Ebusiness Final Study Guide PDF

**Complete Notes on Blockchain and Digital Currency** **1. How Many Bitcoins Are Going to Be Made?** - **Total Supply**: The maximum supply of Bitcoin is capped at **21 million coins**. - **Halving Events**: Bitcoin undergoes a \"halving\" approximately every four years, reducing the...

**Complete Notes on Blockchain and Digital Currency** **1. How Many Bitcoins Are Going to Be Made?** - **Total Supply**: The maximum supply of Bitcoin is capped at **21 million coins**. - **Halving Events**: Bitcoin undergoes a \"halving\" approximately every four years, reducing the reward for mining new blocks by half, which slows the rate of new Bitcoin creation. **2. Characteristics of Banking Systems** **[CENTRALIZED BANKING SYSTEMS]** - **Definition**: A banking system where a single central authority controls the financial operations. \[Central Bank\] - Use intermediaries such as commercial banks and Payment Service Providers (PSP) (mobile banking app) for transactions. - **Characteristics**: - Central authority manages all banking operations - Have the power to determine [interest rates], control the [money supply], and implement [monetary policies.] - Higher risk of fraud and corruption - Due to the concentration of power a single mistake or failure will affect the whole system and lead to a financial crisis. - Slower transaction speeds due to intermediaries. - Take longer to process due to the diverse intermediaries and regulations of each one. **[DECENTRALIZED BANKING SYSTEMS]** - **Definition**: A banking system where **no** single entity has control over the entire network. - Uses ***blockchain*** or ***shared ledger*** - **Characteristics**: - Peer-to-peer transactions without intermediaries. - Transactions occur between users (peers) without intermediaries, and are validated by the same ones. - Enhanced security and privacy. - User have more control over their personal and financial data - And are protected by cryptographic techniques from any fraud or hacking - Faster transaction speeds and reduced costs- as there is no need for intermediaries. **[AUTHENTICATION AND NON-REPUDIATION FOR SAFE TRANSACTIONS ]** **Authentication** - **Definition**: The process of verifying the identity of a user or entity/system involved in the transaction. - It ensures that the parties involved are who they claim to be. - **Characteristics**: - Ensures that only authorized users can access the system. - Can involve multi-factor authentication for added security. - Use of passwords. PINS, biometric data (fingerprints, facial recognition), etc. **Non-repudiation** - **Definition**: Assurance that someone cannot deny the validity of their signature on a document or a message. - Non-repudiation ensures that once a transaction is completed, the sender cannot deny having sent the transaction, and the recipient cannot deny having received it. - transaction history is permanent and verifiable - **Characteristics**: - Provides proof of the origin and integrity of data. - Essential for legal and financial transactions. **3. CONCEPT OF [DIGITAL CURRENCY]** - **Definition**: Digital currency is a form of currency that exists only in digital form and is not tangible like physical money. - **Characteristics**: - Can be used for online transactions. - Includes cryptocurrencies, stablecoins, and Central Bank Digital Currencies (CBDCs). - Offers faster and often cheaper transactions compared to traditional banking. **4. DEFINITION OF [CRYPTOCURRENCY]** - **Definition**: A type of digital or virtual currency that uses cryptography for security and [operates on] a [technology] called [blockchain]. - **Characteristics**: - Decentralized and typically not controlled by any central authority. - Examples include Bitcoin, Ethereum, and Ripple. **5. Problems with Cryptocurrency** - **High Price Volatility**: Cryptocurrencies can experience significant price fluctuations, making them unreliable as a stable store of value. Problems with exchange mediations. - **Regulatory Concerns**: Many governments are still figuring out how to regulate cryptocurrencies, leading to uncertainty. - **Security Risks**: While blockchain is secure, exchanges and wallets can be vulnerable to hacks. **6. DEFINITION OF [STABLECOIN]** - **Definition**: A type of cryptocurrency designed to have a stable value. It is usually tied to something stable, like the U.S. dollar or gold, so its price doesn't change a lot like other cryptocurrencies. - **Characteristics**: - Aims to reduce volatility associated with traditional cryptocurrencies. - Often used for transactions and as a store of value. **7. Issues With Stablecoins** - **Lack of Regulation**: They might not follow strict financial rules, leading to risks for users. - **Trust in Reserves**: Some stablecoins claim to be backed by real assets like dollars, but it\'s not always clear if they have enough reserves. - **Centralization**: Many stablecoins are controlled by a company, which can go against the idea of decentralization in crypto. - **Market Risks**: If the value of the backing asset (like dollars or gold) changes, it could affect the stablecoin\'s stability. - **Systemic Risks**: If a popular stablecoin fails, it could disrupt the broader crypto market. **8. [DEFINITION OF CBDC (CENTRAL BANK DIGITAL CURRENCY)]** - **Definition**: A digital form of a country\'s fiat currency [issued and regulated by the central bank.] - **Characteristics**: - Legal tender and equivalent to physical cash. - Aims to enhance payment systems and financial inclusion. - Can reduce costs associated with currency manufacturing and distribution. - Use central banks rather than individual banks to eliminate credit risk. **9. Problems with CBDC** - **Privacy Concerns**: Governments could track every transaction, raising concerns about surveillance and loss of financial privacy. - **Cybersecurity Risks**: CBDCs could be targets for hacking or cyberattacks, risking loss of funds or data breaches. - **Impact on Banks**: CBDCs might reduce the role of traditional/commercial banks, as people could hold money directly with the central bank. This could destabilize the banking system. - **Financial Inclusion**: In areas with limited digital access, implementing CBDCs could widen the gap between those who can and cannot use them. - **Implementation Challenges**: Technical and regulatory challenges in integrating CBDCs into existing financial systems. **10. [EXPLANATION OF BLOCKCHAIN]** - **Definition**: A blockchain is a decentralized, distributed ledger technology that records transactions across many computers in a way that the registered transactions cannot be altered retroactively. - **Functionality**: Each block contains a number of transactions, and every time a new transaction occurs, a record of that transaction is added to every participant\'s ledger. **11. [CHARACTERISTICS OF BLOCKCHAIN]** **Positive Characteristics** - **Decentralization**: No single point of control, reducing the risk of fraud. - **Transparency**: All transactions are visible to participants, enhancing trust. - **Immutability**: Once recorded, transactions cannot be altered, ensuring data integrity. - **Security**: Cryptographic techniques secure the data against unauthorized access. **Negative Characteristics** - **Scalability Issues**: As the number of transactions increases, the network can become congested. - **Energy Consumption**: Some blockchain networks, especially those using proof-of-work, consume significant amounts of energy. - **Complexity**: Understanding and implementing blockchain technology can be challenging for users and developers. **12. [WHAT IS A NODE IN A BLOCKCHAIN?]** - **Definition**: A node is any active electronic device/ computer that maintains a copy of the blockchain and helps validate and relay transactions. - **Functionality**: Nodes can be full nodes (holding the entire blockchain) or lightweight nodes (holding only part of the blockchain). **13. [WHAT IS A P2P NETWORK IN A BLOCKCHAIN?]** - **Definition**: A peer-to-peer (P2P) network is a decentralized network where each participant (node) can communicate directly with others without a central server. - The participants act as clients and servers simultaneously - **Characteristics**: - Enhances resilience and redundancy. - Facilitates direct transactions between users. **14. [WHAT IS A SHARED LEDGER IN A BLOCKCHAIN?]** - **Definition**: A shared ledger is a database that is shared and synchronized across multiple participants in a network. - **Characteristics**: - Ensures all participants have access to the same data. - Reduces discrepancies and enhances trust among participants. **15. Ethical Issues in Blockchain** **Transparency** - **Explanation**: While transparency can enhance trust, it can [also] lead to privacy concerns as all transactions are visible to participants. **Immutability** - **Explanation**: The inability to alter past transactions can be beneficial for data integrity but may pose challenges in correcting errors or fraudulent activities. **Permanency** - **Explanation**: The permanent nature of blockchain records raises ethical questions about data ownership and the right to be forgotten. **Summary of Ethical Issues** - **Pros**: - Enhances trust and accountability. - Reduces fraud and corruption. - **Cons**: - Potential invasion of privacy. - Challenges in correcting mistakes or fraudulent activities. **Notes on Artificial Intelligence (AI)** **1. [Definition of AI]** Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI is a field of computer science that focuses on machines (especially computer systems) that simulate human thinking and behavior: and have the ability to reason, learn, and find underlying meanings and connections from data. - **Key Points:** - Simulates human thinking and behavior. - Involves reasoning, learning, and decision-making. - Aims to mimic human intelligence. **2. DEFINITION OF [WEAK AND STRONG AI]** - **Weak AI:** Also known as narrow AI, it refers to AI systems that are [designed and trained] for [a specific task]. They can make decisions based on reasoning and past data but do not possess general intelligence. - Do simple decision-making based on past data - **Strong AI:** This type of AI aims to replicate/mimic [human cognitive abilities], allowing machines to understand, learn, and apply knowledge in a way that [is indistinguishable from human intelligence]. - **Key Points:** - Weak AI: Task-specific, prevalent in current applications. - Strong AI: Aspires to achieve human-like intelligence. **3. THE [5 ADOPTERS AI CATEGORIES IN SOCIETY]** 1. **Innovators** (2.5%)**:** The first individuals to adopt new technology, often risk-takers. 1. First to try 2. **Early Adopters** (13.5%)**:** Tech [enthusiasts] who are quick to [embrace] new [innovations] but seek proof of effectiveness. 2. But before making decisions about getting a product they do some research and look for opinions so they can make [informed decisions]. 3. Influencers who guide others 3. **Early Majority** (34%)**:** Users who adopt technology after seeing evidence of its benefits. 4. More cautious but adopt AI after seeing its success with others. 5. Look for proven benefits and reliability before using it. 4. **Late Majority** (34%)**:** Skeptical and adopt AI when it\'s widely used and seen as [necessary]. 6. Need strong evidence of its value and stability. 5. **Laggards** (16%)**:** Resistant to change(last ones in adopting) and adopt AI only when it\'s unavoidable. 7. May prefer traditional methods and require [significant motivation to switch]. **4. The "CHASM" in the Technology Adoption Life Cycle** The \"chasm\" refers to the gap between [early adopters] and the [early majority] in the technology adoption life cycle. It represents a [critical point] where [many] innovations [fail] to gain traction because the early majority is more cautious and requires substantial proof of effectiveness. - **Key Points:** - Represents a significant hurdle for new technologies. - Early adopters are willing to take risks; early majority is more conservative. **5. THE [7 DIFFERENT BRANCHES OF AI]** 1. **Machine Learning (ML)**: This branch involves developing algorithms that enable computers to learn from and make predictions based on data. It includes [supervised, unsupervised, and reinforcement learning techniques]. 2. **Natural Language Processing (NLP)**: NLP focuses on the interaction between computers and humans [through natural language]. [It enables machines to **understand, interpret, and respond to human language**] in a meaningful way. 3. **Computer Vision**: This area deals with how computers can "see" (learn) from digital images or videos. It involves [techniques for image processing, recognition, and analysis]. 4. **Robotics**: Robotics combines AI with engineering to design and create robots that can perform tasks autonomously or semi-autonomously. This branch focuses on the physical embodiment of AI. 5. **Cognitive computing**: Refers to systems that simulate human thought processes, enabling machines to understand, reason, learn, and interact naturally with humans. 6. **Neural Networks**: Inspired by the human brain, neural networks are a set of algorithms designed to recognize patterns. They are particularly effective in tasks like image and speech recognition. 7. **Deep Learning**: A subset of machine learning, deep learning uses multi-layered neural networks to analyze various factors of data. It excels in handling large datasets and complex problems, such as image and voice recognition. - Each branch focuses on different aspects of AI capabilities. **6. WHAT IS [MACHINE LEARNING]?** Machine Learning is a subset of AI that involves the development of algorithms that allow computers to learn from and make predictions based on data. It enables systems to improve their performance on tasks over time without being explicitly programmed. - **Key Points:** - Involves training models on data. - Focuses on pattern recognition and prediction. **7. WHAT IS [SUPERVISED MACHINE LEARNING]?** Supervised Machine Learning is a type of machine learning where a model is trained on labeled data, meaning the input data is paired with the correct output. The model learns to map inputs to outputs and can make predictions on new, unseen data. [A type of machine learning] where a model is trained using labeled data. \"Labeled data\" means the data comes with the correct answers, so the model learns by example. **How It Works:** 1. **Input and Output:** You give the model input data (features) and the correct output (labels). For example: - Input: A picture of a cat. - Output: The label \"cat.\" 2. **Training:** The model looks for patterns in the input data to learn how to predict the correct label. 3. **Prediction:** Once trained, the model can predict labels for new, unseen data. **Example:** Imagine teaching a robot to sort fruits: - Training data: Pictures of apples and bananas, each labeled as \"apple\" or \"banana.\" - The robot learns what makes an apple different from a banana. - Later, it can label new fruit pictures as either \"apple\" or \"banana.\" - **Key Points:** - Involves labeled datasets. - Analogous to a student learning from questions and answers. **8. WHAT IS [UNSUPERVISED MACHINE LEARNING]?** Unsupervised Machine Learning involves training a model on data without labeled responses. The model tries to learn the underlying structure of the data by identifying patterns and groupings. - **Key Points:** - No labeled data is used. - Focuses on discovering hidden patterns. **9. [WHAT IS TRANSFER MACHINE LEARNING?]** Transfer Machine Learning is the process of applying knowledge gained from solving one problem to a different but related problem. It allows for leveraging existing models to improve performance on new tasks. a technique in machine learning where a model trained on one task is reused [as] the [starting point] [for] a different but [related task]. It's like learning a skill in one area and applying it to another. - **Key Points:** - Knowledge transfer from one task to another. - Useful in scenarios with limited data. **10. [THE 4 DIFFERENT ALGORITHM BIASES]** 1. **Sample Bias:** Occurs when the data used to train the model is not representative of the population. 2. **Prejudice Bias:** This type of bias occurs when social or cultural stereotypes are involved in the training data. a. Historically male hiring preference 3. **Measurement Bias:** Results from errors/inconsistencies [in data collection, measurement or labeling]. 4. **Variance Bias:** Occurs when a model is overly complex and fits too closely to the training data, capturing noise or irrelevant patterns. - **Key Points:** - Each bias can lead to inaccurate predictions and unfair outcomes. **11. [WHAT IS OVERFITTING?]** Happens when a model learns too much from the training data---even the noise or random patterns that don't matter---so it [doesn't perform well on new data]. **Overfitting** means the [ model is too good at matching the training data but fails to generalize to unseen data]. - **Key Points:** - Model is too complex. - Performs well on training data but poorly on test data. **12. [WHAT IS UNDERFITTING?]** Underfitting in machine learning happens when a model is too simple to capture the patterns in the data, leading to poor performance on both training and test data. - **Key Points:** - Model is too simplistic. - Fails to capture important patterns. **13[. DEFINITION OF NEURAL NETWORK]** A Neural Network is a computational model inspired by the way biological neural networks in the human brain process information. [It consists of interconnected nodes (neurons)] [that work together to solve specific problems]. - **Key Points:** - Mimics human brain function. - Composed of layers of interconnected nodes. **14. Components of a Neural Network** 1. **Input Layer:** The [first layer] that receives the input data. \[Receives raw data; no processing occurs\] 2. **Hidden Layer:** [Intermediate la]yers that process inputs and extract features. Can have multiple layers 3. **Output Layer:** The [final layer] that produces the output or model's prediction. **15. WHAT IS [COMPUTER VISION]?** Computer Vision is [a field of AI] that enables machines to interpret and make decisions based on visual data from the world. It involves the extraction, analysis, and understanding of information from images and videos. - **Key Points:** - Focuses on visual data interpretation. - Applications include image recognition, object detection, and more. **16. [THE 5 IMAGE PROCESSING STEPS IN COMPUTER VISION]** 1. **Image Acquisition:** Capturing images using cameras or sensors. 2. **Image Preprocessing:** Enhancing image quality and [preparing it for analysis]. 3. **Feature Extraction:** Identifying and isolating important features in the image. 4. **Image Segmentation:** Dividing an image into meaningful parts for easier analysis. 5. **Image Classification:** Assigning labels to segments or objects within the image. **Notes on Cloud Computing** **Definition of Cloud Computing** Cloud computing refers to the on-demand delivery of computing resources, including servers, storage, databases, networking, software, and analytics, over the internet (\"the cloud\"). It allows users to access and utilize these resources without the need for physical hardware or infrastructure. - **Short Paragraph**: Cloud computing is a model that enables convenient, on-demand access to a shared pool of configurable computing resources. This model allows for rapid provisioning and release of resources, providing flexibility and efficiency in managing IT resources. **Key Characteristics of Cloud Computing** 1. **On-Demand Self-Service**: Users can provision computing resources automatically without requiring human interaction with service providers. 2. **Broad Network Access**: Resources are available over the network and can be accessed through standard mechanisms, promoting use across various platforms (e.g., mobile phones, tablets, laptops). 3. **Resource Pooling**: Providers serve multiple customers using a multi-tenant model, with resources dynamically assigned and reassigned according to demand. 4. **Rapid Elasticity**: Resources can be scaled up or down quickly to meet demand, providing flexibility. 5. **Measured Service**: Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service. **Key Cloud Computing Providers** - **Amazon Web Services (AWS)** - **Microsoft Azure** - **Google Cloud Platform (GCP)** - **IBM Cloud** - **Oracle Cloud** - **Alibaba Cloud** **Cloud Service Models** **Software as a Service (SaaS)** - **Definition**: SaaS delivers software applications over the internet on a subscription basis. - **Examples**: Google Workspace, Salesforce, Dropbox. - **Benefits**: No installation or maintenance required; accessible from any device with internet access. **Platform as a Service (PaaS)** - **Definition**: PaaS provides a platform allowing customers to develop, run, and manage applications without the complexity of building and maintaining the infrastructure. - **Examples**: Google App Engine, Microsoft Azure App Service. - **Benefits**: Simplifies the development process; supports multiple programming languages and frameworks. **Infrastructure as a Service (IaaS)** - **Definition**: IaaS offers virtualized computing resources over the internet, providing fundamental computing resources such as virtual machines, storage, and networks. - **Examples**: Amazon EC2, Microsoft Azure Virtual Machines. - **Benefits**: High scalability and flexibility; users can manage their own applications and operating systems. **Cloud Deployment Models** **Public Cloud** - **Definition**: Services are delivered over the public internet and shared across multiple organizations. - **Benefits**: Cost-effective, scalable, and no maintenance required by the user. **Private Cloud** - **Definition**: Cloud infrastructure is exclusively used by a single organization, either managed internally or by a third party. - **Benefits**: Greater control, security, and customization tailored to specific business needs. **Hybrid Cloud** - **Definition**: A combination of public and private clouds, allowing data and applications to be shared between them. - **Benefits**: Flexibility, scalability, and the ability to keep sensitive data secure while leveraging public cloud resources. **Differences Between Public, Private, and Hybrid Cloud** - **Public Cloud**: - Shared resources - Lower costs - Less control over security - **Private Cloud**: - Dedicated resources - Higher costs - Greater control and security - **Hybrid Cloud**: - Combination of both - Flexibility in resource management - Balances cost and control **Migration Strategies** **Lift-and-Shift Migration** - **Definition**: Moving applications to the cloud with minimal changes. - **Benefits**: Quick migration; retains existing architecture. **Replatforming** - **Definition**: Making a few cloud optimizations to achieve some tangible benefit without changing the core architecture. - **Benefits**: Improved performance and cost savings. **Re-architecting** - **Definition**: Redesigning applications to fully leverage cloud capabilities. - **Benefits**: Enhanced scalability and performance. **Hybrid Approach** - **Definition**: Using both on-premises and cloud resources. - **Benefits**: Flexibility and optimized resource allocation. **Multi-Cloud Strategy** - **Definition**: Utilizing multiple cloud services from different providers. - **Benefits**: Reduces dependency on a single provider and enhances resilience. **What is Edge Computing?** - **Definition**: Edge computing refers to the practice of processing data near the source of data generation rather than relying on a centralized data center. - **Benefits**: Reduces latency, improves response times, and saves bandwidth by processing data locally. - **Short Paragraph**: Edge computing enhances cloud computing by bringing computation and data storage closer to the location where it is needed. This approach minimizes latency and bandwidth use, making it ideal for applications requiring real-time data processing, such as IoT devices and autonomous vehicles. **Notes on Retail and Service Sectors** **1. WHAT IS [PURE PLAY] IN THE RETAIL SECTOR?** **Definition:** - Pure play refers to retail businesses that operate ***exclusively*** online without any physical store presence. **Characteristics:** - Lacks a physical store network. - Operates on unproven business assumptions. - No established brand name or loyal customer base. - No real estate investment. - Focuses on delivering superior service and convenience at lower costs. **2. WHAT IS THE [SERVICE SECTOR]?** **Definition:** - The service sector encompasses businesses that provide services rather than goods. **Characteristics:** - Value is derived from collecting, storing, and exchanging information. - Ideal for online platforms, especially in industries like finance, travel, and career services. **3. What is the Definition of the Retail Industry?** **Definition:** - The retail industry involves the sale of goods and services to consumers for personal use. It includes various segments that utilize the internet differently for information and purchasing. **4. Main Difference Between Online and Offline Retail Sectors** **Online Retail:** - Operates through digital platforms. - Offers convenience and often lower prices. - Integrates online and offline operations (omni-channel). **Offline Retail:** - Involves physical stores. - Relies on in-person shopping experiences. - May have higher operational costs. **5. Characteristics of the Online Retail Sector** - Rapid growth compared to offline segments. - Significant revenue generation from categories like computers and consumer electronics. - High consumer adoption, with over 80% of internet users expected to shop online. - Integration of online and offline operations (omni-channel). **6. Facts About the Online Retail Sector Today** - Accounts for about 11% of the total retail industry. - Growing faster than offline retail. - Major beneficiaries include established offline retailers with online presence and pure-play online retailers like Amazon. **7. The 7 Segments of the Retail Industry** 1. **Department Stores:** Large stores offering a wide range of products across various categories. 2. **Specialty Stores:** Focus on specific product categories, providing specialized services. 3. **Supermarkets:** Large grocery stores that offer a variety of food and household products. 4. **Convenience Stores:** Small retail outlets that stock everyday items and are open long hours. 5. **Discount Stores:** Retailers that sell products at lower prices, often through bulk purchasing. 6. **E-commerce:** Online retailing, which includes both pure-play and omni-channel retailers. 7. **Catalog Retailing:** Selling products through catalogs, often combined with online ordering. **8. Difference Between Information vs. Direct Purchasing** - **Information:** Involves using the internet to gather details about products, prices, and reviews without making a purchase. - **Direct Purchasing:** Refers to the actual transaction where consumers buy products or services online. **9. Assumptions of E-commerce Retail** - Consumers prefer the convenience of online shopping. - Online platforms can offer lower prices due to reduced overhead costs. - Effective marketing and customer service can build a loyal customer base. **10. What is Economic Viability?** **Definition:** - Economic viability refers to a firm\'s ability to sustain itself as a profitable business over a specified period (typically 1-3 years). **11. Two Approaches to Analyzing the Viability of an Online Firm** 1. **Strategic Analysis:** - Focuses on both industry and firm-specific factors. - **Industry Strategic Factors:** Barriers to entry, supplier power, customer power, substitute products, industry value chain, intra-industry competition. - \*\*Firm-S \...