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10 QLS Endorsed Courses for 3D Game Developer | 10 Endorsed Certificates Included | Life Time Access

48-Hour Knowledge Knockdown! Prices Reduced Like Never Before! Step into the world of geospatial technology with our exclusive QGIS Cartography and ArcGIS bundle. Discover the power of spatial analysis and remote sensing while learning to create smart maps, web scraping, and data visualisation. Whether you're a beginner or an experienced professional, this bundle offers everything you need to enhance your skillset and advance your career. As a bonus, the QGIS Cartography course is QLS-endorsed, ensuring that you receive a hardcopy certificate upon completion, enhancing your credibility in the field. The remaining ten courses are CPD-QS accredited, providing you with industry-recognised certification. Our courses are designed to be accessible to everyone, with no prior experience or technical knowledge required. With our easy-to-follow lectures, you can learn at your own pace and on your own schedule. You'll have access to comprehensive resources, including course materials and support from our expert team. The QGIS Cartography and ArcGIS bundle is the perfect choice for anyone looking to expand their knowledge and enhance their career prospects in the geospatial industry. With our top-rated courses, you'll gain invaluable skills that will set you apart from the competition. So why wait? Enrol now and take the first step towards a brighter future! This QGIS Cartography and ArcGIS Bundle Package includes: Course 01: Diploma in QGIS Cartography at QLS Level 4 10 Premium Additional CPD QS Accredited Courses - Course 01: Spatial Analysis in ArcGIS Course 02: Remote Sensing in ArcGIS Course 03: ArcGIS for Hydrology Course 04: Spatial Data Visualization and Machine Learning in Python Level 4 Course 05: Create Smart Maps in Python and Leaflet Level 3 Course 06: Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 Course 07: Python Data Science Course 08: Learn to Use Python for Spatial Analysis in ArcGIS Course 09: Maps in R Shiny and Leaflet Course 10: The Basics of ArcGIS for Hydrology Why Prefer This QGIS Cartography and ArcGIS Bundle? You will receive a completely free certificate from the Quality Licence Scheme Option to purchase 10 additional certificates accredited by CPD Get a free Student ID Card - (£10 postal charges will be applicable for international delivery) Free assessments and immediate success results 24/7 Tutor Support After taking this QGIS Cartography and ArcGIS bundle courses, you will be able to learn: Understand the basics of QGIS Cartography and ArcGIS software Learn to perform spatial analysis and remote sensing in ArcGIS Develop skills in creating smart maps and web scraping with Python and Leaflet Learn data visualisation and machine learning in Python Gain expertise in hydrology with ArcGIS Create maps in R Shiny and Leaflet Use Python for spatial analysis in ArcGIS ***Curriculum breakdown of QGIS Cartography*** Section 01: Introduction Section 02: Labels Section 03: Symbology Section 04: Base Layers Section 05: Project Setup Section 06: Water Styling Section 07: Land Styling How is the QGIS Cartography and ArcGIS Bundle Assessment Process? You have to complete the assignment questions given at the end of the course and score a minimum of 60% to pass each exam. Our expert trainers will assess your assignment and give you feedback after you submit the assignment. You will be entitled to claim a certificate endorsed by the Quality Licence Scheme after you successfully pass the exams. CPD 220 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Professionals seeking to enhance their skills in geospatial technology Students interested in a career in GIS or related fields Individuals looking to expand their knowledge in QGIS Cartography and ArcGIS software Anyone seeking industry-recognized certification Career path This bundle will be beneficial for anyone looking to pursue a career as: GIS Analyst: £21,000 - £42,000 per annum GIS Technician: £18,000 - £32,000 per annum GIS Manager: £28,000 - £65,000 per annum Remote Sensing Analyst: £25,000 - £50,000 per annum Data Scientist: £25,000 - £80,000 per annum Hydrologist: £25,000 - £50,000 per annum Certificates Diploma in QGIS Cartography at QLS Level 4 Hard copy certificate - Included After successfully completing thecourse, you can order an original hardcopy Certificate of Achievement endorsed by the Quality Licence Scheme. This certificate will be delivered to your home, free of charge. To obtain the additional Quality Licence Scheme Endorsed Certificate, you will need to pay the corresponding fee based on the level of the courses: Level 1: £59 Level 2: £79 Level 3: £89 Level 4: £99 Level 5: £119 Level 6: £129 Level 7: £139 CPD QS Accredited Certificate Digital certificate - £10 Upon successfully completing the Bundle, you will need to place an order to receive a PDF Certificate for each course within the bundle. These certificates serve as proof of your newly acquired skills, accredited by CPD QS and will cost you £10 for each course. Also, the certificates are recognised throughout the UK and internationally. CPD QS Accredited Certificate Hard copy certificate - £29 International students are subject to a £10 delivery fee for their orders, based on their location.

Give a compliment to your career and take it to the next level. This Web Application Penetration Testing & Security will provide you with the essential knowledge and skills required to shine in your professional career. Whether you want to develop skills for your next job or want to elevate skills for your next promotion, this Web Application Penetration Testing & Security will help you keep ahead of the pack. The Web Application Penetration Testing & Security incorporates basic to advanced level skills to shed some light on your way and boost your career. Hence, you can reinforce your professional skills and essential knowledge, reaching out to the level of expertise required for your position. Further, this Web Application Penetration Testing & Security will add extra value to your resume to stand out to potential employers. Throughout the programme, it stresses how to improve your competency as a person in your profession while at the same time it outlines essential career insights in this job sector. Consequently, you'll strengthen your knowledge and skills; on the other hand, see a clearer picture of your career growth in future. By the end of the Web Application Penetration Testing & Security, you can equip yourself with the essentials to keep you afloat into the competition. Along with this Web Application Penetration Testing & Security course, you will get 10 other premium courses. Also, you will get an original Hardcopy and PDF certificate for the title course and a student ID card absolutely free. This Bundle Consists of the following Premium courses: Course 01: Complete Web Application Penetration Testing & Security Course 02: Secure Programming of Web Applications Course 03: GDPR Data Protection Level 5 Course 04: Front End Web Development Diploma Course 05: Modern Web Designing - Level 2 Course 06: WebGL 2D/3D Programming and Graphics Rendering Course 07: Python for Data Analysis Course 08: Network Security Level 2 Course 09: SQL Server for Beginners Course 10: Functional Skills IT Course 11: Level 2 Microsoft Office Essentials As one of the top course providers in the UK, we're committed to providing you with the best educational experience possible. Our industry experts have designed the Web Application Penetration Testing & Security to empower you to learn all at once with accuracy. You can take the course at your own pace - anytime, from anywhere. So, enrol now to advance your career! Benefits you'll get choosing Apex Learning for this Web Application Penetration Testing & Security: One payment, but lifetime access to 11 CPD courses Certificate, student ID for the title course included in a one-time fee Full tutor support available from Monday to Friday Free up your time - don't waste time and money travelling for classes Accessible, informative modules taught by expert instructors Get 24/7 help or advice from our email and live chat teams Learn at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills How will I get my Certificate? After successfully completing the course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificates: Free (Previously it was £10*11= £110) Hard Copy Certificates: Free (Previously it was £20*11= £220) Curriculum of the Bundle Course 01: Complete Web Application Penetration Testing & Security Unit 01: Introduction Unit 02: BE PREPARED Unit 03: WEB APPLICATION TECHNOLOGIES Unit 04: INFORMATION GATHERING - MAPPING THE APPLICATIONS Unit 05: CROSS-SITE SCRIPTING ATTACKS - XSS Unit 06: SQL INJECTION ATTACKS - EXPLOITATIONS Unit 07: CROSS SITE REQUEST FORGERY - XSRF Unit 08: AUTHENTICATION & AUTHORIZATION ATTACKS Unit 09: CLIENT SIDE SECURITY TESTING Unit 10: FILE RELATED VULNERABILITIES Unit 11: XML EXTERNAL ENTITY ATTACKS - XXE Course 02: Secure Programming of Web Applications Section 01: Introduction Section 02: Well-known Vulnerabilities and Secure Programming Section 03: Conclusion and Summary Course 03: GDPR Data Protection Level 5 Module 01: GDPR Basics Module 02: GDPR Explained Module 03: Lawful Basis for Preparation Module 04: Rights and Breaches Module 05: Responsibilities and Obligations Course 04: Front End Web Development Diploma Welcome to the course! Web Development Basics - HTML Advanced HTML Concepts Introduction to Cascading Style Sheets (CSS) Advanced CSS JavaScript for Begeinners More JavaScript Concepts Getting Started with jQuery More jQuery Bootstrap Basics Project #2 - Pipboy from Fallout 4 Project #3 - Google Chrome Extension BONUS - Coding Another Google Chrome Extension Course 05: Modern Web Designing - Level 2 Module: 01 1.1 Intro 1.2 Install the Tools and Get Started Module: 02 2.1 Intro to HTML 2.2 What is HTML 2.3 Start a New HTML File & Use Tags 2.4 Header Tags 2.5 Edit Text 2.6 Links 2.7 Images 2.8 Lists 2.9 Challenge 1 2.10 HTML Outro Module: 03 3.1 CSS Intro 3.2 Add CSS Styles 3.3 Classes and IDs 3.4 Borders 3.5 Sizing 3.6 Padding and Margin 3.7 Text Styles 3.8 DIVs 3.9 Postioning 3.10 Hover 3.11 Easily Center Elements 3.12 Fonts 3.13 Challenge 3.14 CSS Outro Module: 04 4.1 Intro to Bootstrap 4.2 Install Bootstrap 4.3 Indenting and Containers 4.4 The Grid System 4.5 Images 4.6 Buttons 4.7 Challenge 3 4.8 Bootstrap Outro Module: 05 5.1 Landing Page Intro 5.2 Sketch Your Landing Page 5.3 The Top Section 5.4 Polish the Top Section 5.5 Adding Images 5.6 The Main Points Section 5.7 Collecting Emails With an Opt-In Form 5.8 Challenge 4 5.9 Landing Page Outro Module: 06 6.1 Business Site Intro 6.2 Sketch Up 6.3 Using Fancy Font Logo 6.4 Carousel Basics 6.5 Carousel Extras 6.6 Text on Images 6.7 Phone Number Icon 6.8 Google Maps 6.9 Font Awesome 6.10 Challenge 4 6.11 Business Site Outro Module: 07 7.1 Intro 7.2 Portfolio Sketch 7.3 Jumbotron 7.4 Nav Bar 7.5 Panels 7.6 Challenge 5 7.7 Portfolio Outre Module: 08 8.1 Hosting 8.2 Bluehost 8.3 Uploading 8.4 Tips 8.5 Hosting Outro Course 06: WebGL 2D/3D Programming and Graphics Rendering Section 01: Introduction Section 02: Drawing Objects Section 03: Colours and Textures Section 04: Moving & Transforming Objects Section 05: Movement & Camera Section 06: Lighting & Shading Course 07: Python for Data Analysis Welcome, Course Introduction & overview, and Environment set-up Python Essentials Python for Data Analysis using NumPy Python for Data Analysis using Pandas Python for Data Visualization using matplotlib Python for Data Visualization using Seaborn Python for Data Visualization using pandas Python for interactive & geographical plotting using Plotly and Cufflinks Capstone Project - Python for Data Analysis & Visualization Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Python for Machine Learning - scikit-learn - Logistic Regression Model Python for Machine Learning - scikit-learn - K Nearest Neighbors Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Python for Machine Learning - scikit-learn - K Means Clustering Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Recommender Systems with Python - (Additional Topic) Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Course 08: Network Security Level 2 Security and Risk Management Asset Security Security Engineering Communication and Network Security Identity and Access Management Security Assessment and Testing Security Operations Software Development Security Course 09: SQL Server for Beginners Introduction Setup Basic queries Security MSSQL an ddifferent drivers Files General Course 10: Functional Skills IT Section 1: Introduction Section 2: Information Technology Section 3: Components of IT Section 4: Hardware Section 5: Operating System Section 6: Application/Software Section 7: Networking Section 8: Security Section 9: Traffic Flow & Enterprise Level IT Components Section 10: Storage Section 11: Database Section 12: Virtualisation & Cloud Section 13: Management & Other IT Jobs Course 11: Level 2 Microsoft Office Essentials Excel 2016 Getting Started with Microsoft Office Excel 2016 Performing Calculations Modifying a Worksheet Formatting a Worksheet Printing Workbooks Managing Workbooks Word 2016 Getting Started with Word Formatting Text and Paragraphs Working More Efficiently Managing Lists Adding Tables Inserting Graphic Objects Controlling Page Appearance Preparing to Publish a Document Workbooks - Microsoft Word 2016 (Beginner) PowerPoint 2016 PowerPoint Interface Presentation Basics Formatting Inserting Options Working with Objects Table Charts Review and Presentatin Access 2016 Introduction to Access Modify Data Working with Queries Access Forms Working with Reports CPD 135 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Web Application Penetration Testing & Security bundle. Persons with similar professions can also refresh or strengthen their skills by enrolling in this course. Students can take this course to gather professional knowledge besides their study or for the future. Requirements Our Web Application Penetration Testing & Security 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 Having these various expertise will increase the value in your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included

In this self-paced course, you will learn how to use TensorFlow 2 to build deep neural networks. You will learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial and biological neural networks and how that inspires our thinking in deep learning.

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.

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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. “Introduction to Quantitative and Computational Finance” provides a basis to step into the world of Quantum Computing for Finance. This introductory course will develop fundamental concepts required for an understanding of quantum algorithms and more advanced topics in computational finance. Through this course, you will learn the basics of derivative products, the Black-Scholes-Merton model for pricing vanilla derivatives, and how to compute the price of exotic options with a computer. This course is designed for all those who wish to develop their skills and start a career in quantitative finance. This course is the first part of the specialised training program: “Quantum Computing for Finance”. What Skills you will learn The fundamentals of derivative products, their types – forwards and options, and their pricing. An understanding of the Black-Scholes-Merton model, hedging and volatility modelling. The computational and modelling techniques for pricing options such as Monte-Carlo simulations and the Finite Difference method. A strong foundation in quantitative and computational skills for modelling and solving complex financial problems using Python programming language. The skills for a career in the finance industry, including quantitative asset management and trading, financial engineering, risk management, and applied research. Course Prerequisites All potential learners should have prior knowledge of the following content areas, either through completion of academic studies or relative professional preparation: Basic calculus (partial derivatives) Probability theory (with an exposure to measure theory if possible) Basic linear algebra (matrix operations) Numerical Python (NumPy essentially) The course contains several Python based programming exercises. We recommend that you install Python on your local system to practice and implement the programs explained throughout the course. For instructions and tutorials for beginners, please click on the following link: Python installation instructions and tutorials for beginners Duration The estimated duration to complete this course is approximately 4 weeks (~3hrs/week). Course assessment To complete the course and earn certification, you must pass all the quizzes at the end of each lesson by scoring 80% or more. 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.

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