43582 Courses in Cardiff delivered On Demand

In our exploration of hypnotherapy's effectiveness in addressing trauma and PTSD symptoms, we delve into the intricate relationship between traumatic experiences and physiological responses, such as the function of the vagus nerve. Traumatic events can contribute to an overactive vagus nerve, which manifests in symptoms as anxiety, mood changes, nausea, and pain.

Level 4 Internal Quality Assurance Qualification with Risk Management Training Level up your career possibilities with quality assurance expertise and develop the ability to in internal quality assessment. Our unique and intensive Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) qualification will open up a vast window for furnishing your career! This in-demand Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) qualification is awarded by Focus, an established Ofqual-regulated Awarding Organisation, ensuring a solid foundation and assisting you to gain in-depth knowledge of the quality assurance sector. You will be learning from the principles of quality assurance to the fundamentals of the assessment process to risk assessment at a workplace and much more! Upon completing this Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) qualification, you will ensure your professional position, progress further to higher levels of professional qualifications, and be able to ascend your career ladder. Not only that!!! Along with this well-organized Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) qualification, you will get a premium CPD accredited Risk Management course completely FREE!!! Enrol now and stand out brightly in this competitive field! Earn a globally recognised certificate and thrive in your life. Qualification Summary Qualification type: Level 4 Award Qualification title: Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) Accreditation status: Accredited Level: 4 Guided Learning Hours (GLH): 90 Total Qualification Time (TQT): 120 Qualification number (QN): 601/6107/3 ****Qualification Curriculum**** **Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF)** Unit 1: Understanding The Principles And Practices Of Internally Assuring The Quality Of Assessment Unit 2: Internally Assure The Quality Of Assessment ***Curriculum of The FREE Course*** **Risk Management** Module 01: Introduction to Risk Management Module 02: Risk and Types of Risk Module 03: Benefits of Risk Management Module 04: Enterprise Risk Management Module 05: Risk Management Process Module 06: Risk Analysis and Assessment Module 07: The Risk Evaluation Process Module 08: Managing Financial Risks Module 09: The Fundamentals of Operational Risk Management Module 10: Managing Technology Risks Module 11: Managing Legal & Political Risk Module 12: Environmental Risk Assessment Module 13: Risk Assessment at Workplace Module 14: Market & Social Risk Assessment Module 15: Preparing Risk Management Plan: Step by Step Course Duration Guided Learning Hours These hours are made up of all contact time, guidance or supervision of a learner by a lecturer, supervisor, tutor, trainer or other appropriate providers of education or training. Guided Learning Hours for this qualification is 90. Total Qualification Time This is an estimate of the total length of time it is expected that a learner will typically take to achieve and demonstrate the level of attainment necessary for the award of the qualification i.e. to achieve all learning outcomes. Total Qualification Time is GLH and an estimate of the number of hours a learner is likely to spend in preparation, study or any other learning including assessment, which takes place as directed by, but not under the supervision of a lecturer, supervisor or tutor. The credit value, where given, for qualification, is determined by TQT, as one credit corresponds to 10 hours of learning. Total Qualification Time for this qualification is 120 hours. Method of Assessment Assessment activities consist of a few short assignments that you have to complete to show evidence of general reading while adding references to your work. All assignments should be submitted via the online portal. Certification Successful candidates will be awarded a Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) from Focus Awards Limited. You will also get a CPD accredited Risk Management certificate from Apex Learning. Progression Level 4 Award in Externally Assuring the Quality of Assessment Processes and Practice Level 4 Diploma in Learning and Development Who is this course for? Our extensive Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) course is suitable for ambitious learners who are already working or considering working in the Quality Assurance sector. The qualification is also suitable for individuals who: Wish to join in an assessor role Beginner this field and wish to acquire in-demand assessing skills wish to gain formal recognition of their skills want to study from home at their own convenience Requirements Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) qualification does not have any specific entry requirements for students. However, you must be at least 19 years old or above to register. Career path Level 4 Award in the Internal Quality Assurance of Assessment Processes and Practice (RQF) You will have the ability to accomplish a lot of things with this certificate. Here are just a few examples: Internal quality assurer Market developer Marketing executive Quality monitoring officer

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.

This bundle will transform your approach to the inclusion of autistic children with all of our most effective course modules

Overview Mastering English tenses is pivotal for effective communication, and the importance of this skill cannot be overstated. This Explore the Fundamentals of English Tenses course delves into the core principles that underpin effective communication, emphasising the importance of precise tense usage in academic, professional, and everyday contexts. It begins with a foundational understanding in 'Lesson 1a - Simple vs Perfect,' progressing systematically to intricate topics like 'Lesson 36a - Conditional perfect continuous tense questions.' By the time you conclude with 'Lesson 20e - Table of all the tenses,' you'll possess a robust, unshakeable grasp of English tenses. Empower your communication and amplify your opportunities by mastering the very essence of the English language. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Explore the Fundamentals of English Tenses. It is available to all students, of all academic backgrounds. Requirements Our Explore the Fundamentals of English Tenses is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 37 sections • 90 lectures • 05:43:00 total length •Fly Through English Verb Tenses Introduction: 00:01:00 •Lesson 1a - Simple vs Perfect: 00:03:00 •Lesson 1b - Continuous tenses: 00:03:00 •Lesson 1c - Practice with WALK: 00:02:00 •Lesson 2a - Non-continuous simple future: 00:03:00 •Lesson 2b - Non-continuous simple future contracted: 00:03:00 •Lesson 2c - Non-continuous simple future negative: 00:03:00 •Lesson 2d - Non-continuous simple future practice: 00:03:00 •Lesson 2e - Non-continuous simple future practice 2: 00:02:00 •Lesson 3a - Non-continuous simple conditional: 00:03:00 •Lesson 3b - Non-continuous simple conditional contracted: 00:03:00 •Lesson 3c - Non-continuous simple conditional negative: 00:03:00 •Lesson 3d - Non-continuous simple conditional practice: 00:03:00 •Lesson 4a - Non-continuous simple present: 00:04:00 •Lesson 4b - Non-continuous simple present negative: 00:03:00 •Lesson 4c - Non-continuous simple present practice: 00:04:00 •Lesson 4d - Non-continuous simple present irregular: 00:05:00 •Lesson 4e - Non-continuous simple present BE: 00:05:00 •Lesson 4f - Non-continuous simple present regular verb list: 00:03:00 •Lesson 5a - Non-continuous simple past: 00:03:00 •Lesson 5b - Non-continuous simple past negative: 00:03:00 •Lesson 5c - Non-continuous simple past practice: 00:03:00 •Lesson 5d - Non-continuous simple past irregular: 00:04:00 •Lesson 5e - Non-continuous simple past very irregular: 00:05:00 •Lesson 5f - Non-continuous simple past BE: 00:04:00 •Lesson 6a - Non-continuous perfect tenses: 00:04:00 •Lesson 6b - Non-continuous perfect tenses past participle: 00:09:00 •Lesson 7a - Non-continuous present perfect: 00:03:00 •Lesson 7b - Non-continuous present perfect contracting: 00:03:00 •Lesson 7c - Non-continuous present perfect negative: 00:03:00 •Lesson 7d - Non-continuous present perfect practice: 00:03:00 •Lesson 8a - Non-continuous past perfect: 00:04:00 •Lesson 8b - Non-continuous past perfect negative: 00:04:00 •Lesson 8c - Non-continuous past perfect practice: 00:04:00 •Lesson 9a - Non-continuous future perfect: 00:04:00 •Lesson 9b - Non-continuous future perfect negative: 00:03:00 •Lesson 9c - Non-continuous future perfect practice: 00:03:00 •Lesson 10a - Non-continuous conditional perfect: 00:02:00 •Lesson 10b - Non-continuous conditional perfect contracted: 00:03:00 •Lesson 10c - Non-continuous conditional perfect negative: 00:03:00 •Lesson 10d - Non-continuous conditional perfect practice: 00:04:00 •Lesson 11a - Simple continuous tenses: 00:03:00 •Lesson 11b - Simple continuous tenses present participle: 00:03:00 •Lesson 11c - Simple continuous tenses present participle irregulars: 00:03:00 •Lesson 12a - Simple present continuous tense: 00:04:00 •Lesson 12b - Simple present continuous negative: 00:04:00 •Lesson 12c - Simple present continuous tense negative practice: 00:03:00 •Lesson 12d - Simple present continuous practice: 00:03:00 •Lesson 13a - Simple past continuous tense: 00:04:00 •Lesson 13b - Simple past continuous tense negative: 00:04:00 •Lesson 13c - Simple past continuous tense practice: 00:03:00 •Lesson 14a - Simple future continuous tense: 00:03:00 •Lesson 14b - Simple future continuous negative: 00:04:00 •Lesson 14c - Simple future continuous practice: 00:04:00 •Lesson 15a - Simple conditional continuous tense: 00:04:00 •Lesson 15b - Simple conditional continuous negative: 00:04:00 •Lesson 15c - Simple conditional continuous practice: 00:04:00 •Lesson 16a - Perfect continuous tenses: 00:04:00 •Lesson 17a - Present perfect continuous tense: 00:04:00 •Lesson 17b - Present perfect continuous negative: 00:04:00 •Lesson 17c - Present perfect continuous practice: 00:04:00 •Lesson 18a - Past perfect continuous tense: 00:03:00 •Lesson 18b - Past perfect continuous negative: 00:04:00 •Lesson 18c - Past perfect continuous practice: 00:04:00 •Lesson 19a - Future perfect continuous tense: 00:04:00 •Lesson 19b - Future perfect continuous negative: 00:05:00 •Lesson 19c - Future perfect continuous practice: 00:05:00 •Lesson 20a - Conditional perfect continuous tense: 00:03:00 •Lesson 20b - Conditional perfect continuous contractions: 00:04:00 •Lesson 20c - Conditional perfect continuous negative: 00:03:00 •Lesson 20d - Conditional perfect continuous practice: 00:05:00 •Lesson 20e - Table of all the tenses: 00:01:00 •Lesson 21a - Simple present tense questions: 00:04:00 •Lesson 21b - Simple present tense negative questions: 00:04:00 •Lesson 22a - Simple past tense questions: 00:04:00 •Lesson 22b - Simple past tense negative questions: 00:03:00 •Lesson 23a - Simple future tense questions: 00:05:00 •Lesson 24a - Simple conditional tense questions: 00:05:00 •Lesson 25a - Present perfect tense questions: 00:05:00 •Lesson 26a - Past perfect tense questions: 00:05:00 •Lesson 27a - Future perfect tense questions: 00:05:00 •Lesson 28a - Conditional perfect tense questions: 00:05:00 •Lesson 29a - Present continuous tense questions: 00:06:00 •Lesson 30a - Past continuous tense questions: 00:05:00 •Lesson 31a - Future continuous tense questions: 00:05:00 •Lesson 32a - Conditional continuous tense questions: 00:05:00 •Lesson 33a - Present perfect continuous tense questions: 00:05:00 •Lesson 34a - Past perfect continuous tense questions: 00:06:00 •Lesson 35a - Future perfect continuous tense questions: 00:06:00 •Lesson 36a - Conditional perfect continuous tense questions: 00:06:00

3 QLS Endorsed Diploma | QLS Hard Copy Certificate Included | 10 CPD Courses | Lifetime Access | 24/7 Tutor Support

3 QLS Endorsed Diploma | QLS Hard Copy Certificate Included | 10 CPD Courses | Lifetime Access | 24/7 Tutor Support

https://www.lloydmaritime.com/C-S/cargo-surveying-advanced-diploma-expert-in-draught-surveying

Premium Bundle of all Time | Ofqual Regulation + ATHE Awards + CPD Accreditation | Assessment & Tutor Support Included

Project Accounting and Finance Skills: On-Demand Do you manage both project schedules and budgets, but do not have insight into how actual results relate to the approved budget? Do you desire to have more clarity about the relationship between your project's performance with the accounting and financial systems in your organization? Do you need to understand financial and accounting terminology to bridge the gap between the 'world of finance' and the 'world of project management? Organizations have a need to manage-by-projects, because projects are the means to deliver on strategic goals and objectives. Therefore, the project manager must have an understanding of the financial world of investments to ensure the organization will realize expected business value. This requires a foundation in the principles of accounting and finance to comprehend how the project's contribution provides an organization with a competitive advantage. Learn what you must do to give your organization the assurance it needs that its investment in your project will realize business value. Learn what you must do to give your organization the assurance it needs to know that its investment in your project will realize business value. What You Will Learn At the end of this program, you will be able to: Explain the aspects of classical corporate accounting and finance effects on managing projects Determine how your project fits into the corporate income statement, balance sheet, and cash flow statement Analyze the financial aspects of managing projects Use earned value management as the basis for decision making throughout the project life Recognize the importance of the project manager's financial responsibilities Focus on what PMs do and should be doing, in support of accounting and finance Use financial information within a project environment to meet financial results Track and analyze the project's financial status and forecast with the goal of realizing benefits Generate work performance data to ensure a project's outcome aligns with financial metrics Foundation Concepts Accounting and Finance Terms and Concepts Accounting and Finance Essentials Financial Terms and Concepts Projects as Financial Investments Overview of 'Two Worlds' Project as Investments Accounting and Finance World: Standards, Principles and Practices Accounting and Finance Standards and Principles Accounting and Finance Practices Capital Budgeting Corporate Budgeting Accounting and Finance World: Economic Project Selection Methods Economic Project Selection Methods Economic Project Selection and the Business Case Project Management World Project Management and Financial Controls Project Management and Work Performance Data Project Management and Earned Value Management Project Management and Work Performance Reporting