Diploma in Artificial Intelligence for Business offered by Canadian College of Excellence

Diploma in Artificial Intelligence for Business 
 (12 Month, 52 Weeks) 100% Remote Program

Section 1: Program Overview

1.1 Introduction

The "Artificial Intelligence for Business" online diploma program is meticulously crafted to provide students with the essential knowledge and skills required to leverage artificial intelligence (AI) technologies in modern business environments. This program explores the intersection of AI and business, covering fundamental concepts such as machine learning, natural language processing, and neural networks, while also delving into practical applications across various industries. By the culmination of this program, students will emerge equipped with the expertise to harness AI tools and techniques to drive innovation, optimize processes, and unlock strategic insights, thereby gaining a competitive edge in today's data-driven business landscape.

Goals:

Equip students with foundational knowledge: Ensure students have a solid understanding of the principles, algorithms, and technologies underlying AI.

Foster practical skills: Provide hands-on experience with AI tools, frameworks, and techniques through practical projects and real-world applications.

Promote critical thinking: Encourage students to critically analyze AI applications, ethical implications, and societal impacts.

Prepare for industry demands: Align curriculum with industry requirements to prepare students for fulfilling careers in AI-related fields.

Encourage innovation: Stimulate creativity and innovation in AI by providing opportunities for research and development.

Objectives:

Understanding AI Fundamentals: Demonstrate comprehension of core concepts such as machine learning, neural networks, natural language processing, and computer vision. Explain the mathematical and statistical foundations of AI algorithms.

Applied Skills Development: Develop proficiency in programming languages commonly used in AI, such as Python, gain hands-on experience with AI frameworks and tools like TensorFlow, PyTorch, and scikit-learn. Apply AI techniques to solve real-world problems in domains like healthcare, finance, and cybersecurity.

Ethical Awareness: Analyze ethical issues surrounding AI, including bias, privacy concerns, and job displacement. Evaluate the societal impacts of AI technologies and propose responsible solutions.

 

Industry Readiness: Acquire practical skills valued by employers, including data preprocessing, feature engineering, model evaluation, and deployment. Collaborate on team projects to simulate real-world work environments and enhance teamwork and communication skills. Engage with industry professionals through guest lectures, internships, and networking events.

Innovation and Research: Conduct independent research or capstone projects exploring cutting-edge AI topics. Encourage participation in AI competitions, hackathons, and conferences to showcase skills and foster innovation.

Intended Learning Outcomes: By the end of the program, students should be able to:

• Develop AI models for various tasks such as classification, regression, clustering, and reinforcement learning.
• Evaluate the performance of AI models using appropriate metrics and validation techniques.
• Implement preprocessing techniques to clean, transform, and feature engineer diverse datasets.
• Demonstrate proficiency in using AI libraries, frameworks, and development environments.
• Critically assess the ethical implications of AI applications and propose strategies for responsible AI development and deployment.
• Collaborate effectively in multidisciplinary teams to solve complex problems using AI techniques.
• Communicate technical concepts and findings clearly and concisely to both technical and non-technical audiences.
• Stay updated with the latest trends and advancements in AI by continuous learning and professional development.

By aligning with these goals, objectives, and intended learning outcomes, the AI diploma program can provide Dubai students with a comprehensive education that prepares them for success in the rapidly evolving field of artificial intelligence.

1.2 Program Structure and Curriculum

The program consists of a comprehensive curriculum structured into 12 courses, that includes 40 credits for the program length of 52 weeks focusing on specific aspects of AI and its applications in business settings. From understanding the fundamentals of AI algorithms to exploring advanced topics such as deep learning and predictive analytics, the curriculum is designed to provide students with a well-rounded understanding of AI technologies and their implications for business operations and decision-making.

1.3 Admission Requirements

Admission to the Diploma in Artificial Intelligence for Business program is subject to rigid criteria aimed at selecting candidates with the aptitude and academic background necessary to succeed in the program. Prospective students are required to meet minimum academic qualifications and may undergo additional assessments or interviews to evaluate their readiness for the program. This rigorous selection process ensures that students admitted to the program possess the foundation to excel in the challenging and dynamic field of AI for business.

Educational Qualifications:

Applicants should have completed secondary education or its equivalent, such as a high school diploma or an international baccalaureate (IB) diploma.

A strong background in mathematics, science, and computer science may be advantageous for an AI diploma program.

English is the primary language of instruction in Canadian College of Excellence, Dubai, applicants may need to demonstrate proficiency in English through standardized tests such as the TOEFL or IELTS if the English score in high school diploma or its equivalence is below 60%. 

Interview: CCE, Dubai interviews all eligible applicants, before their provincial letter of admission, test applicant academic readiness, academic and technical proficiency, Interest, and passion in the program-subject chosen, communication skills, problem solving and critical thinking skills and ability to collaborate with peer group and Faculty to successfully complete their academic program. 

Section 2: Program Evaluation

2.1 Learning Outcomes

Upon completion of the Diploma in Artificial Intelligence for Business program, students will:

• Demonstrate a comprehensive understanding of fundamental AI concepts and techniques, including machine learning algorithms, neural networks, and natural language processing.
• Apply AI tools and methodologies to analyze complex datasets, extract actionable insights, and make data-driven decisions in a business context.
• Develop and deploy AI-based solutions to address specific business challenges, such as customer segmentation, predictive modeling, and process automation.
• Evaluate the ethical and societal implications of AI technologies and implement responsible AI practices in business settings.
• Collaborate effectively with multidisciplinary teams to integrate AI solutions into existing business processes and drive innovation across various functional areas.
• Communicate technical concepts and findings related to AI effectively to diverse stakeholders, including non-technical audiences, to facilitate informed decision-making and organizational buy-in.

 

 

 

2.2 Curriculum Mapping

Course	Weekly Coverage	Reference Books
Overview of AI Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: What Is Intelligence?  Topic 2: Testing Machine Intelligence  Week 2:  Topic 3: Strong and Weak Artificial Intelligence  Topic 4: Artificial Intelligence Planning  Week 3:  Practical Applications of Machine Learning  Week 4:  Topic 5: Introduction to Big Data	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th, 2020) - Copyright © 2021
AI – Applications Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Expert System Versus Machine Learning  Topic 2: Supervised Versus Unsupervised Learning  Week 2:  Topic 3: Backpropagation of Errors  Topic 4: Regression Analysis  Week 3:  Topic 5: Intelligent Robots  Topic 6: Natural Language Processing  Week 4:  Topic 7: The Internet of Things	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Machine Learning Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Understanding the Concept of Big Data  Topic 2: Machine Learning and Data Mining  Week 2:  Topic 3: Making the Leap to Machine Learning  Topic 4: How a Machine Learns  Week 3:  Topic 5: Working with Data  Topic 6: Applying Machine Learning  Week 4:  Topic 7: Different Types of Learning	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Machine Learning Algorithms Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Supervised, Unsupervised, Semi-Supervised Learning  Topic 2: Reinforcement Learning  Week 2:  Topic 3: Decision Trees  Topic 4: k-Nearest Neighbor  Week 3:   Topic 5: k-Means Clustering   Topic 6: Regression Analysis   Week 4:   Topic 7: Naive Bayes	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Applications of AI Algorithms Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Fitting the Model to Your Data  Topic 2: Choosing Algorithms  Week 2:  Topic 3: Ensemble Modeling  Week 3:  Topic 4: Deciding on a Machine Learning Approach  Week 4:  Topic 5: Best Practices and Tips for AI Applications	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Artificial Neural Networks Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Why the Brain Analogy?  Topic 2: Understanding Perceptron  Week 2:  Topic 3: Sigmoid Neuron  Week 3:  Topic 4: Adding Bias  Week 4:  Topic 5: Overview of Algorithms	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Artificial Neural Networks in Action Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Feeding Data into the Network  Topic 2: Hidden Layers  Topic 3: Activation Functions  Week 2:  Topic 4: Adding Weights  Topic 5: Starting with Random Weights and Biases  Week 3:  Topic 6: Cost Function and Gradient Descent  Topic 7: Backpropagation and Error Correction  Week 4:  Topic 8: Tuning the Network  Topic 9: Employing the Chain Rule  Topic 10: Batching the Data Set with Stochastic Gradient Descent	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Neural Networks and Data Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Solving Classification Problems  Topic 2: Solving Clustering Problems  Week 2:  Topic 3: Obtaining Quality Data  Week 3:  Topic 4: Keeping Training and Test Data Separate  Week 4:  Topic 5: Choosing the Right Tool for the Job	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Power of Natural Language Processing Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Extracting Meaning from Text and Speech with NLU  Topic 2: Delivering Sensible Responses with NLG  Week 2:  Topic 3: Automating Customer Service  Week 3:  Topic 4: Review of NLP Tools and Resources  Week 4:  Topic 5: NLU and NLG Tools	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Customer Interactions and Decision-Making Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Choosing Natural Language Technologies  Topic 2: Review of Top Tools for Chatbots and Virtual Agents  Week 2:  Topic 3: Automated vs. Intuitive Decision-Making  Week 3:  Topic 4: Real-time Data Gathering from IoT Devices  Week 4:  Topic 5: Review of Automated Decision-Making Tools	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Predict Events and Outcomes using AI Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Machine Learning Applications  Topic 2: Predictive Analytics  Week 2:  Topic 3: Answering Questions and Making Decisions  Topic 4: Replicating Expertise in Your Business  Week 3:  Topic 5: Ethics in Machine Learning  Week 4:  Topic 6: Review of Top Machine Learning Tools	Artificial Intelligence for Business, 2nd edition Rose | Published by Pearson FT Press (December 9th 2020) - Copyright © 2021
Building Artificial Minds Hours: 80 Weeks: 4 Credits: 3	Week 1:  Topic 1: Separating Intelligence from Automation  Topic 2: Adding Layers for Deep Learning  Week 2:  Topic 3: Applications for Artificial Neural Networks  Topic 4: Customer Classification and Recommendations  Week 3:  Topic 5: Store Layout Optimization  Topic 6: Analyzing and Tracking Biometrics  Week 4:  Topic 7: Review of Top Deep Learning Tools

 

 

 

2.3 Teaching and Learning Strategies:

 

Instructional methods and strategies include:

• Lectures and presentations for theoretical knowledge.
• Practical exercises, role-playing, and case studies for active learning.
• Hands-on practice sessions for skill development.
• Group discussions and interactive sessions for critical thinking and application of knowledge.
• Use of simulation models, diagrams, and technology to enhance learning experience.
• Guest speakers and field visits to provide real-world insights into healthcare practices.

 

2.4 Assessment Methods:

 

Assessment methods include:

• Formative assessments such as quizzes, assignments, and practical exercises throughout each module.
• Summative assessments at the end of each module to evaluate overall understanding and proficiency.
• Practical assessments to evaluate patient care skills, communication abilities, and cultural competence.
• Case studies and projects to assess critical thinking and application of knowledge.
• External assessments or certifications to validate competence in specific areas, if applicable.
• Regular review and updating of the curriculum are conducted through feedback from students, faculty, industry stakeholders, and ongoing monitoring of industry trends and best practices. Adjustments are made to ensure alignment with program goals and industry needs.

 

Section 3: Faculty and Resources

3.1 Faculty Qualifications

The faculty members overseeing the Diploma in Artificial Intelligence for Business program possess a blend of academic expertise and industry experience essential for delivering high-quality instruction and mentorship. They hold advanced degrees in relevant fields such as computer science, data science, and artificial intelligence, supplemented by industry certifications and professional accolades. Additionally, faculty members actively engage in research and professional development activities to stay abreast of the latest advancements in AI technologies and their applications in business contexts. Their diverse backgrounds and practical insights enrich the learning experience, providing students with valuable perspectives and real-world insights into the intersection of AI and business. The resume detailing the educational qualifications, professional work experience, relevant research and thesis work, laboratory experience are included along with copies of certificates for the following faculty:

 

• Afrin Sadia Rumana
• Hafez Md. Nasrullah
• Dr. Md. Morshedul Islam
• Mahudun Nabi
• Md. Ashraful Islam
• McVern Gall

3.2 Academic Resources

The program benefits from a rich array of academic resources designed to support student learning and research endeavors. State-of-the-art computing facilities equipped with AI software and tools provide students with hands-on experience in developing and deploying AI solutions. Access to digital libraries, online journals, and research databases offers students a wealth of scholarly resources to deepen their understanding of AI concepts and explore emerging trends in the field. Moreover, collaborative spaces and project rooms foster teamwork and innovation, enabling students to collaborate on AI projects and experiential learning opportunities. The specific academic resources including eLearning/ digital content offered to students are:

• D2L- Brightspace -Learning Management System 
• McGraw Hill Connect
• Pearson Higher Education Online Resource
• eBookshelf-Vitalsource

3.3 Student Support Services

We are committed to providing comprehensive support services to ensure the success and well-being of our students throughout their academic journey. Our dedicated student support team offers personalized guidance and assistance with academic advising, course selection, and career planning. Additionally, tutoring services, workshops, and study groups are available to help students overcome academic challenges and enhance their learning outcomes. Furthermore, networking events, guest lectures, and industry partnerships facilitate connections with professionals and organizations in the AI ecosystem, providing students with valuable networking opportunities and access to internships and job placements.

Online Learning Platforms:

• Access to online learning platforms or learning management systems (LMS) where students can find course materials, lecture notes, assignments, and supplementary resources.
• Integration with video lecture platforms for asynchronous learning and revision.

Faculty Support:

• Availability of experienced and knowledgeable faculty members who provide guidance, mentorship, and support to students both in and out of the classroom.
• Office hours and consultation sessions for students to seek help with course material, assignments, and research projects.

 

Workshops and Seminars:

• Regular workshops, seminars, and guest lectures conducted by faculty members, industry professionals, and researchers to expose students to current trends, best practices, and real-world applications of AI.
• Opportunities for students to present their own research findings and projects to their peers and faculty members.

Research Opportunities:

• Access to research facilities and resources for conducting independent or collaborative research projects in AI and related disciplines.
• Support for participation in research conferences, symposiums, and competitions to showcase research outcomes and network with peers and experts in the field.

Career Services:

• Career counseling and guidance services to help students explore career paths, develop professional skills, and prepare for job interviews and internships in AI-related industries.
• Job placement assistance and connections with industry partners for internship and employment opportunities.

Collaborative Spaces:

• Collaborative spaces such as study rooms, group work areas, and project labs where students can collaborate on assignments, projects, and research activities.
• Opportunities for interdisciplinary collaboration with students and faculty from other departments or programs.

 

Section 4: Continuous Improvement

4.1 Program Evaluation and Feedback Mechanism

Continuous evaluation and feedback mechanisms are integral to our commitment to maintaining the quality and relevance of the "Artificial Intelligence for Business" program. We regularly solicit feedback from students, faculty, alumni, and industry partners through surveys, focus groups, and advisory committees. This feedback is carefully analyzed to identify areas for improvement and inform curriculum updates, instructional methodologies, and program enhancements. Additionally, ongoing assessment of student learning outcomes and program effectiveness allows us to monitor progress and adapt our strategies to meet the evolving needs of learners and the industry.

Student Feedback:

• Regular course evaluations: Students are provided with opportunities to evaluate each course they undertake, typically at the end of each semester. These evaluations cover aspects such as course content, teaching quality, learning resources, and overall satisfaction.
• Mid-term feedback sessions: Mid-way through the semester, feedback sessions may be conducted to gather input from students regarding their learning experience, challenges faced, and suggestions for improvement.
• Student representatives: Each cohort may elect student representatives who serve as liaisons between students and faculty/administration, providing a channel for ongoing feedback and communication. 

Faculty Feedback:

• Peer evaluations: Faculty members may participate in peer evaluation processes where they provide feedback on each other's teaching methods, course materials, and assessment strategies.
• Faculty meetings and forums: Regular meetings and forums are held where faculty members can discuss curriculum development, teaching methodologies, and student feedback to identify areas for improvement.

4.2 Quality Enhancement Initiatives

Our dedication to quality enhancement drives us to continuously innovate and improve our program offerings. We actively engage in quality assurance processes, accreditation reviews, and benchmarking exercises to ensure that our program meets or exceeds industry standards and best practices. Faculty development initiatives, curriculum revisions, and investments in instructional technologies are prioritized to enhance the learning experience and equip students with the skills and competencies demanded by employers. Furthermore, strategic partnerships with industry leaders and research institutions facilitate knowledge exchange and collaboration, enabling us to stay at the forefront of AI education and research.

Proposed Strategic Partnerships in 2024:

• Peerless College, Calgary AB Canada, -For offering student exchange programs. 
• Canadian Institute for Advanced Education, Dhaka Bangladesh for professional development. 
• Celestia Bookings, Calgary AB Canada -For Internship & to gain Industry experience for business students.
• Dr. Gulshan Akter Center for Healthcare -Calgary AB Canada -Healthcare programs partnership and practicum offerings. 

4.3 Program Review and Monitoring

Regular program review and monitoring are essential components of our quality assurance efforts. We conduct comprehensive reviews of the "Artificial Intelligence for Business" program periodically, involving stakeholders from within and outside the institution. These reviews assess program outcomes, student satisfaction, faculty contributions, and industry relevance to ensure that our program remains responsive to changing market demands and technological advancements. Monitoring mechanisms such as student performance tracking, course evaluations, and alumni surveys provide ongoing insights into program effectiveness and areas for improvement, guiding our continuous improvement efforts.

Annual Program Review:

• The institution conducts an annual comprehensive review of the AI diploma program.
• Program coordinators, faculty members, administrators, and relevant stakeholders participate in the review process.
• The review covers various aspects of the program, including curriculum content, teaching methodologies, learning outcomes, student performance, and feedback received from stakeholders.

Curriculum Alignment and Relevance:

• The curriculum is reviewed to ensure alignment with industry trends, technological advancements, and evolving job market demands in the field of AI.
• Program coordinators assess the relevance of course offerings, learning objectives, and practical components to meet the current and future needs of AI professionals in Dubai and globally.
• Industry Engagement and Employer Feedback:
• The institution engages with industry partners, employers, and professional associations to gather feedback on the skills and competencies needed in the AI workforce.
• Employer feedback on the performance of graduates, their preparedness for the workforce, and areas for improvement is collected and analyzed.
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Section 5: Conclusion

In conclusion, this self-evaluation report serves as a comprehensive overview of our diploma program in Artificial Intelligence, underscoring our unwavering dedication to upholding high-quality educational standards. Throughout this report, we have meticulously detailed the program's goals, structure, curriculum, and assessment methods, while also spotlighting the expertise and qualifications of our esteemed faculty members. Additionally, we have highlighted the array of resources at our disposal that are tailored to support student learning and development effectively.

Central to our commitment to excellence is our steadfast embrace of continuous improvement. We have established robust feedback mechanisms and quality enhancement initiatives that allow us to continually assess and refine our program offerings. By actively soliciting input from students, alumni, faculty, employers, and other stakeholders, we ensure that our program remains relevant, responsive, and aligned with industry needs and emerging trends.

Confident in the caliber of our program, we firmly believe that it meets the requisite standards for programmatic accreditation. We are eager to engage in the external review process and welcome feedback from the Accreditation Committee. We view this as an invaluable opportunity to further enhance the quality and effectiveness of our program, ultimately benefiting our students and the broader community.

In closing, we extend our gratitude for considering our institution for programmatic accreditation. We eagerly anticipate the opportunity to demonstrate our unwavering commitment to providing a high-quality education and preparing our students for successful and impactful careers in the dynamic field of Artificial Intelligence.