancing Resource Efficiency with AI
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AI in Waste Management and Recycling
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AI in Agriculture: Precision Farming and Resource Optimization
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AI in Supporting Global Sustainability Efforts
Course Coverage:
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Future Potential of AI in Sustainability
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Emerging AI Technologies for Sustainability
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The Role of AI in Global Sustainability Initiatives
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Ethical Considerations and Sustainability
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Balancing AI Innovation with Ethical Responsibilities
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Ensuring Fair and Equitable AI Deployment
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Developing Energy-Efficient AI
Course Coverage:
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Techniques for Reducing Energy Consumption in AI
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Model Pruning and Optimization
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Hardware Solutions for Energy Efficiency
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Practical Applications
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Case Study: Energy-Efficient AI Models in Industry
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Implementing Energy-Efficient Algorithms in Practice
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Data-Efficient AI Techniques
Course Coverage:
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Approaches to Data Efficiency
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Data Augmentation and Transfer Learning
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Synthetic Data Generation and Its Applications
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Case Studies
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Examples of Data-Efficient AI in Real -World Applications
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Reducing Data Requirements in AI Model Training
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Bias Mitigation in AI Systems
Course Coverage:
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Identifying Bias in AI Algorithms
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Types of Bias: Data, Algorithmic, and Societal
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Tools for Detecting Bias in AI
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Mitigating Bias and Ensuring Fairness
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Techniques for Bias Mitigation: Re-Sampling, Re-Weighting
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Implementing Fairness-Aware AI Systems
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Trustworthy AI for Sustainable Development
Course Coverage:
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Principles of Trustworthy AI
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Transparency, Explainability, and Accountability in AI
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Ethical AI Frameworks and Guidelines
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Practical Implementation
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Building Trustworthy AI Solutions for Sustainable Industries
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Case Study: Trustworthy AI in Healthcare and Environment
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Developing Sustainable AI Applications
Course Coverage:
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Industry-Specific AI Applications
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AI in Agriculture: Precision Farming and Sustainable Practices
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AI in Energy: Renewable Energy Management
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Project Development
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Designing and Implementing a Sustainable AI Project
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Evaluating the Impact of AI Solutions on Sustainability Goals
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Ethics in AI for Sustainability
Course Coverage:
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Key Ethical Challenges
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Addressing Bias and Discrimination in AI
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Ethical Considerations in Data Privacy and Security
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Balancing Ethics and Innovation
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The Role of Ethics in AI Innovation
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Case Study: Ethical Dilemmas in AI Deployment
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AI Governance in Sustainability
Course Coverage:
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Importance of AI Governance
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Establishing Governance Frameworks for AI
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International Guidelines and Ethical Standards
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Practical Applications
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Implementing Governance Frameworks in AI Projects
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Ensuring Compliance with Ethical Standards
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Societal Impact of AI-Driven Sustainability Initiatives
Course Coverage:
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Social Implications of AI in Sustainability
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AI and Its Impact on Employment and Economy
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AI in Public Policy and Its Influence on Society
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Strategies for Minimizing Negative Impacts
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Promoting Inclusive AI Practices
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Engaging Communities in AIDriven Sustainability Projects
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Developing Ethical AI for Sustainability
Course Coverage:
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Best Practices for Ethical AI Development
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Embedding Ethical Principles in AI Design
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Stakeholder Engagement and Ethical Audits
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Policy Recommendations for Ethical AI
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Creating Ethical Guidelines for AI in Sustainability
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Case Study: Successful Implementation of Ethical AI
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AI in Clean Energy
Course Coverage:
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AI for Renewable Energy Optimization
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AI in Solar and Wind Energy Management
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Smart Grids and AI-Driven Energy Storage Solutions
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AI in Energy Efficiency
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Reducing Energy Consumption in Industries
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AI for Energy Demand Forecasting and Management
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AI in Sustainable Agriculture
Course Coverage:
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Precision Farming with AI
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AI in Crop Monitoring and Yield Optimization
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Sustainable Resource Management in Agriculture
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Reducing Environmental Impact with AI
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AI for Soil Health Monitoring and Conservation
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AI in Sustainable Pest and Disease Management
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AI in Water Management
Course Coverage:
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AI for Water Quality Monitoring
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Real-Time Monitoring and Prediction of Water Quality
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AI in Managing Water Resources and Distribution
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Sustainable Irrigation with AI
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Optimizing Irrigation Systems with AI
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AI in Reducing Water Waste in Agriculture
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AI in Waste Management and Circular Economy
Course Coverage:
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AI-Driven Waste Sorting and Recycling
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Automated Waste Sorting Systems
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Enhancing Recycling Efficiency with AI
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Promoting Circular Economy with AI
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AI in Resource Recovery and Reuse
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Sustainable Product Design and Lifecycle Management
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AI in Urban Sustainability and Smart Cities
Course Coverage:
AI for Smart Urban Planning
AI in Traffic Management and Urban Mobility
AI for Sustainable Infrastructure Development
AI in Enhancing Urban Quality of Life
AI in Public Health and Safety Monitoring
Smart City Initiatives: Energy, Waste and Water Management
Indicative reading list
Core texts:
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Floridi, L. (2014). The Ethics of Information. Oxford University Press
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Goodfellow, I., Bengio, Y., & Courville, A (2016). Deep Learning. MIT Press.
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Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
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Rolnick, D., Donti, P. L., Kaack, L. H., et al. (2019). Tackling Climate Change with Machine Learning. arXiv:1906.05433
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Chollet, F. (2018). Deep Learning with Python. Manning Publications.
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Marr, B. (2020). Artificial Intelligence in Practice: How 50 Successful Companies Used AI and Machine Learning to Solve Problems. Wiley.
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Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
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Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
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Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
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Floridi, L. (2019). The Logic of Information: A Theory of Philosophy as Conceptual Design. Oxford University Press.
Additional reading:
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United Nations Sustainable Development Goals (UNSDGs):www.un.org/sustainabledevelopment
Entry requirements
To enroll The Master Award, the learner must possess:
- Graduated with a Bachelor’s degree from an accredited university or achieved a Level 6 Diploma according to the European Qualifications
- For a degree from non-recognized universities; The learner should have followed Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) policy of SIMI and/or University Partners.
- Learners must be over 21 years old.
The SIMI Swiss reserves the highest decision-making power for admission whether to accept or not accept after a specific review of each candidate’s profile to ensure that they can comprehend and gain benefits when participating. For the fake university or diploma mills, University Partners shall not be accepted.
English requirements
If a learner is not from a predominantly English-speaking country, proof of English language proficiency must be provided.
- Common European Framework of Reference (CEFR) level B2 or equivalent
- Or A minimum TOEFL score of 101 or IELTS 6.5; Reading and Writing must be at 6.5 or equivalent
After graduating with Master Award, students receive all certified documents from the SIMI Swiss.
Certified Documents:
- e-Certificate from the Swiss Information and Management Institute (SIMI Swiss).
- Hard copy certificate from the Swiss Information and Management Institute (SIMI Swiss) – Optional.
- Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) certified from SIMI Swiss for credit and tuition fee transfer.
Because the program is accredited and recognized, students can easily use certified in the working environment and have many opportunities for career advancement. In addition, in case if you want to study for a SIMI degree or university partner degree, students can convert all credits and the full paid tuition fee when participating in the program University Partners.
The SIMI Swiss’ Master Award means:
SIMI Swiss Master Award is the award at the master level and is equivalent to:
- Level 7 certificate of Regulated Qualification Framework (RQF) of UK
- Level 10 certificate of Scottish Credit and Qualifications Framework (SCQF)
- Level 7 certificate of Credit and Qualifications Framework (CQFW)
- Level 7 certificate of European Qualifications Framework (EQF)
- Level 9 certificates of the Australian Qualifications Framework (AQF)
- Level 7 certificate of ASEAN Qualifications Reference Framework (AQRF)
- Level 9 certificate of the African Continental Qualifications Framework (ACQF)
Students can convert all credits and the full tuition fee when participating in the SIMI Swiss and/or University Partners academic programs if they want to study for an academic degree.
Credits transfer:
Learners can accumulate 20 credits from the Master Award program when participating in the Master of Artificial Intelligence. Please see the credit transfer policy HERE
Tuition fee transfer:
When participating in the Master of Artificial Intelligence program, students who have graduated 1 Master Award will receive a discount of full tuition fee which you paid. Please see the tuition fee transfer HERE
The SIMI Swiss micro-credential program allows for the transfer of credits and tuition fees into full degree programs from SIMI Swiss and/or its university partners. SIMI Swiss reserves the right to limit admissions once the number of students exceeds the quotas.
Apply Policy:
- To participate in the SIMI Swiss micro-credential program, students need to meet the entry criteria corresponding to each level. Please see the “Entry” tab for more details.
- SIMI Swiss will not accept applicants if their entry qualifications are from diploma mill universities or schools/universities that are not accredited.
- For Master Award programs, if an entry bachelor is unavailable, students must demonstrate a minimum of 5 years of work experience in the relevant field. Please note that a bachelor’s degree is required for the Master’s program at SIMI Swiss and University Partners so that you could study Master Award but could not move to the Master’s program of SIMI and University Partners.
- English is not a mandatory entry requirement for short course programs, but candidates need to ensure that English is used in reading documents, listening to lectures, and doing assignments. Candidates should note that English is a mandatory requirement when switching to an academic program at SIMI Swiss and University Partners.
Apply Process:
- Choose the program that suits your requirements. Note that applicants without a university degree will not be able to participate in the program at Master’s level, and applicants without a Master’s degree will not be able to participate in the program at the Doctoral level.
- Email your application to support@simiswiss.ch with all the required documents. You could download the application form here.
- Our admission department will contact you and guide you through further processes if the registration documents need to be supplemented.
- SIMI Swiss will issue the Letter of Acceptant (LOA). You wil proceed to the next steps according to the instructions and pay tuition fee.
- SIMI Swiss will issue a student confirmation letter, login account to the e-learning system and related documents.
- You have become an official SIMI Swiss student and enjoy your study journey.
The SIMI Swiss micro-credential program is fully online, allowing you to study anytime, anywhere. You have the option to attend live classes with SIMI Swiss. The final exam will be uploaded to the system and evaluated by the academic panel of SIMI Swiss. Students must submit assignments on time; failure to do so will result in the student being considered to have discontinued the program.
Pricing Plans
Take advantage of one of our non-profit professional certified programs with favorable terms for your personal growing carreers.
- Live Class (Option)
- Full online videos
- e-Books
- Self study contents
- Online tutor videos
- Assignment guide
- e-Certificate
- Hard copy certificate
- Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) certified from University Partners for credit and tuition fee transfer
- Accreditation & Recognition certified from University Partners
- Deliver hard copy certificate and all certified documents to your home
- Transfer full credits & tuition fees to equivalent academic programs
- Get more support tuition fees and scholarships when becoming University Partners' international students
- (*) In the event that you receive a scholarship or discount, the fee you should transfer is the amount you actually paid.
SWISS MICRO CREDENTIAL
Contact us
If you interested this micro credential course, please feel free to contact with us! Please note that this program is a not for profit and learning with full online model.