4017 Courses delivered Online

Microsoft Project Orange Belt® 2016: In-House Training This workshop gives participants a full insight into creating effective schedules using Microsoft® Project 2016, allowing you to estimate, coordinate, budget, staff, and control projects and support other users. This workshop gives participants a full insight into creating effective schedules using Microsoft® Project 2016, allowing you to estimate, coordinate, budget, staff, and control projects and support other users. This workshop provides the eBook Dynamic Scheduling with Microsoft® Project 2013, and is kept up to date with the framework of knowledge outlined by the Project Management Institute's PMBOK® Guide and the Practice Standard for Scheduling. What you Will Learn You'll learn how to: Understand what's new in Project 2016 Explain where Project 2016 fits in the Microsoft® EPM solution Initialize Project 2016 to start project planning Create a well-formed project schedule Understand task types and the schedule formula Assign resources and costs to tasks Analyze resource utilization and optimize the schedule Set a baseline and track progress Create and manage project reports Customize views and fields Apply Earned Value Management Understand the basics of managing multiple projects Getting Started Introductions Course structure Course goals and objectives Concepts of Project Management Getting Started with Project 2016 Project 2016: What's New and General Overview Setting Up a New Project Schedule (templates, options, save, etc.) Setting the Project Calendar Entering Tasks The planning processes Importing tasks (Word, Excel, SharePoint) Synchronizing with SharePoint Tasks List Creating and managing the WBS (include tasks, manually scheduled tasks, summary tasks, milestones, and custom WBS coding) Entering Estimates Tendencies in estimating The rolling wave approach Entering duration and work estimates Types of tasks Entering Dependencies The principle of dynamic scheduling Choosing the right Type of dependency Entering dependencies in Project 2016 Different applications of dependencies Entering Deadlines, Constraints, and Task Calendars Use of deadlines and constraints Entering deadlines and constraints in Project 2016 Entering Resources and Costs Types of resources Entering resources in Project 2016 Calendars and resources Entering costs in Project 2016 Entering Assignments Assignments in a schedule Assignments and task types Entering assignments in Project 2016 Assignments, budgets, and costs Optimizing the Schedule The critical path method (CPM) and the resource critical path (RCP) Resource leveling Optimizing time, cost, and resources in Project 2016 Updating the Schedule The baseline Updating strategies and situations Prepare the status and forecast report in Project 2016 Reporting Using reports Using Visual Reports Formatting and printing in Project 2016 Customizing fields in Project 2016 Earned Value Management (EVM) Overview of EVM Applying EVM with Project 2016 Evaluating the Project Evaluating the project performance Project benefits and results Templates for future projects Summary and Next steps Tools and checklist Best practices checklist Certification options

Understanding the basics of Apache Maven through practical demonstration

An intermediate-level training that will help you learn how hackers find SQL injections with Sqlmap, web application security testing with Google Hacking, fuzzing with Burp Suite, and exploiting race conditions with OWASP ZAP. You will learn how to use these tools in your penetration testing projects and use them to protect your web applications.

Dive into the comprehensive world of Entity Framework Core with this detailed course, designed to equip you with the skills to efficiently use EF Core in .NET applications.

In this course, you will learn how to create your own Azure Functions apps and visualize how full applications can be built using Azure Functions. We will also explore the tools needed to support development, both locally and in the Azure portal, and explore the different triggers, bindings, and deployment models that are possible.

⏰️ Available from 18th September at 7pm Price: £25 (non-refundable) Watch our pre-recorded webinar of ST4 and ST1 Applications and Interviews! This a recording from last year's highly successful webinar. 📝 Sign up to listen to a breakdown of the scoring matrix, how best to boost your portfolio to get a national training number and all the interview tips we can give you! Hear from National trainees who have been through the process, their advice, hints and tips. 🚨 Sign up and get access to the Live Q&A where we will talk about any updates and changes to the 2025 scoring matrix (on release) - delivered by current and recently appointed ST4 and ST1 trainees. This session will give you the best opportunity to discuss in real time any changes that come out! Please ensure you check all mail folders, including spam/junk folders, and add 'notifications@system.cademy.co.uk' Cademy to the list of 'safe senders', to ensure you receive all future emails. 

Description: Computer Specialist Diploma Course is intended for those who wants to gain computer skills and knowledge. It has modules which will give you all the ins and outs of operating on a personal computer and its broad applications. First of all, it will cover all the fundamental computing terms, input, output, and storage devices. You will also learn about the operating systems, interface and its applications. As you step up through the hierarchy of the basic to intermediate to expert levels, you will be exposed to several contents which will teach you about networking, internet basic, ergonomics, safeguarding your data from malware, customization, etc. And the end, you will have a profound knowledge about computers. Learning Outcomes: Realise the common computing concepts Figure out the difference between hardware and software, as well as how they work Realise the operations of information networks Be conscious of security measures as well as learn working safely Obtain knowledge of the primary steps of operating a computer, involving using the keyboard,mouse, and Windows desktop Become competent to manage and use files and folders with proficiency Get knowledge of how to implement the fundamental Windows Applications,at the same time, learn Wordpad, Notepad, Task Manager, Calculator, Paint and Internet Explorer. Understand general computing concepts Understand the difference between hardware and software, and how each works Understand how information networks operate Become aware of security concerns and how to work safely Learn the first steps of using a computer, including using the keyboard, mouse, and Windows desktop Be able to effectively manage and use files and folders Learn how to use the basic Windows applications, including WordPad, NotePad, Task Manager, Calculator, Paint, and Internet Explorer Basic computing terms The fundamental computing skills Anatomy of a PC Input, output, and storage devices Operating systems and applications Legal issues, including licensing of software Networking and Internet basics Computers in the workplace Telecommuting and the electronic world Ergonomics, safety, and the environment Being proactive about security Protecting your data Understanding malware Using the mouse, keyboard, and desktop Customizing your computer Printing Understand and using files and folders Basic Windows applications Working with a window Working with applications Browsing the Web Realise the common computing concepts Figure out the difference between hardware and software, and how they function Interpret how information networks work Be alert of security measures as well as how you can work safely Acquire the knowledge of operating the first steps of a computer, learn how to use the keyboard, mouse, as well as Windows desktop Become competent to proficiently handle and apply files and folders Attain knowledge of operating fundamental Windows applications, along with Wordpad, Notepad, Task Manager, Calculator, Paint and Internet Explorer Assessment: At the end of the course, you will be required to sit for an online MCQ test. Your test will be assessed automatically and immediately. You will instantly know whether you have been successful or not. Before sitting for your final exam you will have the opportunity to test your proficiency with a mock exam. Certification: After completing and passing the course successfully, you will be able to obtain an Accredited Certificate of Achievement. Certificates can be obtained either in hard copy at a cost of £39 or in PDF format at a cost of £24. Who is this Course for? Computer Specialist Diploma is certified by CPD Qualifications Standards and CiQ. This makes it perfect for anyone trying to learn potential professional skills. As there is no experience and qualification required for this course, it is available for all students from any academic background. Requirements Our Computer Specialist Diploma is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation. Career Path After completing this course you will be able to build up accurate knowledge and skills with proper confidence to enrich yourself and brighten up your career in the relevant job market. Module 01 Basic Terms 00:15:00 Types of Computers 00:15:00 Anatomy of a PC 00:30:00 How a PC Works 00:15:00 CPU and Memory 00:30:00 Input Devices 00:15:00 Output Devices 00:15:00 Secondary Storage Devices 00:30:00 The Basics 00:15:00 Operating Systems and Applications 00:30:00 How is Software Built 00:15:00 Types of Software 00:15:00 Legal Issues 00:15:00 Module 02 Basic Terms 00:15:00 Advanced Terms 00:15:00 Networking Basics 00:15:00 Basic Internet Concepts 00:30:00 Internet Security 00:30:00 Computers in the Workplace 00:15:00 Tele-Commuting 00:15:00 The Electronic World 00:15:00 Ergonomics 00:15:00 Safety and the Environment 00:15:00 Being Proactive 00:15:00 Identifying Yourself 00:15:00 Protecting Your Data 01:00:00 Understanding Malware 00:15:00 Protecting Against Malware 00:15:00 Module 03 Application Basics 00:30:00 Basic Applications 01:00:00 Working with a Window 01:00:00 Working with WordPad 01:00:00 Working With Applications 01:00:00 Basics of Web Browsers 01:00:00 Browsing the Web 00:15:00 Working with Bookmarks 01:00:00 Working With Web Pages 01:00:00 Printing Web Pages 01:00:00 Module 04 First Steps 00:30:00 Basic Tasks 01:00:00 Using the Desktop 00:15:00 Customizing Your Computer 00:15:00 Printing 00:15:00 The Basics of Files and Folders 00:10:00 Managing Files and Folders, Part I 01:00:00 Managing Files and Folders, Part II 00:15:00 Viewing File or Folder Properties 00:30:00 Working With Files and Folders 00:30:00 Compressed Files 00:05:00 Order Your Certificate and Transcript Order Your Certificates and Transcripts 00:00:00

Python 3 is one of the most popular programming languages. Companies like Facebook, Microsoft, and Apple all want Python

This course brings together all the important topics related to modern distributed applications and systems in one place. Explore the common challenges that appear while designing and implementing large-scale distributed systems, and how big-tech companies solve those problems. Throughout the course, we are going to build a distributed URL shortening service.

Getting Started The QUALIFI Level 3 Diploma in Data Science aims to offer learners a comprehensive introduction to data science. This Level 3 Diploma provides a modern and all-encompassing overview of data science, artificial intelligence, and machine learning. It covers the evolution of artificial intelligence and machine learning from their beginnings in the late 1950s to the emergence of the "big data" era in the early 2000s. It extends to the current AI and machine learning applications, including the associated challenges. In addition to covering standard machine learning models like linear and logistic regression, decision trees, and k-means clustering, this diploma introduces learners to two emerging areas of data science: synthetic data and graph data science. Moreover, the diploma familiarizes learners with the landscape of data analysis and the relevant analytical tools. It includes introducing Python programming so learners can effectively analyse, explore, and visualize data and implement fundamental data science models. Key Benefits Acquire the essential mathematical and statistical knowledge necessary for conducting fundamental data analysis. Cultivate analytical and machine learning proficiency using Python. Foster a solid grasp of data and its related processes, encompassing data cleaning, data structuring, and data preparation for analysis and visualisation. Gain insight into the expansive data science landscape and ecosystem, including relational databases, graph databases, programming languages like Python, visualisation tools, and various analytical instruments. Develop expertise in comprehending the machine learning procedures, including the ability to discern which algorithms are suited for distinct problems and to navigate the steps involved in constructing, testing, and validating a model. Attain an understanding of contemporary and emerging facets of data science and their applicability to modern challenges Key Highlights This course module prepares learners for higher-level Data science positions through personal and professional development. We will ensure your access to the first-class education needed to achieve your goals and dreams and to maximize future opportunities. Remember! The assessment for the Qualification is done based on assignments only, and you do not need to worry about writing any exam. With the School of Business and Technology London, you can complete the Qualification at your own pace, choosing online or blended learning from the comfort of your home. Learning and pathway materials and study guides developed by our qualified tutors will be available around the clock in our cutting-edge learning management system. Most importantly, at the School of Business and Technology London, we will provide comprehensive tutor support through our dedicated support desk. If you choose your course with blended learning, you will also enjoy live sessions with an assigned tutor, which you can book at your convenience. Career Pathways Upon completing the QUALIFI Level 3 Diploma in Data Science, learners can advance their studies or pursue employment opportunities. Data Analyst with an estimated average salary of £39,445 per annum Business Intelligence Analyst with an estimated average salary of £40,000 per annum Data entry specialist with an estimated average salary of £22,425 per annum Database Administrator with an estimated average salary of £44,185 per annum About Awarding Body QUALIFI, recognised by Ofqual awarding organisation has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Leadership, Hospitality & Catering, Health and Social Care, Enterprise and Management, Process Outsourcing and Public Services. They are liable for awarding organisations and thereby ensuring quality assurance in Wales and Northern Ireland. What is included? Outstanding tutor support that gives you supportive guidance all through the course accomplishment through the SBTL Support Desk Portal. Access our cutting-edge learning management platform to access vital learning resources and communicate with the support desk team. Quality learning materials such as structured lecture notes, study guides, and practical applications, which include real-world examples and case studies, will enable you to apply your knowledge. Learning materials are provided in one of the three formats: PDF, PowerPoint, or Interactive Text Content on the learning portal. The tutors will provide Formative assessment feedback to improve the learners' achievements. Assessment materials are accessible through our online learning platform. Supervision for all modules. Multiplatform accessibility through an online learning platform facilitates SBTL in providing learners with course materials directly through smartphones, laptops, tablets or desktops, allowing students to study at their convenience. Live Classes (for Blended Learning Students only) Assessment Time-constrained scenario-based assignments No examinations Entry Requirements The qualification has been intentionally designed to ensure accessibility without imposing artificial barriers that limit entry. To enrol in this qualification, applicants must be 18 years of age or older. Admittance to the qualification will be managed through centre-led registration processes, which may involve interviews or other appropriate procedures. Despite the presence of advanced mathematics and statistics in higher-level data science courses, encompassing subjects such as linear algebra and differential calculus, this Level 3 Diploma only requires learners to be comfortable with mathematics at the GCSE level. The diploma's mathematical and statistical concepts are based on standard mathematical operations like addition, multiplication, and division. Before commencing the Level 3 Diploma in Data Science, learners are expected to meet the following minimum requirements: i) GCSE Mathematics with a grade of B or higher (equivalent to the new level 6 or above); and ii) GCSE English with a grade of C or higher (equivalent to the new level 4 or above). Furthermore, prior coding experience is not mandatory, although learners should be willing and comfortable with learning Python. Python has been selected for its user-friendly and easily learnable nature. In exceptional circumstances, applicants with substantial experience but lacking formal qualifications may be considered for admission, contingent upon completing an interview and demonstrating their ability to meet the demands of the capability. Progression Upon successful completion of the QUALIFI Level 3 Diploma in Data Science, learners will have several opportunities: Progress to QUALIFI Level 4 Diploma in Data Science: Graduates can advance their education and skills by enrolling in the QUALIFI Level 4 Diploma in Data Science, which offers a more advanced and comprehensive study of the field. Apply for Entry to a UK University for an Undergraduate Degree: This qualification opens doors to higher education, allowing learners to apply for entry to a UK university to pursue an undergraduate degree in a related field, such as data science, computer science, or a related discipline. Progress to Employment in an Associated Profession: Graduates of this program can enter the workforce and seek employment opportunities in professions related to data science, artificial intelligence, machine learning, data analysis, and other relevant fields. These progression options provide learners with a diverse range of opportunities for further education, career advancement, and professional development in the dynamic and rapidly evolving field of data science Why gain a QUALIFI Qualification? This suite of qualifications provides enormous opportunities to learners seeking career and professional development. The highlighting factor of this qualification is that: The learners attain career path support who wish to pursue their career in their denominated sectors; It helps provide a deep understanding of the health and social care sector and managing the organisations, which will, in turn, help enhance the learner's insight into their chosen sector. The qualification provides a real combination of disciplines and skills development opportunities. The Learners attain in-depth awareness concerning the organisation's functioning, aims and processes. They can also explore ways to respond positively to this challenging and complex health and social care environment. The learners will be introduced to managing the wide range of health and social care functions using theory, practice sessions and models that provide valuable knowledge. As a part of this suite of qualifications, the learners will be able to explore and attain hands-on training and experience in this field. Learners also acquire the ability to face and solve issues then and there by exposure to all the Units. The qualification will also help to Apply scientific and evaluative methods to develop those skills. Find out threats and opportunities. Develop knowledge in managerial, organisational and environmental issues. Develop and empower critical thinking and innovativeness to handle problems and difficulties. Practice judgement, own and take responsibility for decisions and actions. Develop the capacity to perceive and reflect on individual learning and improve their social and other transferable aptitudes and skills Learners must request before enrolment to interchange unit(s) other than the preselected units shown in the SBTL website because we need to make sure the availability of learning materials for the requested unit(s). SBTL will reject an application if the learning materials for the requested interchange unit(s) are unavailable. Learners are not allowed to make any request to interchange unit(s) once enrolment is complete. UNIT1- The Field of Data Science Reference No : H/650/4951 Credit : 6 || TQT : 60 This unit provides learners with an introduction to the field of data science, tracing its origins from the emergence of artificial intelligence and machine learning in the late 1950s, through the advent of the "big data" era in the early 2000s, to its contemporary applications in AI, machine learning, and deep learning, along with the associated challenges. UNIT2- Python for Data Science Reference No : J/650/4952 Credit : 9 || TQT : 90 This unit offers learners an introductory approach to Python programming tailored for data science. It begins by assuming no prior coding knowledge or familiarity with Python and proceeds to elucidate Python's fundamentals, including its design philosophy, syntax, naming conventions, and coding standards. UNIT3- Creating and Interpreting Visualisations in Data Science Reference No : K/650/4953 Credit : 3 || TQT : 30 This unit initiates learners into the realm of fundamental charts and visualisations, teaching them the art of creating and comprehending these graphical representations. It commences by elucidating the significance of visualisations in data comprehension and discerns the characteristics distinguishing effective visualisations from subpar ones. UNIT4- Data and Descriptive Statistics in Data Science Reference No : L/650/4954 Credit : 6 || TQT : 60 The primary objective of this unit is to acquaint learners with the foundational concepts of descriptive statistics and essential methods crucial for data analysis and data science. UNIT5- Fundamentals of Data Analytics Reference No : M/650/4955 Credit : 3 || TQT : 30 This unit will enable learners to distinguish between the roles of a Data Analyst, Data Scientist, and Data Engineer. Additionally, learners can provide an overview of the data ecosystem, encompassing databases and data warehouses, and gain familiarity with prominent vendors and diverse tools within this data ecosystem. UNIT6- Data Analysis with Python Reference No : R/650/4956 Credit : 3 || TQT : 30 This unit initiates learners into the fundamentals of data analysis using Python. It acquaints them with essential concepts like Pandas Data Frames and Series and the techniques of merging and joining data. UNIT7- Data Analysis with Python Reference No : R/650/4956 Credit : 3 || TQT : 30 This unit initiates learners into the fundamentals of data analysis using Python. It acquaints them with essential concepts like Pandas Data Frames and Series and the techniques of merging and joining data. UNIT8- Machine Learning Methods and Models in Data Science Reference No : T/650/4957 Credit : 3 || TQT : 30 This unit explores the practical applications of various methods in addressing real-world problems. It provides a summary of the key features of these different methods and highlights the challenges associated with each of them. UNIT9- The Machine Learning Process Reference No : Y/650/4958 Credit : 3 || TQT : 30 This unit provides an introduction to the numerous steps and procedures integral to the construction and assessment of machine learning models. UNIT10- Linear Regression in Data Science Reference No : A/650/4959 Credit : 3 || TQT : 30 This unit offers a foundational understanding of simple linear regression models, a crucial concept for predicting the value of one continuous variable based on another. Learners will gain the capability to estimate the best-fit line by computing regression parameters and comprehend the accuracy associated with this line of best-fit. UNIT11- Logistic Regression in Data Science Reference No : H/650/4960 Credit : 3 || TQT : 30 This unit introduces logistic regression, emphasizing its role as a classification algorithm. It delves into the fundamentals of binary logistic regression, covering essential concepts such as the logistic function, Odds ratio, and the Logit function. UNIT12- Decision Trees in Data Science Reference No : J/650/4961 Credit : 3 || TQT : 30 This unit offers an introductory exploration of decision trees' fundamental theory and practical application. It elucidates the process of constructing basic classification trees employing the standard ID3 decision-tree construction algorithm, including the node-splitting criteria based on information theory principles such as Entropy and Information Gain. Additionally, learners will gain hands-on experience in building and assessing decision tree models using Python. UNIT13- K-means clustering in Data Science Reference No : K/650/4962 Credit : 3 || TQT : 30 This unit initiates learners into unsupervised machine learning, focusing on the k-means clustering algorithm. It aims to give learners an intuitive understanding of the k-means clustering method and equip them with the skills to determine the optimal number of clusters. UNIT14- Synthetic Data for Privacy and Security in Data Science Reference No : L/650/4963 Credit : 6 || TQT : 60 This unit is designed to introduce learners to the emerging field of data science, specifically focusing on synthetic data and its applications in enhancing data privacy and security. UNIT15- Graphs and Graph Data Science Reference No : M/650/4964 Credit : 6 || TQT : 60 This unit offers a beginner-friendly introduction to graph theory, a foundational concept that underlies modern graph databases and graph analytics. Delivery Methods School of Business & Technology London provides various flexible delivery methods to its learners, including online learning and blended learning. Thus, learners can choose the mode of study as per their choice and convenience. The program is self-paced and accomplished through our cutting-edge Learning Management System. Learners can interact with tutors by messaging through the SBTL Support Desk Portal System to discuss the course materials, get guidance and assistance and request assessment feedbacks on assignments. We at SBTL offer outstanding support and infrastructure for both online and blended learning. We indeed pursue an innovative learning approach where traditional regular classroom-based learning is replaced by web-based learning and incredibly high support level. Learners enrolled at SBTL are allocated a dedicated tutor, whether online or blended learning, who provide learners with comprehensive guidance and support from start to finish. The significant difference between blended learning and online learning methods at SBTL is the Block Delivery of Online Live Sessions. Learners enrolled at SBTL on blended learning are offered a block delivery of online live sessions, which can be booked in advance on their convenience at additional cost. These live sessions are relevant to the learners' program of study and aim to enhance the student's comprehension of research, methodology and other essential study skills. We try to make these live sessions as communicating as possible by providing interactive activities and presentations. Resources and Support School of Business & Technology London is dedicated to offering excellent support on every step of your learning journey. School of Business & Technology London occupies a centralised tutor support desk portal. Our support team liaises with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receives a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and streamline all the support processes efficiently. Quality learning materials made by industry experts is a significant competitive edge of the School of Business & Technology London. Quality learning materials comprised of structured lecture notes, study guides, practical applications which includes real-world examples, and case studies that will enable you to apply your knowledge. Learning materials are provided in one of the three formats, such as PDF, PowerPoint, or Interactive Text Content on the learning portal. How does the Online Learning work at SBTL? We at SBTL follow a unique approach which differentiates us from other institutions. Indeed, we have taken distance education to a new phase where the support level is incredibly high.Now a days, convenience, flexibility and user-friendliness outweigh demands. Today, the transition from traditional classroom-based learning to online platforms is a significant result of these specifications. In this context, a crucial role played by online learning by leveraging the opportunities for convenience and easier access. It benefits the people who want to enhance their career, life and education in parallel streams. SBTL's simplified online learning facilitates an individual to progress towards the accomplishment of higher career growth without stress and dilemmas. How will you study online? With the School of Business & Technology London, you can study wherever you are. You finish your program with the utmost flexibility. You will be provided with comprehensive tutor support online through SBTL Support Desk portal. How will I get tutor support online? School of Business & Technology London occupies a centralised tutor support desk portal, through which our support team liaise with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receive a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and to streamline all the support process efficiently. Learners should expect to receive a response on queries like guidance and assistance within 1 - 2 working days. However, if the support request is for assessment feedback, learners will receive the reply with feedback as per the time frame outlined in the Assessment Feedback Policy.