1 Cybersecurity Challenge Is Inadequate Identification of Key RisksJuly 13, 202240% of chief security officers say their organizations are not well prepared for today’s rapidly evolving threat landscapeNews summary25% increase in 2021 in material cybersecurity breaches – those generating a large loss, compromising many records, or having a significant impact on business operationsTop 4 causes of breaches are avoidable, according to cybersecurity researchers48% of organizations with no breaches in 2021 were risk-based cybersecurity leadersSAN JOSE, Calif.–(BUSINESS WIRE)–Skybox Security released new findings from the largest cybersecurity benchmarking study of global executives. The research reveals that traditional security approaches that rely on reactive, detect-and-respond measures and tedious manual processes can’t keep pace with the volume, variety, and velocity of current threats. As a result, 27% of all executives and 40% of chief security officers (CSOs) say their organizations are not well prepared for today’s rapidly shifting threat landscape.“What\On average, organizations experienced 15% more cybersecurity incidents in 2021 than in 2020. In addition, “material breaches” — defined as “those generating a large loss, compromising many records, or having a significant impact on business operations” — jumped 24.5%.Human errorUnknown assets”What’s notable about this list is that all of these conditions result from mistakes or manual processes inside organizations — which means they are all in principle avoidable,” said Ran Abramson, threat intelligence analyst, Skybox Research Lab. “The clear implication is that, however pernicious external threats have become, cybersecurity teams still have the power to repel them. And that’s the good news: With the right practices and tools – including automation to maximize efficiency and get the most out of limited staff – breaches can be prevented.”The study surveyed executives and analyzed the cybersecurity investments, practices, and performance of 1,200 companies and public-sector organizations in 16 countries and a wide range of industries. It’s the largest cybersecurity benchmarking study with C-level decision-makers ever undertaken. The research findings uncovered that conventional cybersecurity approaches are falling short, and organizations that shift to modern, risk-based strategies are more successful in preventing breaches.Though organizations, on average, saw a significant uptick in incidents and material breaches in the past two years, a distinct subset had few or no breaches at all. So, what sets these exceptional organizations apart? The researchers found that firms with fewer breaches were different from the rest of the pack in two fundamental respects:Organizations that prevented breaches ranked higher in cybersecurity progress as measured by the NIST framework. The framework, developed by the National Institute of Standards and Technology, provides guidelines that help companies evaluate and improve their cybersecurity maturity in activities such as detecting and responding to incidents.Beyond the NIST framework, organizations with no breaches took what the researchers call “a risk-based approach” to cybersecurity. Forty-eight percent of organizations with no breaches in 2021 had implemented risk-based cybersecurity management strategies. They also performed better in key cybersecurity metrics: 46% were top performers in time to respond to a breach, and 50% were top performers in time to respond.Looking more closely at the ingredients of a risk-based approach and the specific practices that distinguish risk-oriented organizations from their less proficient peers, the benchmark study found that risk-based leaders excelled in key areas beyond the NIST framework, including:Attack simulationRisk scoringResearch (threat intelligence)Technology assessments and consolidation”You must take a risk-based approach because you can’t secure everything a hundred percent. There are a lot of questions to ask: What is the business of the business? What does the risk profile look like? What are the threats? What are the implications? And what is the governance process an organization goes through to make risk-based decisions?” stated Gary McAlum, Board Director, National Cybersecurity Center.The business impact of successful risk-based security management — versus the old status-quo, detect-and-respond approach — is measured in this research. By preventing or mitigating breaches, risk-based methods could have saved companies millions of dollars annually and prevent untold damage to reputation, customer trust, company morale, and market standing.”The cybersecurity industry is witnessing a paradigm shift in cyber risk. To prevent breaches, CISOs must make a strategic shift – from the traditional volume play of identifying vulnerabilities and merely adhering to cybersecurity frameworks – to taking a strategic risk-based view of reducing actual exposure,” said Gidi Cohen, CEO and Founder, Skybox Security. “At the board level, leaders want to understand their risk profile rather than how many vulnerabilities were patched each month. CISOs need to validate and report on how they’re taking measurable, proactive steps to reduce risk systematically and reduce the financial impact a breach could have on their company.”Read the full paper, underpinned by research conducted by ThoughtLab: Reduce cybersecurity risk with security posture managementOver 500 of the world’s largest and most security-conscious enterprises rely on Skybox for the insights and assurance required to stay ahead of dynamically changing attack surfaces. Our Security Posture Management Platform delivers complete visibility, analytics, and automation to quickly map, prioritize, and remediate vulnerabilities across your organization. The vendor-agnostic solution intelligently optimizes security policies, actions, and change processes across all corporate networks and cloud environments. With Skybox, security teams can now focus on the most strategic business initiatives while ensuring enterprises remain protected.https://www.skyboxsecurity.com/© 2022 Skybox Security, Inc. All rights reserved. Skybox Security and the Skybox Security logo are either registered trademarks or trademarks of Skybox Security, Inc., in the United States and/or other countries. All other trademarks are the property of their respective owners. Product specifications subject to change at any time without prior notice.ContactsAshley NakanoCorporate Communications Directormedia-relations@skyboxsecurity.com

2022 cybersecurity forecasts predict growth, emphasizing resilienceJuly 5, 2022 by Louis ColumbusGartner predicts end-user spending for the information security and risk management market will grow from $172.5 billion in 2022 to $267.3 billion in 2026, attaining a compound annual growth rate (CAGR) of 11%.Cybersecurity Ventures expects global cybercrime costs to grow by 15% per year over the next five years, reaching $10.5 trillion annually by 2025, up from $3 trillion in 2015.AI adoption and sales within cybersecurity platforms are projected to grow from $10.5 billion in 2020 to $46.3 billion in 2026, attaining a 28% CAGR in the forecast period.   Why cybersecurity spending Is resilient Cybersecurity tech stacks must close the gaps that leave human and machine endpoints, cloud infrastructure, hybrid cloud and software supply chains vulnerable to breaches. The projected fastest-growing areas of cybersecurity reflect how urgent the issue of streamlining cybersecurity tech stacks is. Seventy-five percent of executives report too much complexity in their organizations, leading to concerning cybersecurity and privacy risks.Secure access service edge (SASE) and extended detection and response (XDR) are integration-based approaches to closing the gaps in cybersecurity tech stacks. They’re proving effective in minimizing risks while providing CISOs, CIOs and their teams the visibility and control they need across all systems, endpoints and threat surfaces. Every organization should anticipate that the attack surfaces they’re protecting will grow faster than forecasted, and that more human and machine identities will see attempts to compromise them than security and IT teams expect. In addition, more privileged access credentials will be stolen than a given business expects. These combined effects make cybersecurity spending one of the most resilient enterprise software. The following is a curated list of the most recent cybersecurity forecasts and market estimates: 69% of organizations predict a rise in cyber spending in 2022 compared to 55% last year. More than a quarter (26%) predict cyber spending hikes of 10% or more; only 8% said that in 2021. PwC says their survey of senior management shows that organizations expect risks to continue increasing. In addition, more than 50% expect a surge in reportable incidents next year above 2021 levels, according to PwC’s 2022 Global Digital Trust Insights Survey. Sixty-nine percent of organizations plan to increase cybersecurity spending this year, driven by the business case of securing increasingly complex digital infrastructures their businesses rely on.Global cybersecurity insurance spending is projected to grow from $12.47 billion in 2022 to $37.14 billion by 2030, reaching a 21.8% CAGR. Digital-first business models and full-scale digital transformation projects combined with the exponential increase in ransomware attacks drive enterprises to spend more on cybersecurity insurance. Insurance carriers are pushing back against ransom payments, citing cyberattackers deliberately target their largest clients for quick, lucrative payouts. Last year, global insurance provider AXA decided no longer to pay ransomware payments in France. McKinsey and Company’s recent article on cybersecurity trends reflects how much enterprises are willing to pay for cyber insurance, predicting a 21% CAGR between 2022 and 2025 in cyber insurance. McKinsey also provides $101.5 billion that will be spent with service providers by 2025, as the enterprise seeks outside expertise to streamline complex cybersecurity tech stacks. This is all according to McKinsey & Company’s report titled Cybersecurity trends: Looking over the horizon. McKinsey & and Company predicts organizations will rely more on service providers, and 85% of small and midsize businesses will invest more in cybersecurity, given the exponential increase in ransomware attacks and social-engineered attacks.The global cybersecurity software, services, and systems market is predicted to grow from $240.27 billion in 2022 to $345.38 billion by 2026, attaining a 9.5% CAGR, according to Statista. The major factors fueling the cybersecurity market include the rising frequency and sophistication of target-based cyberattacks, increasing demand for the cybersecurity mesh, and growing demand for cyber-savvy boards. Worldwide secure access service edge (SASE) spending will reach $14.7 billion by 2025. Gartner predicts that global spending on SASE will grow at a 36% CAGR between 2020 and 2025, far outpacing global spending on information security and risk management. In 2022, global SASE spending will reach $6.8 billion, growing to $9.1 billion in 2023. Gartner says a key assumption of their forecasts is that enterprises will prefer paying for SASE using a subscription model over perpetual licenses. Leading SASE vendors include Cato Networks, Fortinet, Palo Alto Networks, Versa Networks, VMware, Zscaler and others. The worldwide security software market grew 22.5% in 2021, reaching $61.38 billion in revenue. The top five market segments by market share were: endpoint protection platform (enterprise), consumer security software, access management, security information and event management (SIEM), and identity governance and administration. Cloud workload protection platform spending grew 37.8% between 2020 and 2021, and spending on access management solutions jumped 33.5%. Cloud Access Security Brokers (CASB) spending grew the third fastest at 32.7%, followed by endpoint protection platforms, which grew 25.9%. Together, these five segments accounted for 46.1% of the total market size for security software. This is according to Gartner’s report: Market Share: Security Software, Worldwide, 2021.  77% of C-level executives plan to increase their zero trust spending over the next 12 months. The Cloud Security Alliance (CSA) recently published its latest report, CISO Perspective and Progress in Deploying Zero Trust. The study is based on interviews with security and risk management professionals and C-level executives who provided insights into current and future zero trust deployment plans. It found that 80% of C-level executives cite zero trust as a priority for their organizations, and 94% are implementing zero-trust strategies. Ericom’s Zero Trust Market Dynamics Survey found that 80% of organizations plan to implement zero-trust security in less than 12 months, and 83% agree that zero trust is strategically necessary for their ongoing business. CISOs must remove trust from tech stacks and define their unique strategy to adopt the framework. Cloud Security Alliance’s recent survey shows zero trust is gaining momentum across enterprises, with most senior management respondents saying their investment levels will increase.The global endpoint security market is predicted to reach $31.1 billion by 2026 from $17.4 billion in 2021, attaining a 12.3% CAGR. Frost &  Sullivan’s most recent endpoint security forecast reflects the growing need for resilient endpoint protection platforms that can withstand multiple attacks and capitalize on AI and machine learning to predict potential breach attempts. In addition, self-healing endpoints are a catalyst driving the growth of the endpoint security market. However, 55% of cybersecurity professionals estimate that more than 75% of endpoint attacks can’t be stopped with their current systems, based on a survey by Tanium. 84% of C-level executives agree that cyber resilience is considered a business priority for their organizations in 2022. The World Economic Forum’s (WEF) cybersecurity survey found that 81% of C-level executives believe digital transformation is the main motivator for improving cyber resilience. WEF’s findings reflect many other surveys that cite the accelerating pace of digitalization due to the COVID-19 pandemic, and the shift in our working habits is pushing cyber resilience to a higher priority today. In addition, 87% of executives plan to improve their organization’s cyber resilience by strengthening policies, processes, and standards for engaging and managing third parties. Zero-trust network access (ZTNA) spending is on pace to reach $823.1 million in 2022, reaching $1.973 billion by 2026, growing at a 19.1 CAGR. Gartner predicts that global demand for ZTNA-based systems, solutions, and platforms will grow faster than the global information security and risk management market, eclipsing the overall market growth rate by 8%. Of the five years included in the forecast, 2023 sees the most rapid growth, with ZTNA spending rising over $1 billion for the first time. Gartner is also seeing a 60% year-over-year growth rate in ZTNA adoption. Their 2022 Market Guide for Zero Trust Network Access is noteworthy in providing insights into all CISOs need to know about zero-trust security.$5.9 billion was invested in cybersecurity startups in Q1, 2022. Crunchbase says funding in Q1 of this year was nearly a 50% increase from Q1, 2020. Cybersecurity venture funding is on pace to break the $20 billion invested in cybersecurity in 2021. A total of 189 funding deals were announced in Q1, 2022, down slightly from Q4, 2021, which recorded 232 funding events. Cybersecurity continues to show resilience as venture capitalists and private equity investors continue to finance startups. While cybersecurity startups successfully obtained funding rounds through Q1/22, Q2 and Q3/22 will reflect just how resilient the industry continues to prove itself to be.Spending will continue despite economic uncertainty  Cybersecurity market estimates reflect continued spending despite global economic uncertainty. No business can afford to be down during challenging economic times. Therefore, the forecasts reflect a resilient outlook for the industry. Reducing risks, ensuring continued operations and managing identities and privileged access credentials are essential now.Additional reading:Cybercrime Magazine, 2022 Cybersecurity Almanac: 100 Facts, Figures, Predictions And Statistics, January 19, 2022Cloud Security Alliance, CISO Perspectives and Progress in Deploying  Zero Trust. June 3, 2022 Economist Intelligence Unit and Pillsbury, AI and Cybersecurity: Balancing Innovation, Execution and Risk, September 9, 2021. Forrester, The Forrester Wave™: Endpoint Detection And Response Providers, Q2 2022, April 6, 2022 (Reprint courtesy of CrowdStrike) Gartner, Forecast: Information Security and Risk Management, Worldwide, 2020-2026, 2Q22 Update, June 30, 2022. Client Access Required. Gartner, Forecast Analysis: Secure Access Service Edge, Worldwide, July 27, 2021. Client Access Required  KuppingerCole, Endpoint Protection Detection and Response, May 12, 2022McKinsey and Company, Cybersecurity trends: Looking over the horizon, March 10, 2022 McKinsey and Company, Giving developers a leading role in cybersecurity Podcast, June 14, 2022PwC, 2022 Global Digital Trust Insights Survey, opt-in, 31 pp., pdf, free. World Economic Forum, Global Cybersecurity Outlook 2022. Published January 18, 2022.  

ARTIFICIAL INTELLIGENCE AND DATA SCIENCE TRAINING EVENT

Artificial intelligence introduction

For Theory Section: Some knowledge in algebra and calculus.
For Hands-On Section: Gmail account to use Google Colab and Google Drive. Basic programming knowledge using Python 3
If you have no programming background, you may still take the course. You can still benefit from the mathematical understanding of the subject. You can play with all the code that is provided.
All codes are provided for you to download.
No software needed to install on your computer. We will use Google Colab with step-by-step set up instructions included in the course.
Description
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.

Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.

The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.

Hand-on examples are available for you to download.

Please watch the first two videos to have a better understanding of the course.



TOPICS COVERED



What is Machine Learning?



Linear Regression

Steps to Calculate the Parameters

Linear Regression-Gradient Descent using Mean Squared Error (MSE) Cost Function



Logistic Regression: Classification

Decision Boundary

Sigmoid Function

Non-Linear Decision Boundary

Logistic Regression: Gradient Descent

Gradient Descent using Mean Squared Error Cost Function

Problems with MSE Cost Function for Logistic Regression

In Search for an Alternative Cost-Function

Entropy and Cross-Entropy

Cross-Entropy: Cost Function for Logistic Regression

Gradient Descent with Cross Entropy Cost Function

Logistic Regression: Multiclass Classification



Introduction to Neural Network

Logical Operators

Modeling Logical Operators using Perceptron(s)

Logical Operators using Combination of Perceptron

Neural Network: More Complex Decision Making

Biological Neuron

What is Neuron? Why Is It Called the Neural Network?

What Is An Image?

My “Math” CAT. Anatomy of an Image

Neural Network: Multiclass Classification

Calculation of Weights of Multilayer Neural Network Using Backpropagation Technique

How to Update the Weights of Hidden Layers using Cross Entropy Cost Function



Hands On

Google Colab. Setup and Mounting Google Drive (Colab)

Deep Neural Network (DNN) Based Image Classification Using Google Colab. & TensorFlow (Colab)



Introduction to Convolution Neural Networks (CNN)

CNN Architecture

Feature Extraction, Filters, Pooling Layer

Hands On

CNN Based Image Classification Using Google Colab & TensorFlow (Colab)



Methods to Address Overfitting and Underfitting Problems

Regularization, Data Augmentation, Dropout, Early Stopping

Hands On

Diabetes prediction model development (Colab)

Fixing problems using Regularization, Dropout, and Early Stopping (Colab)



Hands On: Various Topics

Saving Weights and Loading the Saved Weights (Colab)

How To Split a Long Run Into Multiple Smaller Runs

Functional API and Transfer Learning (Colab)

How to Extract the Output From an Intermediate Layer of an Existing Model (Colab), and add additional layers to it to build a new model.

Who this course is for:
Who is this course for? Almost for everyone. Machine Learning is not a topic for one single profession. Machine Learning (along with neural networks) is an immensely powerful tool that may help you to find solutions to some of the problems that one may not know how to solve otherwise. Try this course and see if it gives you better insight to address some of the problems you are working on.
People from a diverse range of professions may find this knowledge useful in their own profession.
Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.
The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.
Please watch the first two videos to have a better understanding of the course.

Artificial Intelligence Tutorial
Artificial Intelligence Tutorial | AI Tutorial
The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc.

Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts.

What is Artificial Intelligence?
In today’s world, technology is growing very fast, and we are getting in touch with different new technologies day by day.

Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc.

AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human.

Introduction to AI
Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines “man-made,” and intelligence defines “thinking power”, hence AI means “a man-made thinking power.”

So, we can define AI as:

“It is a branch of computer science by which we can create intelligent machines which can behave like a human, think like humans, and able to make decisions.”
Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems

With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI.

It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans.

Why Artificial Intelligence?
Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI:

With the help of AI, you can create such software or devices which can solve real-world problems very easily and with accuracy such as health issues, marketing, traffic issues, etc.
With the help of AI, you can create your personal virtual Assistant, such as Cortana, Google Assistant, Siri, etc.
With the help of AI, you can build such Robots which can work in an environment where survival of humans can be at risk.
AI opens a path for other new technologies, new devices, and new Opportunities.
Goals of Artificial Intelligence
Following are the main goals of Artificial Intelligence:

Replicate human intelligence
Solve Knowledge-intensive tasks
An intelligent connection of perception and action
Building a machine which can perform tasks that requires human intelligence such as:
Proving a theorem
Playing chess
Plan some surgical operation
Driving a car in traffic
Creating some system which can exhibit intelligent behavior, learn new things by itself, demonstrate, explain, and can advise to its user.
What Comprises to Artificial Intelligence?
Artificial Intelligence is not just a part of computer science even it’s so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc.

To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline:

Mathematics
Biology
Psychology
Sociology
Computer Science
Neurons Study
Statistics
Introduction to AI
Advantages of Artificial Intelligence
Following are some main advantages of Artificial Intelligence:

High Accuracy with less errors: AI machines or systems are prone to less errors and high accuracy as it takes decisions as per pre-experience or information.
High-Speed: AI systems can be of very high-speed and fast-decision making, because of that AI systems can beat a chess champion in the Chess game.
High reliability: AI machines are highly reliable and can perform the same action multiple times with high accuracy.
Useful for risky areas: AI machines can be helpful in situations such as defusing a bomb, exploring the ocean floor, where to employ a human can be risky.
Digital Assistant: AI can be very useful to provide digital assistant to the users such as AI technology is currently used by various E-commerce websites to show the products as per customer requirement.
Useful as a public utility: AI can be very useful for public utilities such as a self-driving car which can make our journey safer and hassle-free, facial recognition for security purpose, Natural language processing to communicate with the human in human-language, etc.
Disadvantages of Artificial Intelligence
Every technology has some disadvantages, and thesame goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI:

High Cost: The hardware and software requirement of AI is very costly as it requires lots of maintenance to meet current world requirements.
Can’t think out of the box: Even we are making smarter machines with AI, but still they cannot work out of the box, as the robot will only do that work for which they are trained, or programmed.
No feelings and emotions: AI machines can be an outstanding performer, but still it does not have the feeling so it cannot make any kind of emotional attachment with human, and may sometime be harmful for users if the proper care is not taken.
Increase dependency on machines: With the increment of technology, people are getting more dependent on devices and hence they are losing their mental capabilities.
No Original Creativity: As humans are so creative and can imagine some new ideas but still AI machines cannot beat this power of human intelligence and cannot be creative and imaginative.
Prerequisite
Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily:

Any computer language such as C, C++, Java, Python, etc.(knowledge of Python will be an advantage)
Knowledge of essential Mathematics such as derivatives, probability theory, etc.
Audience
Our AI tutorial is designed specifically for beginners and also included some high-level concepts for professionals.

Problems
We assure you that you will not find any difficulty while learning our AI tutorial. But if there any mistake, kindly post the problem in the contact form.

Audience: The Academy is primarily for members who work in industry and need to understand new technical information quickly so they can apply it to their work. At the completion of the IEEE Academy on Artificial Intelligence, the learner will be able to demonstrate their new knowledge and will earn a certificate.Publication Year: 2022

DescriptionYou are about to embark on an exciting journey learning about the information revolution and the incredible world of computers. This course is very practical and applicable. It focuses on teaching you skills you can use. These skills include not only specific hands-on skills, like “right-clicking” and taking “screenshots,” but also skills such as keeping yourself safe online, not texting while you’re driving, and what to look for when buying computers (just to mention a very small fraction of the skills this course will teach you). This is a university level course designed to introduce individuals to the world of computers, so it is rich in its depth and breadth of content. I have taught this course for over a decade and have refined it to be incredibly amazing and awesome. You are going to love this course and it will forever change your life. You will gain skills in this course which you will use for the rest of your life and which will make your life easier. Knowing how to use computers is essential in our day-and-age. This course will give you the skills you need to use computers well. Presented with high-quality video lectures, this course will visually show you how to easily do everything with computers. This is just some of what you will learn in this course: Learn the basic principles of hardware including circuits, coding schemes, binary, the five generations of computers, Moore’s Law, IPOS, registers, cache, RAM, magnetic storage, optical storage, solid-state storage, ROM, BIOS, the motherboard, buses, and the CPU.Learn how to operate a computer including a vast array of hands-on skills – just to mention a few for example: managing files, backing –up files, right clicking, taking screenshots, determining your computer’s properties, upgrading your computer, changing settings on your computer.Learn how to use word processing software including the creation of a title page, document sections, headers and footers, styles, an automatically generated table of contents, the insertion of images, references, and the insertion of an automatically generated citation of works referenced.Learn how to use spreadsheet software including formulas, functions, relative references, absolute references, mixed references, and the creation of a graph or chart.Learn how to use video editing software including adding credits and transitions then publishing that video to a video hosting website such as YouTube.Learn how to use databases including table creation, the setting of a primary key, the establishment of table relationships, the setting of referential integrity, and the creation and execution of a query.Learn how to use presentation software to more effectively give presentations.Learn to do some simple programing including designing, coding, testing, debugging, and running a program.Learn about the world wide web including sending email, conducting searches , having familiarity with online educational resources such as Khan Academy, and having an awareness of online “cloud computing” tools such as Google Word Processing, Google Spreadsheets, and the many other online tools offered by Google.Learn about application software and system software including operating systems, utilities, and drivers.Learn about networks including architecture, topology, firewalls, security, wireless networks, and securing wireless networks.Learn about the Internet, the World Wide Web, censorship, the digital divide, net neutrality, differing legal jurisdictions, website creation, multimedia, social media, and eCommerce.Learn about information systems, systems development, and the systems development life cycle.Learn about program development, programming languages, and the program development life cycle.Learn about databases including table creation, primary keys, relationships, referential integrity, queries, and structured query language.Learn about privacy and security issues related to computers.Learn about robots and artificial intelligence including the Turing test.Learn about intellectual property including patents, trademarks, copyrights, and the creative commons.Learn about ethics and ethical issues relating to the use of technology.Learn about health ramifications of using computers including repetitive stress injury, carpal tunnel syndrome, and ergonomics.Learn about e-Waste and other environmental concerns related to technology.

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