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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.

ITIL® 4 Specialist: High-Velocity IT: On-Demand This course is based on the ITIL® 4 Specialist: High-Velocity IT exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam. What You Will Learn At the end of this course, participants will be able to: Understand concepts regarding the high-velocity nature of the digital enterprise, including the demand it places on IT. Understand the digital product lifecycle in terms of the ITIL operating model. Understand the importance of the ITIL guiding principles and other fundamental concepts for delivering high-velocity IT. Know how to contribute to achieving value with digital products. Course Introduction Introduction to High-Velocity IT High-Velocity IT Approaches High-Velocity IT Operating Models Introduction ITIL Building Blocks for High-Velocity IT Supporting Models and Concepts for Purpose Supporting Models and Concepts for People Supporting Models and Concepts for Progress High-Velocity IT Objectives and Techniques Techniques for Valuable Investments Techniques for Fast Developments Techniques for Resilient Operations Techniques for Co-created Value Techniques for Assured Conformance

Developing the Business Case - Defining the Business Problem and Solution Scope: On-Demand This course teaches the purpose, structure, and content of a business case. It presents the basic techniques for determining financial ROI, non-tangible benefits, and the probability of meeting expectations. What You Will Learn You will learn how to: Prepare a business case Justify the business investment to solve the business need Perform feasibility studies and ROI analyses Foundation Concepts The role of the business analyst An introduction to the BABOK® Guide The business analyst and the product/project life cycle The business case deliverable Introducing the Business Case Process The business analyst and Strategy Analysis The business analyst and the business case process The business analyst during the business case process The business analyst after the business case process Importance of defining solution performance metrics Defining the Business Need Overview of defining the business need Business needs: problem/opportunity statement Product vision Objectives and constraints Exploring Business Case Solutions Overview of exploring solutions Solution identification for feasibility Solution definition for analysis Assessing project risks Justifying the Business Case Overview of justifying the business case Qualitative justification Quantitative justification Approving the Business Case Overview of business case approval Developing recommendations Preparing the decision package - Documents Preparing the decision Package - Presentations

Politics and Project Management - Turn Project Politics into a Positive Force for Project Objectives: On-Demand The goal of this course is to provide project managers with a strong working ability to recognize and successfully navigate organizational politics for the good of projects. What You Will Learn You'll learn how to: Explain how positive politics are a valuable method for getting things done in projects Apply positive political principles and techniques in an ethical manner Use politically sound strategies, tools, and techniques for positive achievement of project objectives Getting Started Introductions Course structure Course goals and objectives Introduction to Project Politics Framing project politics Characterizing project politics Distinguishing self-interest from project politics Political Skills in Projects Embracing positive politics Identifying political competencies in projects Exploring personal political competencies Politics and Power in Projects Positioning power in projects Exploring sources of power Comparing power and influence Developing strategies for power and influence Project Politics and Conflict Facing conflict - a fact of life Turning conflict into a positive force Channeling conflict into good decisions Resolving conflict in and around project politics Tackling deeper conflicts Managing conflict that escalates Summary What did we learn and how can we implement this in our work environments? Creating your own personal action plan

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?

Business Analysis Fundamentals - Solving the Business Problem: On-Demand This course teaches participants the overall process of business analysis and where it fits in the bigger picture of the project life cycle and the business context. The course is interactive and combines discussion, active workshops, and demonstrations of techniques. The goal is bottom-line results that cut through the real-world problems facing people seeking to improve the way they operate to develop new and improved systems and products or otherwise deliver results through project performance. What You Will Learn Upon completion, participants will be able to: Define the solution scope Work with the development team in the systems testing stage Ensure the solution is usable in the business environment Foundation Concepts Defining the Business Analyst Function Role of the Business Analyst as Change Agent An Introduction to the BABOK® Guide Business Analyst Roles and Relationships through the Project Life Cycle Business Analysis Planning and Monitoring - Defining the Process Overview of Business Analysis Planning & Monitoring (BAP&M) BAP&M - Process and Tools BAP&M - Roles and Responsibilities BAP&M - Governance, Information Management & Performance Improvement Elicitation and Collaboration - Gathering and Confirming Information Overview of Elicitation and Collaboration Elicitation and Collaboration Techniques Requirements Life Cycle Management - Maintaining Requirements and Design Information Overview of Requirements Life Cycle Management Requirements Life Cycle Management Tasks - Details Strategy Analysis - Providing Context to Requirements Analysis and Design Definition Overview of Strategy Analysis Analyze Current State Define Future State Assess Risks Define Change Strategy Requirements Analysis and Design Definition - Defining Solution Options Overview of Requirements Analysis and Design Definition (RA&DD) The Anatomy of Requirements RA&DD Task Descriptions RA&DD Techniques Solution Evaluation - Assessing Solution Performance Overview of Solution Evaluation Solution Evaluation Tasks Solution Evaluation in Development Stages Underlying Competencies Overview of Underlying Competencies (UCs) Underlying Competencies - Details

ITIL® 4 Strategist: Direct, Plan and Improve: On-Demand The ITIL® 4 Strategist: Direct, Plan, and Improve course is based on the ITIL® 4 Strategist Direct, Plan, and Improve candidate syllabus from AXELOS. This course is based on the ITIL® 4 Strategist: Direct, Plan and Improve IT exam specifications from AXELOS. With the help of ITIL® 4 concepts and terminology, exercises, and examples included in the course, candidates acquire the relevant knowledge required to pass the certification exam. This course provides the practical skills necessary to create a 'learning and improving' IT organization, with a strong and effective strategic direction. It was designed to provide practitioners with a practical and strategic method for planning and delivering continual improvement with necessary agility. It covers both practical and strategic elements, making it the universal module that is a key component to both ITIL® 4 Managing Professional and ITIL® 4 Strategic Leader streams. What You Will Learn At the end of this course, participants will be able to: Understand the key concepts of direction, planning, improvement Understand the scope of what is to be directed and/or planned and know how to use key principles and methods of direction and planning in that context Understand the role of GRC and know how to integrate the principles and methods into the service value system Understand and know how to use the key principles and methods of continual improvement for all types of improvements Understand and know how to use the key principles and methods of Communication and Organizational Change Management to direction, planning and improvement Understand and know how to use the key principles and methods of measurement and reporting in direction, planning, and improvement Understand and know how to direct, plan, and improve value streams and practices Course Introduction Let's Get to Know Each Other Course Overview ITIL® 4 Certification Scheme Course Learning Objectives Course Components Course Agenda Exercises Case Study: Axle Car Hire Case Study: HandyPerson on Demand Exam Details Core Concepts of DPI Key Terms Covered in the Module Module Learning Objectives Basics of Direction Basics of Planning Basics of Improvement Other Core Elements DPI through Service Value Chain and Guiding Principles Key Terms Covered in the Module Module Learning Objectives DPI of the SVS DPI of Guiding Principles Role of Direction in Strategy Management Key Terms Covered in the Module Introducing Strategy Management Developing Effective Strategies Implementation of Strategies Key Terms Covered in the Module Module Learning Objectives Managing Risks Making Decisions through Portfolio Management Directing via Governance, Risk, and Compliance (GRC) Introduction to Assessment and Planning Key Terms Covered in the Module Module Learning Objectives Core Concepts of Assessment Conducting Effective Assessments Core Concepts of Planning Assessment and Planning through VSM Key Terms Covered in the Module Module Learning Objectives Introducing VSM Developing Value Stream Maps Knowing More About VSM Measurement, Reporting, and Continual Improvement Key Terms Covered in the Module Module Learning Objectives Measurement and Reporting Alignment of Measurements and Metrics Success Factors and Key Performance Indicators Continual Improvement Measurements and Continual Improvement through Dimensions and SVS Key Terms Covered in the Module Module Learning Objectives Measurements for the Four Dimensions Continual Improvement of the Service Value Chain and Practices OCM Principles and Methods Key Terms Covered in the Module Module Learning Objectives Basics of OCM OCM throughout DPI and Service Value Chain Resistance and Reinforcement Communication Principles and Methods Key Terms Covered in the Module Module Learning Objectives Basics of Effective Communication Communication with Stakeholders SVS Development Using Four Dimensions Key Terms Covered in the Module Module Learning Objectives Organizations and People in the SVS Partners and Suppliers in the SVS Value Streams and Processes in the SVS Information and Technology in the SVS

Validation of equipment, services, systems and processes is vitally important in the medicines and healthcare products industries. Regulatory authorities require documented evidence that manufacturing processes will consistently result in products meeting predetermined quality standards. This module provides an introduction to validation and to the regulations and guidance that apply to it. It describes the activities of a typical validation team as they carry out a project for a pharmaceutical company.

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