Sessions 1 Course Overview - Overview of the course and what students can expect to learn. Introduction to the topics that will be covered, as well as the skills and knowledge that students will gain by completing the course. Overview of the course structure, including the number of lessons and their duration, the types of assessments that students will complete, and any group work or projects that will be included. Information on how to contact the instructor or access course materials. Importance of New Technologies – Discussion on the growing importance of new technologies in today's business world, and how they are transforming industries and creating new opportunities for businesses. Scientific Reasoning Paradigm – Introduction to the scientific reasoning paradigm and how it applies to the study of new technologies in businesses. Discussion of the importance of evidence-based decision-making and the role of experimentation in testing new technologies. Learning Objectives – Outline of the specific learning objectives of the course, such as understanding how new technologies can enhance internal mechanisms of businesses, identifying the potential impact of these technologies on the short and long term, and evaluating different technologies to determine which ones are a good fit for a given context. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Introduction to AI and ML Applications in Finance - Overview of how AI and ML are transforming the finance industry today. Focus on the benefits of using AI and ML in finance, such as improved accuracy, faster decision-making, and reduced costs. Fraud Detection with AI and ML - Various ways in which AI and ML are being used to detect and prevent fraud in the finance industry. Examples of fraud detection algorithms, such as anomaly detection, pattern recognition, and machine learning-based fraud detection. Risk Management with AI and ML - Risk management in the finance industry. Discussion on how these technologies are being used to model and predict risk, as well as how they are being used to optimize risk management strategies. Investment Analysis with AI and ML – How AI and ML are being used to improve investment analysis in the finance industry. Discussion on how these technologies are being used to identify patterns and trends in financial data, as well as how they are being used to develop predictive models for investment decisions. Case Studies and Real-World Examples - How AI and ML are being used in the finance industry today. Examples from major financial institutions and startups alike. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Introduction to Deep Learning and Neural Networks - Overview of deep learning and neural networks and how they differ from traditional machine learning algorithms. How deep learning models are constructed and trained, examples of the types of problems that they are well-suited to solve. Image and Speech Recognition - How deep learning and neural networks are being used to develop advanced image and speech recognition systems. Discussion of the different types of neural networks used for these tasks, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Examples of how these networks are used in real-world applications, such as facial recognition and speech-to-text systems. Natural Language Processing - How deep learning and neural networks are being used to develop natural language processing (NLP) systems, which enable computers to understand and respond to human language. Discussion of the different types of NLP tasks, such as sentiment analysis and named entity recognition. Examples of how these tasks are performed using deep learning models. Robotics - How deep learning and neural networks are being used in robotics to enable machines to learn and adapt to their environments. Discussion of the different types of robots, such as industrial robots and autonomous vehicles. Examples of how these robots are being trained using deep learning models to perform tasks like object recognition and path planning. Case Studies and Real-World Examples – Case studies and real-world examples of how deep learning and neural networks are being used in a variety of industries, such as healthcare, finance, and manufacturing. This will help students understand the practical applications of these technologies beyond image and speech recognition, natural language processing, and robotics. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Introduction to Cloud Computing - Overview of cloud computing and its key characteristics, such as on-demand self-service, broad network access, and resource pooling. Explanation of the different types of cloud computing services, including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). Infrastructure as a Service (IaaS) - The basics of IaaS, including how it allows businesses to outsource their hardware needs, such as servers and storage, to a third-party provider. Benefits of IaaS, such as scalability, cost savings, and increased reliability. Platform as a Service (PaaS) - The concept of PaaS, including how it enables businesses to develop and deploy applications without the need for their own infrastructure. Benefits of PaaS, such as reduced time-to-market, improved collaboration, and enhanced security. Software as a Service (SaaS) - The concept of SaaS, including how it allows businesses to access software applications over the internet, rather than installing and maintaining them on their own hardware. Benefits of SaaS, such as reduced costs, increased productivity, and improved collaboration. Cloud Platforms - Overview of the major cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, and compare their respective benefits and drawbacks. How businesses can choose the right cloud platform for their needs, based on factors such as cost, functionality, and reliability. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Overview of blockchain technology and its key features, such as decentralization, immutability, and transparency. Blockchain applications in supply chain management: How blockchain technology can be used to improve supply chain efficiency, reduce costs, and enhance transparency and traceability. NFT introduction. Digital identity verification: How to create a decentralized and secure system for digital identity verification, without the need for intermediaries. Smart contracts and their potential applications in various industries, such as insurance, real estate, and finance. Provide examples of how smart contracts can automate the execution of contracts, reduce costs, and increase efficiency. Challenges and limitations of blockchain technology, such as scalability, interoperability, and regulatory issues. Case studies: Real-world examples of companies that have implemented blockchain technology in their businesses and the benefits they have gained. Future developments: Potential future developments in blockchain technology and its implications for businesses and society. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Introduction to the Internet of Things (IoT): definition, characteristics, and examples of IoT devices and applications The role of IoT in Industry 4.0: how IoT is a key enabler of Industry 4.0 and its various components (e.g., smart factories, digital twins, cyber-physical systems) Benefits of IoT for businesses: how IoT can improve efficiency, reduce costs, enhance safety, and enable new business models Challenges of IoT implementation: security, privacy, interoperability, and scalability issues IoT applications in various industries: examples of how IoT is being used in manufacturing, logistics, healthcare, energy, and other sectors IoT data analytics: how to extract insights from the large amounts of data generated by IoT devices and systems Future trends in IoT: emerging technologies and standards, such as edge computing, 5G networks, and blockchain, that could impact the development of IoT Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Guest lectures by invited speakers from companies and industries

Sessions 1 Introduction to Cloud Platforms: Discussion on cloud platforms and why they are important for businesses. Comparison of Cloud Platforms: Overview of the major cloud platforms, including AWS, Azure, and Google Cloud, and compare their features, pricing, and availability. Benefits of Cloud Platforms: The benefits of using cloud platforms, such as increased scalability, cost savings, and flexibility. Applications of Cloud Platforms: Common applications of cloud platforms, such as website hosting, data storage and backup, and app development and deployment. Best Practices for choosing and implementing cloud platforms, including security considerations, vendor lock-in, and compliance requirements. Future Trends: Emerging trends in cloud computing, such as serverless computing, hybrid cloud environments, and artificial intelligence services. Introduction to Big Data: What is big data and why it matters for businesses. Data Warehousing and how it can be used to store and manage large amounts of data efficiently. Data Mining and its importance for discovering patterns, trends, and insights from large datasets. Applications of Big Data: Common applications of big data in businesses, such as customer analytics, fraud detection, and supply chain optimization. Case Studies: Provide some real-world examples of businesses that have successfully implemented big data technologies, and how they have benefited from them. Best Practices for implementing big data technologies, including data quality, data privacy, and governance. Future Trends: Emerging trends in big data, such as edge computing, machine learning, and natural language processing. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Introduction to Cybersecurity and why it matters for businesses. Threats and Attacks: Common cyber threats and attacks that businesses face, such as malware, phishing, and ransomware. Risk Assessment to identify potential vulnerabilities and risks to the business. Security Tools and Technologies: Provide an overview of some common security tools and technologies, such as firewalls, intrusion detection systems, and encryption, and how they can be used to protect data and systems from cyber threats. Incident Response and Recovery: The importance of having an incident response and recovery plan in place, and how it can help businesses minimize the impact of a cyberattack. Cybersecurity Training and Awareness for employees, and how businesses can develop a culture of cybersecurity. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Session 1 Ethical considerations surrounding the use of new technologies in businesses. Data privacy, including issues related to data collection, storage, and usage. Brief introduction to different regulations and frameworks, such as the GDPR and CCPA, that aim to protect individuals' data privacy rights. Algorithmic bias and discrimination. How to identify and mitigate bias in AI and ML systems, and the potential consequences of not addressing these issues. Accountability and transparency in the use of technology, including the responsibility of businesses to communicate clearly with their customers and stakeholders about the ways in which technology is being used. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Sessions 1 Introduction to emerging technologies - What are emerging technologies and why are they important to businesses? Quantum Computing - An overview of the basics of quantum computing and its potential applications in businesses, including cryptography, optimization problems, and simulation. 5G Networks - An exploration of the capabilities and benefits of 5G networks, including increased speed, lower latency, and the potential for new applications in areas such as augmented reality and virtual reality. Edge Computing - An explanation of edge computing and its role in processing data at or near the source of its creation, including its potential benefits for businesses such as reduced latency and increased efficiency. Robotics and AI - A discussion of the latest advancements in robotics and AI, including the integration of the two technologies and their potential to transform various industries such as healthcare and transportation. Biotechnology - A discussion of the latest developments in biotechnology and their potential impact on businesses, including gene editing, personalized medicine, and agricultural biotechnology. Green Technologies - An exploration of emerging technologies in the field of sustainable energy, including renewable energy sources, energy storage, and carbon capture and storage. Future Trends in Emerging Technologies - A discussion of the future trends in emerging technologies and their potential impact on businesses in the coming years. Session 2 Hands on exercises. Vertical focuses. Additional examples of successful companies that have leveraged new technologies to gain a competitive edge. Use cases. Cases of study.

Discussion of the business cases