14642 Ear courses

Includes: Hard copy take home training manual and pinkfishes.com official training kit Duration – Fast track one day practical classroom-based training plus online theory work. Theory work to be completed in your own time via The Guild student portal before your practical course date. Practical day – 10:30am – 5:30pm inc short lunch break. It is advisable that students arrive 15 mins early to ensure a prompt start. In a lot of cases the practical training can be finished earlier than expected depending on how many students there are, and the time taken on practical work. Widely recognisable and insurable qualification, allowing you to deliver this treatment on the paying general public.

Includes: Hard copy take home training manual and pinkfishes.com official training kit Duration – Fast track one day practical classroom-based training plus online theory work. Theory work to be completed in your own time via The Guild student portal before your practical course date. Practical day – 10:30am – 5:30pm inc short lunch break. It is advisable that students arrive 15 mins early to ensure a prompt start. In a lot of cases the practical training can be finished earlier than expected depending on how many students there are, and the time taken on practical work. Widely recognisable and insurable qualification, allowing you to deliver this treatment on the paying general public.

This is a highly interactive programme designed by the communication experts at TCM. The course helps learners to develop essential skills for everyday management. On this programme, delegates learn how to navigate the complex maze of having difficult conversations, managing performance, and giving bad news. Offering a proactive and hands on approach to teaching, the TCM trainers equip delegates with the confidence to have those crucial conversations, to prevent disputes before they escalate out of control and, to be assertive in addressing performance issues to achieve engagement and sustainable outcomes.

Writing and Managing Requirements Documents - Creating the Acceptable, Approvable Requirements Document: On-Demand This course will enhance the skill set needed for writing and managing the complex readership that business analysts interact with on a day-to-day basis. What You Will Learn Upon Completion, participants will be able to: Write an understood requirements document that is approvable and acceptable Validate a requirements document Manage the changes to requirements documents through the SDLC Foundation Concepts The role of the business analyst An introduction to the BABOK® Guide The business analyst and the product/project life cycle The requirements documentation process Planning for Effective Requirements Documentation Overview of requirements planning Planning for validation Planning for verification: well-formed criteria Planning for verification: understood and usable criteria Writing Effective Requirements Documents Overview of writing requirements documents Using a standard structure / template Applying formatting techniques Meeting the challenge of writing non-functional requirements Baselining Requirements Documents Overview of the requirements baseline process Validation Verification Approval Managing Requirements Change through the Product Life Cycle Overview of requirements change management Establishing a formal change management process Tracing requirements through design and development (build, test, and implementation) Following through to post-implementation (transition and early production) Summary What did we learn, and how can we implement this in our work environments?

Assuring Quality Through Acceptance Testing - Making Sure that the Business Problem is Solved: On-Demand It is the business analyst's job to define the business problem to be solved by IT. It is also the business analyst's responsibility to confirm that the resulting solution developed by IT does, in fact, solve the defined problem. This is done first through testing, especially acceptance testing, and then through monitoring of the installed solution in the user community. The business analyst is not only concerned with the testing itself, but also with the management and monitoring of the users doing the acceptance testing, and recording, analyzing, and evaluating the results. What You Will Learn Upon completion of this course, participants will be able to: Create a set of acceptance test cases Manage and monitor an acceptance test stage where users perform the testing Work with the development team in the systems testing stage Assess the solution once it is in the business environment Foundation Concepts The role of the business analyst An introduction to the BABOK® Guide BA roles and relationships through the project life cycle Introduction to assuring software quality through acceptance testing The Scope of IT Testing Overview of testing stages The testing process Testing documentation Pre-Acceptance Testing The BA's role in testing Early development testing stages (unit and integration) Late development testing stage (system) The Acceptance Test Stage - Part I (Planning, Design, and Development) Overview of user acceptance testing Acceptance test planning Designing user acceptance tests Developing individual user acceptance test cases Building effective user acceptance test scenarios The Acceptance Test Stage - Part II (Execution and Reporting) Operating guidelines Execution Reporting Post-Acceptance Testing Overview Project implementation Project transition (project closure) Production through retirement Testing Commercial Off-the-Shelf (COTS) Software Overview Selecting the software Implementing the software Summary What did we learn and how can we implement this in our work environments?

Assuring Quality Through Acceptance Testing: On-Demand It is also the business analyst's responsibility to confirm that the resulting solution developed by IT does, in fact, solve the defined problem. This is done first through testing, especially acceptance testing, and then through monitoring of the installed solution in the user community. It is the business analyst's job to define the business problem to be solved by IT. It is also the business analyst's responsibility to confirm that the resulting solution developed by IT does, in fact, solve the defined problem. This is done first through testing, especially acceptance testing, and then through monitoring of the installed solution in the user community. The business analyst is not only concerned with the testing itself, but also with the management and monitoring of the users doing the acceptance testing, and recording, analyzing, and evaluating the results. What you will Learn Upon completion, participants will be able to: Create a set of acceptance test cases Manage and monitor an acceptance test stage where users perform the testing Work with the development team in the systems testing stage Assess the solution once it is in the business environment Foundation Concepts The role of the business analyst An introduction to the BABOK® Guide BA roles and relationships through the project life cycle Introduction to assuring software quality through acceptance testing The Scope of IT Testing Overview of testing stages The testing process Testing documentation Pre-Acceptance Testing The BA's role in testing Early development testing stages (unit and integration) Late development testing stage (system) The Acceptance Test Stage - Part I (Planning, Design, and Development) Overview of user acceptance testing Acceptance test planning Designing user acceptance tests Developing individual user acceptance test cases Building effective user acceptance test scenarios The Acceptance Test Stage - Part II (Execution and Reporting) Operating guidelines Execution Reporting Post-Acceptance Testing Overview Project implementation Project transition (project closure) Production through retirement Testing Commercial Off-the-Shelf (COTS) Software Overview Selecting the software Implementing the software Summary What did we learn and how can we implement this in our work environments?

Work Breakdown Structures It's amazing how often project managers begin the project planning process by making an outlined list of every task they believe will be required to complete a project and then proclaim they have created the work breakdown structure (WBS) for the project. The result is a list of hundreds, or even thousands of tasks, many of them having durations of a few days or a few hours. Essentially, what they have done is create a 'to do' list, which they then use as a 'checklist' to measure progress. This approach leads to, and even encourages, micromanagement of the resources working on the project without consideration of more critical aspects of project management such as: requirements management, risk management, procurement management, estimating, scheduling, executing, and controlling. Further, it makes it impossible to see the big picture, at levels of detail, in keeping with the needs of sponsors, clients, project and functional managers, team leaders, and project performers. Join us for this exciting program and learn how to use the WBS to make better-informed business decisions. What You Will Learn You will learn how to: Describe the need for a project WBS Describe the WBS role in the project Gain practical experience in the development, decomposition, and use of the WBS Determine the appropriate level of detail in the WBS. Explain how the WBS integrates with project requirements, risk, procurement, estimating, scheduling, and overall project execution. Provide the basic tools to enhance efficient re-use of key information in your future projects Foundation Concepts Key definitions History of the WBS Importance of the WBS Overall structure Terminology Other breakdown structures WBS tools WBS & Scope Project scope management processes Specification of the project objectives WBS design based on project deliverable WBS decomposition process and 'The 100% rule' Work Packages and Control Accounts WBS & Risk Risk management planning and WBS Risk identification to enhance the WBS Risk analysis and the WBS Risk responses and updating the WBS Implementing risk response and Monitoring risks and the WBS WBS & Estimating Use of WBS in the estimating process Components and work packages Sizing and algorithmic estimates WBS & Scheduling Component Scheduling - High-Level Milestones WBS activity decomposition WBS elements dependencies Work Package Level Schedules Responsibility assignment matrix WBS & Execution and Control Earned Value Management and tracking of work performance Progress reports, forecasts, and corrective and preventive actions used to manage work performance Necessary information to close out a project

Course Information This highly interactive course will provide guidance on why and how to implement a quality system successfully into the research laboratory. By doing so, you will position your innovation for the success it deserves. But leave things as they are and there is a good chance that your science will not realise its full potential should success, and its consequences, come your way. A quality system in your research laboratory is the most effective and efficient way to: Help scientists work more efficiently Ensure discoveries can be defended Protect the value of intellectual property. This course is particularly aimed at those working in early phase research environments which are not constrained by the regulatory requirements of the Good Practice regulations but are producing intellectual property, testing and/or products for the therapeutic market. For organisational reasons, rather than regulatory ones, this is a place where you need to get it right. The programme is delivered by leaders in the field who, quite simply, ‘have done it’. Whether delegates are at senior management level seeking strategic direction, a laboratory head wishing to deliver science that will stand the test of time or a quality professional thrown in at the deep end, this course will provide key insight and practical guidance to underpin future success. Based on risk based systems, tried and tested over many years in the workplace, the programme will help delegates to define, train, implement and monitor the quality of their research, irrespective of field or discipline. Delegates will learn how to help position their organisation for success. Course content: Delegates will be guided thoughtfully through each key component of the process in a stimulating learning environment. The course probes all avenues of the research quality arena, from an initial understanding of the cultural aspects of the scientific discovery environment, to managing quality in outsourced research programmes. Computer systems and e-data security in the research environment will be discussed and pragmatic solutions described to help manage the ballooning cloud of e-data. In addition, the ever blurring boundary between the regulated and non-regulated research environments will be discussed and delegates given perspective on future developments in the field. With this knowledge, delegates will be able to get it ‘right first time’. Is this course for you? The course is designed for all those involved in the research laboratory quality arena and it has been tailored to meet the needs of scientific management, bench scientists and quality professionals alike. Delegates get immediate access to highly experienced tutors who will share their wisdom and insights in an area where few others have been successful. The course is linked with the RQA guidance which builds on years of experience and forms the foundation of the programme. Tutors Tutors will be comprised of (click the photos for biographies): Louise Handy Director, Handy Consulting Ltd Sandrine Bongiovanni Associate Director in Research and Quality Compliance, Novartis Programme Please note timings may be subject to alteration. Day 1 09:00 Registration 09:10 Welcome and Introductions 09:20 History and Overview of the Field Examples of business and regulatory risks and the consequences of low quality in research. A look at the standards and guidelines that exist. 10:00 The Culture, the Politics and the Scientist's Perspective Understanding research environments, the drivers and the challenges. 10:30 Break 10:45 Workshop - Risk Management Thinking about risk management and prioritisation. Looking at the critical factors for the implementations of a successful quality system. 12:15 Workshop - Feedback 12:45 Lunch 13:45 Personnel, Plans, Procedures, Facilities, Equipment, Materials and Reagents Looking at planning the work, defining procedures in a way which promotes robust science without compromising brilliance and ensuring that all these elements are demonstrably fit for their intended purpose. 14:30 Workshop - Assay Validation How much validation is required at what stage? What do we need to validate an assay? 15:00 Workshop - Feedback 15:15 Research, Work Records, Archives and Research Review Data and records which are accurate, attributable, legally attestable and safe to permit reconstruction experiments and studies. Looking at aspects of the work where there is a chance to review, correct or improve the science, the data and the processes. 16:15 Continual Improvement and Quality Systems Reviewing implementation of a quality system, finding opportunities for improvement, understanding culture change. 16:45 Questions and Answers 17:00 Close of Course Extra Information Course Material This course will be run completely online. You will receive an email with a link to our online system, which will house your licensed course materials and access to the remote event. Please note this course will run in UK timezone. The advantages of this include:   Ability for delegates to keep material on a mobile device Ability to review material at any time pre and post course Environmental benefits – less paper being used per course Access to an online course group to enhance networking. You will need a stable internet connection, a microphone and a webcam. CPD Points 7 Points   Development Level Develop

About the course “Quantum Computing for Finance” is an emerging multidisciplinary field of quantum physics, finance, mathematics, and computer science, in which quantum computations are applied to solve complex problems. “Quantum Algorithms for Computational Finance” is an advanced course in the emerging field of quantum computing for finance. This technical course will develop an understanding in quantum algorithms for its implementation on quantum computers. Through this course, you will learn the basics of various quantum algorithms including: Grover’s and Rudolf’s algorithm, Quantum amplitude Estimation (QAE) algorithm envisioned as a quadratic speed-up over Classical Monte-Carlo simulations, Combinatorial optimization algorithms namely Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE), and Quantum-inspired optimization algorithms – Simulated Coherent Ising Machine (Sim-CIM), and Simulated Bifurcation Algorithm (SBA). This course is meant for all those learners who want to explore the long-term employability of quantum computing in finance, assuming that you are familiar with the concepts of quantitative and computational finance. In addition, the course contains several Python based programming exercises for learners to practice the algorithms explained throughout the course. This course is the second part of the specialised educational series: “Quantum Computing for Finance”. What Skills you will learn Ability to perform quantum arithmetic operations and simulations. An understanding of the Quantum Amplitude Estimation algorithm and its variants. The computational and modelling techniques for option pricing and portfolio optimization on a quantum computer. The skills for a career in quantum finance including Quantum Algorithmic Research, Quantitative Asset Management and Trading, financial engineering, and risk management, using quantum computing technology. Course Prerequisites All potential learners must have prior knowledge or familiarity with basic quantum algorithms/basic quantum programming. Before enrolling this course, we recommend all learners to complete the first course “Introduction to Quantitative and Computational Finance” of the series “Quantum Computing for Finance”, if they have no previous experience with the concepts of quantitative and computational finance. Duration The estimated duration to complete this course is approximately 6 weeks (~4hrs/week). Course assessment To complete the course and earn the certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more on each of them. Instructors QuantFiQuantFi is a French start-up research firm formed in 2019 with the objective of using the science of quantum computing to provide solutions to the financial services industry. With its staff of PhD's and PhD students, QuantFi engages in fundamental and applied research in in the field of quantum finance, collaborating with industrial partners and universities in seeking breakthroughs in such areas as portfolio optimisation, asset pricing, and trend detection.

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.