648 Computing & IT courses

Computer Science GCSE Syllabus The GCSE Computer Science Tutor Syllabus is designed to provide tutors in England with a comprehensive framework for teaching the GCSE Computer Science curriculum effectively. This syllabus aims to equip tutors with the necessary knowledge and skills to support students in their understanding and application of core computer science concepts.  Module 1: Introduction to Computer Science - Overview of computer science and its relevance in today's world - Understanding the components of a computer system - Introduction to algorithms and problem-solving techniques - Exploration of programming languages and their uses Module 2: Computer Hardware - Understanding the main components of a computer system, including CPU, memory, and storage devices - Exploring input and output devices and their functionalities - Understanding the role of operating systems and software in computer systems Module 3: Software Development - Introduction to programming concepts and languages (e.g., Python or Java) - Understanding variables, data types, and operators - Building algorithms and logical reasoning skills - Introduction to flowcharts and pseudocode - Implementation of simple programs and debugging techniques Module 4: Data Representation - Understanding binary, hexadecimal, and denary number systems - Representation of text, images, and sound using binary - Introduction to data compression and encryption techniques Module 5: Computer Networks - Understanding the basics of computer networks, including LAN, WAN, and the Internet - Introduction to network topologies, protocols, and security - Exploring the impact of digital communication on society Module 6: Cybersecurity and Ethical Issues - Understanding the importance of cybersecurity and data protection - Introduction to common threats and vulnerabilities - Exploring ethical issues related to computer science, such as privacy and intellectual property rights Module 7: Algorithms and Programming Techniques - Advanced programming concepts, including conditionals, loops, and functions - Introduction to sorting and searching algorithms - Exploring data structures, such as arrays and lists Module 8: System Architecture - Understanding the structure and function of a CPU - Introduction to memory hierarchy and cache - Exploring the Von Neumann architecture and its limitations Module 9: Computational Thinking and Problem Solving - Advanced problem-solving techniques using computational thinking - Introduction to algorithms for complex problems - Exploring algorithmic efficiency and optimization techniques Module 10: Exam Preparation and Revision - Reviewing key concepts covered throughout the syllabus - Practicing past exam questions and providing guidance on exam techniques - Supporting students with exam preparation strategies Please note that the duration and depth of each module can vary depending on the level of expertise required and the specific needs of the learners. Additionally, it's important to adapt the curriculum to the learners' proficiency levels, whether they are A Level/GCSE students or adult learners with different experience levels.

We use 'Scratch' for computer coding. Scratch is the world’s largest coding community for children and a coding language with a simple visual interface that allows young people to create digital stories, games, and animations. Scratch is designed, developed, and moderated by the Scratch Foundation, a nonprofit organization. Scratch promotes computational thinking and problem solving skills; creative teaching and learning; self-expression and collaboration; and equity in computing. Scratch is designed especially for ages 8 to 16, but is used by people of all ages. Millions of people are creating Scratch projects in a wide variety of settings, including homes, schools, museums, libraries, and community centers.  The ability to code computer programs is an important part of literacy in today’s society. When people learn to code in Scratch, they learn important strategies for solving problems, designing projects, and communicating ideas. 

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A series of hourly small group sessions where we look at different aspects of Excel Duration: 1 hr sessions Alternatively, we can do you one-to-one sessions, either via virtual classroom or in person at our training course. These are £30 per hour, or £125 for 5-hour blocks.

Software comes in a variety of guises - application software, firmware, middleware, system software. Increasingly, however, it doesn't necessarily present that way, especially as the boundaries between software, data and source code are becoming more and more blurred. And as software becomes more complex and more difficult to disentangle, so it becomes harder to manage and to value. But as it becomes more integral to every aspect of a business, so it is ever more important to keep on top of the technical, legal and commercial issues that arise, issues such as: To address these issues, organisations need a process for evaluating their current situation from all perspectives and for identifying the key actions they need to take to ensure holistic management of their software. This very practical programme will help set your organisation on the right path. Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. This programme is designed to give you a deeper understanding of: The technical, legal and commercial risks associated with software development, procurement, use and commercial exploitation The most appropriate processes and responsibilities for managing those risks Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. 1 Software business model What is the software business model? What options exist? Has the software business model been thoroughly reviewed to ensure its viability? This means fully understanding the market opportunity, the business environment and customer and end-user expectations. 2 Technology What are the technologies? How has the technology selection been validated considering the competitiveness, structure, and potential for future innovation? 3 UI and UX What is the UI and UX? How to best articulate this? Has the user interface and user experience been studied from both a subjective and objective view to give insight into customer behaviour? 4 Legal framework / commercial aspects Has the necessary legal framework or commercial aspects that may impact upon use or operation of the software been understood and risks identified and mitigated? 5 Software development What is the software development process? Are both the business management and development team's processes resilient in order to improve the company's capability and the maturity of the software? 6 Software quality What is quality? What are the metrics around software quality? What is the maturity level, based around a qualitative and quantitative assessment? 7 Intellectual property associated What IP should be considered when it comes to software? Does the company understand both the intellectual property risks and potential opportunities associated with this software? 8 Security What does software security mean in this context? How is it being addressed? 9 An holistic approach Review of roles and responsibilities to ensure appropriate management and protection

Boost your cyber security skills with Edoxi's Cyber Security courses in Dubai. Learn how to safeguard networks and data from cyber threats. With Edoxi’s cyber security courses upskill your knowledge in the areas such as Security Engineering, System Security, Software Security, cyberattacks, Security Software, Software Testing, and more.

Full Stack Development encompasses the complete creation of end-to-end development of both the front-end and back-end of an application. LSET Bridges The Gap Between Education And Employment

Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

Overview This 1 day course focus on comprehensive review of the current state of the art in quantifying and pricing counterparty credit risk. Learn how to calculate each xVA through real-world, practical examples Understand essential metrics such as Expected Exposure (EE), Potential Future Exposure (PFE), and Expected Positive Exposure (EPE) Explore the ISDA Master Agreement, Credit Support Annexes (CSAs), and collateral management. Gain insights into hedging strategies for CVA. Gain a comprehensive understanding of other valuation adjustments such as Funding Valuation Adjustment (FVA), Capital Valuation Adjustment (KVA), and Margin Valuation Adjustment (MVA). Who the course is for Derivatives traders, structurers and salespeople xVA desks Treasury Regulatory capital and reporting Risk managers (market and credit) IT, product control and legal Quantitative researchers Portfolio managers Operations / Collateral management Consultants, software providers and other third parties Course Content To learn more about the day by day course content please click here To learn more about schedule, pricing & delivery options, book a meeting with a course specialist now

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