What is the purpose of choosing appropriate visual encoding in data visualization?

To accurately represent data patterns

To make the visualization more complicated

To make the visualization visually appealing

What is the purpose of adding labels to data visualization?

To make the visualization more cluttered

Which of the following is a recommended practice for creating an effective data visualization?

Clearly communicate the main message

Overload the visualization with excessive information

Use a small and illegible font size

When choosing a chart type for data visualization, what should you consider?

The type of data being presented

The popularity of the chart type

Which of the following is NOT a best practice for selecting colors in data visualization?

Use random colors without any consideration

Consider color blindness accessibility

Use contrasting colors for clarity

How to make an effective data visualization

Got a presentation to make? Easy enough. Think of this as an opportunity to convince someone to see your point of view. One effective way to do that is by using data. Data is facts, numbers, and figures used to report the results of the research. Using data can be helpful to explain complex subjects. Here's how to do it. Research your topic to find data to support your presentation. Step 2. Be sure that any facts or figures you use are from reliable sources, like government, university, and scientific websites or trusted news outlets. You have to be careful. There is a lot of fake information floating around. Step 3. Take the time to edit your data so you're only using what's important and relevant to your topic. Reciting a long list of facts and numbers can put your audience to sleep. Step 4. Remember your data has to tell a story. Choose the right words and phrases to describe your findings. Reports indicate a sharp decrease in costs. Step 5. Take your facts and numbers and turn them into something exciting. This chart shows we had a boom in exports. Almost any data can be used to create colorful and eye-catching graphics and charts. In this graph, you can see a dramatic increase. Which makes it more memorable and 100% more entertaining to watch. Thank you.

Create me a multiple choice test questions with 4 options on the following topic:Consumer Education for Different Audience 1. Children and Youth: - Focus: Building foundational knowledge about basic consumer concepts, making safe choices, understanding money and value, and recognizing scams and unsafe situations. 2. Teens and Young Adults: - Focus: Building financial literacy, responsible debt management, understanding contracts and agreements, responsible technology use, online safety, and consumer rights. 3. Working Adults and Families: - Focus: Managing budgets, making informed purchasing decisions, understanding credit and debt, finding consumer protection resources, and navigating complex financial products (mortgages, insurance, investments). 4. Seniors: - Focus: Protecting themselves from scams and fraud, understanding common consumer issues like telemarketing, identity theft, and online scams, managing medications and healthcare costs, and accessing community resources. 5. Special Populations: - Focus: Adapting consumer education programs to the specific needs of people with disabilities, immigrants, refugees, and other marginalized communities. 6. Business and Industry:- Focus: Understanding ethical marketing practices, complying with consumer protection laws, and providing clear and accurate information to consumers. 7. Policymakers and Regulators: - Focus: Understanding consumer needs, developing effective consumer protection laws, enforcing regulations, and ensuring a fair and competitive marketplace. Adapting consumer education programs for children, teens, and seniors requires tailoring content and delivery methods to their unique needs and learning styles. Children (Ages 5-12): - Understanding the concept of money: Teaching children about saving, spending, and the value of money. - Developing basic budgeting skills: Helping children learn to make choices about how to spend their allowance or pocket money. EFFECTIVE STRATEGIES •Focus on basic concepts: Introduce core concepts like saving, spending, and budgeting in a fun and engaging way. Use simple language and relatable examples. •Real-life scenarios: Use age-appropriate scenarios to illustrate financial concepts, like buying toys or snacks. •Parental involvement: Encourage parent participation and provide resources to help them reinforce lessons at home. Teens (Ages 13-18): - Building budgeting and financial planning skills: Teaching teens how to manage their money, set financial goals, and plan for the future. - Navigating the digital marketplace: Equipping teens with the knowledge and skills to make safe and informed online purchases, understand digital marketing, and protect themselves from scams. EFFECTIVE STRATEGIES • Practical skills: Focus on skills relevant to teens, like managing money for social activities, saving for college, and understanding credit cards. • Digital literacy: Address the growing influence of online shopping, social media advertising, and financial scams. • Real-world applications: Connect financial concepts to real-life decisions teens make, like choosing a part-time job or making purchases online. Seniors (Ages 65+) - Managing retirement savings and healthcare costs: Providing information and resources on retirement planning, Medicare and Medicaid, and other healthcare options. - Navigating the digital world: Offering technology training and resources to help seniors access online services and information safely and securely. EFFECTIVE STRATEGIES • Addressing specific concerns: Focus on topics relevant to senior citizens, like retirement planning, managing healthcare expenses, and avoiding scams. • Clear and concise communication: Use simple language and visual aids to ensure easy understanding. • Social interaction: Create opportunities for seniors to share experiences and learn from each other. Teaching Financial Literacy in school and Communities In Schools: Curriculum Integration: Financial literacy concepts can be seamlessly integrated into existing subjects, making learning more relevant and engaging. - Math: Budgeting exercises, calculating interest rates, analyzing financial data, and understanding compound interest are all natural applications of math skills. - Social Studies: Exploring the history of money, financial institutions, economic systems, and the impact of financial decisions on society provide valuable context. - Economics: Discussions about supply and demand, inflation, investment, and the role of consumers in the economy enhance financial literacy. Dedicated Courses: Offering elective courses or workshops specifically focused on personal finance provides deeper dives into crucial topics. - Personal Finance: Cover budgeting, saving, investing, credit, debt management, and insurance. - Entrepreneurship: Introduce concepts like business planning, marketing, financial forecasting, and managing cash flow. In Communities: Community Centers and Libraries: Workshops, seminars, and classes tailored to adults and families provide accessible learning opportunities. - Financial Planning: Cover budgeting, retirement planning, debt management, and estate planning. - Homeownership: Provide guidance on buying, selling, and maintaining a home. - Consumer Protection: Educate individuals about their rights and how to avoid scams. Partnerships with Financial Institutions: Collaborations with banks, credit unions, and financial advisors offer valuable resources, workshops, and financial literacy programs. Consumer Education for Low-Income and Vulnerable Populations Low-income refers to individuals or households with limited financial resources, typically below a certain threshold. Low-income individuals may face challenges like: 1. Limited education and job opportunities 2. Poor living conditions and housing 3. Food insecurity and malnutrition Causes of low income: 1. Unemployment or underemployment 2. Low-paying jobs or minimum wage 3. Limited education or skills 4. Single parenthood or large family size Vulnerable population'' is a term that is used to describe a group of people who possess some sort of disadvantage. elderly people, people with low incomes, homeless people, people in prison, migrant workers, pregnant women, Family Consumer Education: Managing Household Finances and Resources Financial literacy is the ability to understand and manage personal finances effectively. 1. Debt Debt is money you spend that isn’t yours. If you borrow money from the bank, use a credit card, or take out a short-term loan, or a payday loan, you are accumulating debt. Good debt is considered money borrowed for things that are absolutely necessary for making a life e.g. a house and for advancing your money-making potential e.g. an education. Bad debt is considered borrowing money or using a credit card to pay for things you don’t need, such as expensive clothes, hi-tech electronics, eating out at restaurants, going on holidays, etc. 2. Saving Saving is an essential part of financial wellness, a secure present, and a happy future. 3. Budgeting Budgeting is the life skill of planning and managing your money. By understanding exactly where your money goes every month, you are empowered to create an actionable plan by which you can spend less, by curtailing those unnecessary expenses and saving more for the things you need and want. 4. Investing Investing is all about creating and growing the wealth you need to enjoy a financially secure and happy future. It’s about putting your money into something that will make you a profit over time, such as property, retirement funds, and unit trusts Integrating Consumer Education into the Home Economics Curriculum. Integrating consumer education into the home economics curriculum can provide students with essential skills for making informed choices about their personal finances, food, clothing, and overall well-being. Here are some strategies and ideas for effectively incorporating consumer education: Financial Literacy Budgeting: Teach students how to create and manage a personal budget, including setting financial goals, tracking expenses, and understanding savings. Saving and Investment: Cover the basics of saving, including different saving accounts, and introduce concepts related to investing. Food and Nutrition Food Label Literacy: Engage students in learning how to read and interpret food labels, including nutrition facts and ingredient lists. Grocery Shopping Skills: Teach students how to compare product costs, understand unit pricing, and make healthy, budget-friendly choices while shopping. Clothing and Textile Education Consumer Choices in Clothing:Discuss factors influencing clothing purchases, such as quality, price, and sustainability. Fashion and Trends: Analyze the impact of marketing and advertising on consumer behavior regarding clothing. Sustainable Purchasing Eco-Friendly Choices: Raise awareness about environmentally friendly products and the importance of sustainability in consumer choices. Project-Based Learning - Assign real-life projects where students must apply their knowledge, such as creating a meal plan within a budget, planning a shopping list based on nutrient needs, or evaluating the cost-effectiveness of different products. Technology Integration - Use technology to teach students about online shopping, price comparison websites, and apps that aid budgeting and financial planning. Collaborative Learning Opportunities - Organize team projects where students work together to solve consumer-related problems, emphasizing teamwork and communication skills. Assessment and Reflection - Incorporate assessments that allow students to reflect on what they have learned about consumer education and how they can apply these skills in their daily lives.

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

Certainly, let's expand on each section in detail so you can learn more from the provided text: # Summary This text aims to explain various aspects related to technology, particularly focusing on a concept known as "Tech Disruption." It explores how technology affects different sectors, outlines the conditions for tech disruption, discusses the industrial revolutions, introduces the laws of disruption, and mentions factors influencing technology choices. ## Concept of Technology **Technology** encompasses a range of elements within organizations. It includes **expertise, equipment, and procedures** used to convert inputs, such as resources or raw materials, into outputs, which can be products or services. This involves various aspects, such as **product design, production techniques, quality assurance measures, human resource development, and management systems**. In essence, technology represents the tools and knowledge used to create and deliver goods or services effectively. ## What is Tech Disruption **Tech Disruption** refers to a phenomenon where smaller companies with limited resources successfully challenge well-established incumbent businesses. This disruption is primarily driven by **technology**, which acts as the catalyst, enabler, or even the sole reason behind the change. The significance of this concept lies in the fact that entrepreneurs must make careful and thoughtful decisions when it comes to adopting and investing in technology. **Why it's Important?** These decisions are crucial because they involve significant investments and will have a substantial impact on a company's ability to create, innovate, and operate its services in a sustainable and cost-effective manner. ## Four Main Impact **Tech Disruption** has four main impacts on businesses and industries: 1. **Shifting Customer Expectations**: As technology evolves, customer expectations change. Companies must adapt to meet these evolving demands to remain competitive. 2. **Enhanced Products Through Data**: Data-driven insights improve the productivity and efficiency of assets, leading to better products and services. 3. **New Partnerships and Collaboration**: Tech disruption encourages companies to form new partnerships and collaborations, recognizing the importance of working together to stay relevant. 4. **Transformation of Operating Models**: Traditional operating models are being transformed into digital models, where technology plays a central role in how businesses operate and deliver value. ## The 11 Macro Sources of Distribution The **11 Macro Sources of Distribution** represent various factors that influence the distribution of resources and opportunities in society. These factors include: 1. **Wealth Distribution**: How wealth is distributed among individuals and entities. 2. **Education**: The availability and quality of education opportunities. 3. **Infrastructure**: The state of infrastructure, such as transportation and communication networks. 4. **Government**: Government policies and regulations that impact resource distribution. 5. **Geopolitics**: Geopolitical factors, such as international relations and conflicts. 6. **Economy**: Economic conditions and trends, including markets and financial systems. 7. **Public Health**: The state of healthcare and public health systems. 8. **Demographics**: Characteristics of the population, such as age and gender. 9. **Environment**: Environmental factors and sustainability concerns. 10. **Media and Telecommunications**: The role of media and communication technologies. 11. **Technology**: Technological advancements and their impact on society. ## When Does Tech Disruption Happen? **Tech Disruption** occurs when specific conditions are met: ### Technology Is Mature Enough - **Technology Accessibility**: Technology must be accessible to a wide range of people and organizations. - **Critical Mass**: It should have reached a critical mass where it can create significant impact. - **Affordability**: Technology must be affordable for businesses to adopt. ### Sector Is Ready For Change - **Tech Infrastructure**: The sector should have the necessary technological infrastructure in place. - **Policy Framework**: A conducive policy framework is essential to support and regulate the use of technology. - **Lack of Disruption**: If the sector is stagnant or facing issues, it becomes ripe for tech disruption. ### Sector + Technology + Timing + Product - **Mature Technology with an Unready Sector**: If technology is mature but the sector is not ready, it can lead to building the wrong product based on incorrect assumptions. - **Unmatured Technology with a Ready Sector**: Conversely, if technology is not matured but the sector is ready, it may take longer to develop the product. ## Ready for Industri 5.0? This section briefly outlines the five industrial revolutions: 1. **Industri 1.0 (1784)**: Marked by mass production assembly lines using electrical power. 2. **Industri 2.0 (1870)**: Introduced mechanization, steam, and water power. 3. **Industri 3.0 (1969)**: Characterized by automated production, computers, IT systems, and robotics. 4. **Industri 4.0 (Present)**: Involves smart factories, autonomous systems, IoT (Internet of Things), and machine learning. 5. **Industri 5.0 (Future)**: Envisions mass customization and cyber-physical cognitive systems. ## Three Laws of Disruption These laws explain the nature of disruption: 1. **Disruption Comes to All**: Disruption is a universal phenomenon; it affects all industries and businesses sooner or later. 2. **Product-Market Fit**: Disruption occurs due to changes in Product-Market Fit, which means aligning a product with its target market effectively. 3. **Methods to Change Product-Market Fit**: To address disruption, a company can change the product, the target market, or influence people's preferences regarding the product. ## The 40% Rule This rule provides a framework for evaluating the fit between a product and its market: - **Value Proposition**: The product should solve customers' problems effectively. - **Channels**: The product should be able to reach customers cost-effectively. - **Monetization**: Customers should be willing to pay for the product. ## PMF Framework: 5 Steps to Product/Market Fit The **PMF (Product/Market Fit) Framework** consists of five steps: 1. **Business Modeling**: Developing a business model that aligns with the market. 2. **Market Validation**: Confirming that there is demand for the product in the market. 3. **Customer Interviews**: Gaining insights from potential customers. 4. **Product Development and Customer Acquisition**: Creating the product and acquiring customers. 5. **Product Analytics**: Using data to determine if the product has achieved Product/Market Fit. ## Factors Determining the Choice of Technology Several factors influence the choice of technology: 1. **Government Policy**: Government regulations and policies can encourage or restrict the adoption of specific technologies. 2. **Available Resources**: The resources, both financial and human, impact the adoption of technology. 3. **Technological Capability**: The organization's technological capabilities influence the choice of technology. 4. **Existing Technological Level**: The current technological state of the industry or organization plays a role. 5. **Institutional Arrangement**: Organizational structures and arrangements affect technology choices. ## Conclusion In conclusion, the text emphasizes the critical role of technology in driving change and disruption in various industries. It highlights the need for informed decision-making when it comes to technology investments, as well as the conditions necessary for tech disruption to occur. Understanding the historical context of industrial revolutions, the laws of disruption, and the factors influencing technology choices is essential in today's fast-paced and tech-driven business environment. Embracing technology disruption is crucial for transforming business models and adapting to evolving market dynamics.

In this video we take a look at the 0:02 fetch to code 0:03 execute cycle including its effect on 0:06 the various registers we've previously 0:12 [Music] 0:14 discussed a computer is defined Definition 0:17 as an electronic device that takes an 0:20 input 0:22 processes data 0:25 and delivers output 0:29 in this simple example you can see we're 0:31 taking the input 5 0:35 we're multiplying it by 2 that's our 0:37 process 0:39 and we're outputting 10. 0:44 but this could be way more complex for 0:46 example of a game console 0:48 the input could be the buttons you press 0:50 on a controller 0:53 the processes would then be carried out 0:55 by the console itself 0:59 and the output would be some form of 1:01 update to a monitor 1:02 and sound out for a speaker possibly 1:04 vibration feedback through the 1:06 controller 1:10 to process data a computer follows a set 1:13 of instructions 1:14 known as a computer program 1:18 if we take the lid off a typical desktop 1:20 computer we can identify 1:22 two critical components the memory 1:26 that stores the program and the central 1:29 processing unit or processor 1:31 which is under this large fan and 1:33 carries out the instructions 1:37 a computer carries out its function by 1:40 fetching 1:41 instructions decoding them and then 1:43 executing them 1:44 in a continuous repetitive cycle 1:46 billions of times a second 1:48 let's look at each of these stages in a 1:50 little more detail Fetch 1:53 so let's start with the fetch stage the 1:55 very first thing that happens 1:57 is the program counter is checked as it 2:00 holds the address 2:01 of the next instruction to be executed 2:07 the address stored is then copied into 2:09 the memory address register 2:14 the address is then sent along the 2:16 address bus to main memory 2:18 where it waits to receive a signal from 2:21 the control 2:22 bus so it knows what to do 2:27 as we want to read the data that's 2:29 stored in memory address 2:30 0 0 0 0 the control unit sends 2:34 a read signal along the control bus to 2:36 main memory 2:41 now main memory knows the data needs to 2:44 be read 2:45 the content stored in memory address 000 2:49 can be sent along the data bus to the 2:51 memory data register 2:56 now as we're currently in the process of 2:58 fetching an instruction 3:00 the data received by the memory data 3:03 register gets copied 3:04 into the current instruction register 3:11 the instruction effectively has now been 3:14 fetched from memory 3:16 just before we proceed to the decode 3:18 phase we now 3:19 increment the program counter so that 3:22 the address it contains 3:24 points to the address of the next 3:26 instruction which will need to be 3:30 executed 3:32 the instruction now being held in the 3:33 current instruction register 3:35 is ready to be decoded 3:39 now as we mentioned in the previous 3:41 video the instruction is made up of two 3:43 parts 3:44 we have the op code that's what it is we 3:47 need to do 3:50 and we have the operand what are we 3:53 going to do it to 3:55 now the operand could contain the actual 3:57 data 3:58 or indeed it could contain an address of 4:01 where the data is to be found 4:06 by decoding this instruction we can see 4:08 the operation we need 4:10 is a load operation so we need to load 4:14 the contents of memory location0101 4:18 into the cpus accumulator 4:25 in the exam a simple model will be used 4:27 to describe the 4:29 structure of any given instruction 4:32 you're not going to be expected to 4:34 define how an opcode is made up 4:36 but simply to interpret opcodes in the 4:39 given context of an exam 4:40 question in the example here 4:44 you can see there's a total of 16 4:46 different opcodes available 4:48 and this is because we're using four 4:50 bits for our representation 4:56 so now we've fetched the instruction and 4:59 we've decoded it so we know what we need 5:00 to do 5:01 we're finally ready to execute it 5:05 so we now send address 0101 5:08 to the memory dress register 5:13 now we're in the memory address register 5:15 we can finally send the address 5:18 down the address bus to main memory 5:24 this time we want to read the data 5:26 that's stored in memory 5:28 and so the control unit again sends a 5:30 read signal along the control bus 5:36 so main memories now receive an address 5:38 and a read signal 5:40 so the content stored at memory location 5:43 0101 5:44 can now be sent along the data bus back 5:46 to the cpu 5:47 and into the memory data register 5:54 finally the contents of the memory data 5:56 register are copied to the accumulator 5:59 and this is one of a number of general 6:00 purpose registers found in the cpu 6:04 this first instruction is now complete Branching 6:11 so what does this program actually do 6:14 you should be able to work it through 6:16 carefully and figure it out 6:19 we're now pointing instructions zero 6:21 zero zero one in the program counter 6:23 and we're ready to fetch the second 6:25 instruction 6:27 at the end of this video we're gonna 6:29 provide you with the answer 6:34 so let's talk a second about programs 6:37 that branch 6:40 on the left here we have a very simple 6:42 piece of pseudo code 6:44 line zero says first execute this line 6:46 of code 6:47 line 1 now execute this line and then 6:50 line 2 says 6:52 if the age is greater than 18 then 6:56 we're going to execute lines 3 and 4 6:58 otherwise 6:59 we're going to execute lines six and 7:02 seven 7:03 so this program doesn't necessarily 7:05 follow strictly in sequence from line 7:07 zero through to seven there's a chance 7:10 here the program may branch and jump 7:14 around 7:16 so we're going to pretend that this 7:17 program has been loaded into memory 7:20 each line of code on the left here has 7:23 ended up 7:24 as a location in memory now this is not 7:27 strictly how this would happen in this 7:28 one-to-one way 7:29 but for the purpose of example it's 7:31 absolutely fine 7:35 so the program counter starts by 7:37 pointing to memory address zero 7:39 and we fetch the first instruction 7:41 decode it and execute it 7:44 it then updates and tells us the next 7:47 instruction 7:48 is zero zero zero one because remember 7:50 the program counter is being incremented 7:52 so we fetch it decode it and we execute 7:55 line one of our program 7:59 we then fetch line two which in binary 8:01 is one 8:02 zero 8:06 now at this point depending on what 8:10 happens during the execution 8:11 of line two the program may be required 8:15 to fetch line three from memory or 8:18 line five from memory 8:25 so let's look at how this actually works 8:27 because we've said the program counter 8:28 simply gets incremented 8:31 well in the current instruction register 8:33 we have an instruction with the op code 8:36 0 1 1 0. 8:41 now when we look this up in the decode 8:43 unit we discover that this 8:45 code means branch always 8:51 this replaces the value held in the 8:54 program counter 8:56 with the contents of the operand that's 8:58 the second part of the instruction 9:01 from the current instruction register so 9:03 this case 9:04 one zero zero one 9:09 now when the next fetch cycle begins the 9:12 program counter is obviously checked 9:14 and as its contents have been previously 9:16 updated to a new memory location 9:19 and not simply incremented the program 9:22 effectively is able to jump 9:24 around memory 9:28 so having watched this video you should 9:30 be able to answer the following key 9:32 question 9:33 how does a cpu work 9:39 okay so let's um answer the question we 9:41 posed 9:42 earlier what did that program actually 9:48 do 9:50 so this is the first fetch to code 9:53 execute cycle 9:55 and this is the one that we ran through 9:57 in detail earlier 9:58 it effectively loaded the contents of 10:01 the memory 10:02 stored at location location0101 10:05 into the accumulator in other words 10:08 the dna number 3 is moved 10:11 from memory into the cpu 10:18 we then proceed onto the second fetch 10:20 decode execute cycle 10:23 now this one adds the contents of memory 10:27 located at 0 1 1 0 10:30 to the current contents of the 10:32 accumulator 10:34 so in other words the dna number one 10:38 because that's what's stored at address 10:40 zero one one zero 10:43 is added to the number three that was in 10:45 the accumulator 10:46 the results are stored back over the 10:48 accumulator 10:49 so effectively we've done three plus one 10:53 equals four 10:58 the third fetch to code execute cycle 11:00 stores the contents which are in the 11:02 accumulator 11:03 into memory location zero one one one 11:07 and that's because the op code the first 11:09 part of this current instruction 11:10 zero zero one one is the command to 11:13 store when we look it up in the decoder 11:15 unit 11:16 so in other words the result of the 11:17 previous calculation three plus one 11:19 equals four 11:20 is now written back into main memory 11:28 the fourth fetch decode execute cycle 11:30 outputs the contents of the accumulator 11:33 remember they were copied into main 11:34 memory but they're still held in the 11:35 accumulator 11:37 so in this simple abstraction the number 11:40 four is now 11:41 output to the user so they can see the 11:43 result of the calculation 11:49 the fifth and final fetch code execute 11:51 cycle 11:52 brings a halt to the current program 11:58 so this very simple program which has 12:01 five 12:02 fetch decode execute cycles has 12:04 performed the calculation 12:06 three plus one is then stored the result 12:09 in main memory 12:10 and displayed the result four to the 12:12 user 12:13 and in a high-level language this may 12:15 look something very similar to the 12:17 following two lines of code 12:20 sum variable equals num1 plus num2 12:24 print sum to the user 12:27 so you can start to get an appreciation 12:29 here of how the high level code you 12:32 write actually ends up being fetched 12:34 decoded 12:35 and executed inside a processor 12:38 of course your processor is doing 12:40 billions and billions of these 12:42 operations a second 12:43 which when you think about it is really 12:45 very impressive 12:52 [Music] 13:03 you. make 10 questions for a standerd of a level

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