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24-Hour Knowledge Knockdown! Prices Reduced Like Never Before Did you know that Python is the fastest-growing programming language in the world? In the UK alone, the demand for Python developers has grown by a staggering 38% in the last year! If you're not one of them, now is the perfect time to get on board. And with our Diploma in Python Programming bundle, you can do just that. This diploma takes you on a structured learning journey, starting with the fundamentals of Python programming. You'll master essential concepts like data structures, control flow, and functions. As you progress, you'll delve into advanced topics like data science, machine learning, and even explore its application in specialised fields like spatial analysis. By the program's end, you'll be proficient in writing Python scripts to solve complex problems, analyse data, and automate tasks. With a single payment, you will gain access to Python Programming course, including 10 premium courses, a QLS Endorsed Hardcopy certificate (for the title course) and 11 PDF certificates for Absolutely free. This Diploma in Python Programming Bundle Package includes: Main Course: Diploma in Python Programming at QLS Level 5 10 Premium Additional CPD QS Accredited Courses - Course 01: Learn Programming with Python Course 02: Python Data Science Course 03: An Introduction to Graph Theory Course 04: Cloud Computing / CompTIA Cloud+ (CV0-002) Course 05: Level-3 Machine Learning Course with Python Course 06: Learn to Use Python for Spatial Analysis in ArcGIS Course 07: Higher Order Functions in Python - Level 03 Course 08: Easy to Advanced Data Structures Course 09: Introduction to Data Analysis Course 10: Business Intelligence and Data Mining Diploma With so much valuable content included, it's no wonder that this bundle is quickly becoming one of our most popular offerings. So why wait? Enrol today and start your journey towards becoming a Python expert. Learning Outcomes of Python Programming Grasp fundamental Python concepts like variables, data types, operators, and control flow. Master essential Python libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Gain expertise in data analysis techniques and explore machine learning algorithms using Python. Implement Python for cloud deployment using platforms like AWS or Azure (course dependent). Utilise Python for spatial data analysis with ArcGIS (course dependent). Develop proficiency in writing clean, efficient, and maintainable Python code. Why Choose Us? Get a Free QLS Endorsed Certificate upon completion of Python Programming Get a free student ID card with Python Programming Training program (£10 postal charge will be applicable for international delivery) The Python Programming is affordable and simple to understand This course is entirely online, interactive lesson with voiceover audio Get Lifetime access to the Python Programming course materials The Python Programming comes with 24/7 tutor support Start your learning journey straightaway! *** Course Curriculum *** Main Course: Diploma in Python Programming at QLS Level 5 Unit 01: Introduction To Python Programming Unit 02: Getting Started With Python Unit 03: Conditional Branching With Python Unit 04: Importing External/Internal Library In Python Unit 05: Project Rock Paper And Scissors Unit 06: Strings Operation In Python Unit 07: Date And Time In Python Unit 08: File Handling, Read And Write Using Python Unit 09: Data Storage Structures, Tuple, List And Dictionary Unit 10: Writing User Functions In Python Unit 11: Sending Mail Unit 12: Import Tricks In Python Unit 13: Import Operating System And Platform Unit 14: Exceptions Handling In Python Unit 15: Installing Packages And Scheduling In Python Unit 16: Data Base In Python Using Sqlite Unit 17: Running Program From Command Prompt And Jupyter Notebook Unit 18: Conclusion Course 01: Learn Programming with Python Module 01 A Installing Python FREE Documentation Command Line Variables Simple Python Syntax Keywords Import Module Module 02 Additional Topics If Elif Else Iterable For Loops Execute Exceptions Module 03 Data Types Number Types More Number Types Strings More Strings Files Lists Dictionaries Tuples Sets Course 02: Python Data Science Unit 01: Introduction To Python Data Science Module 01: Introduction to Python Data Science Module 02: Environment Setup Unit 02: Data Cleaning Packages Module 01: Numpy package for calculations Module 02: Panda package for Data cleaning Unit 03: Data Visualization Packages Module 01: Matplotlib Data Visualization Part 1 Module 02: Matplotlib Data Visualization Part 2 =========>>>>> And 8 More Courses <<<<<========= How will I get my Certificate? After successfully completing the course, you will be able to order your QLS Endorsed Certificates and CPD Accredited Certificates as proof of your achievement. PDF Certificate: Free (Previously it was £12.99*11 = £143) QLS Endorsed Hard Copy Certificate: Free (For The Title Course: Previously it was £119) CPD 255 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone interested in learning more about the topic is advised to take this bundle. This bundle is ideal for: Individuals who want to become proficient Python programmers. Professionals who want to enhance their programming skills. Students who want to pursue a career in data science or machine learning. Anyone interested in learning Python programming language. Requirements There is no formal requirements to enrol in this course. Career path This bundle will help you to develop your knowledge and skills to pursue different careers, such as Junior Python Developer: £25,000 - £40,000 per year. Data Analyst: £26,000 - £50,000 per year. Machine Learning Engineer: £40,000 - £80,000 per year. Data Scientist: £45,000 - £85,000 per year. Certificates CPD Accredited Digital Certificate Digital certificate - Included Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free. CPD QS Accredited Certificate Hard copy certificate - Included Please note that International students have to pay an additional £10 as a shipment fee. Diploma in Python Programming at QLS Level 5 Hard copy certificate - Included Please note that International students have to pay an additional £10 as a shipment fee.

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Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Our monthly Social Media Marketing Update will break down the need-to-know marketing trends across TikTok, Instagram, LinkedIn, YouTube and more!

Overview Mastering data science skills and expertise can open new doors of opportunities for you in a wide range of fields. Learn the fundamentals and develop a solid grasp of Python data science with the comprehensive Data Science with Python course. This course is designed to assist you in securing a valuable skill set and boosting your career. This course will provide you with quality training on the fundamentals of data analysis with Python. From the step-by-step learning process, you will learn the techniques of setting up the system. Then the course will teach you Python data structure and functions. You will receive detailed lessons on NumPy, Matplotlib, and Pandas. Furthermore, you will develop the skills for Algorithm Evaluation Techniques, visualising datasets and much more. After completing the course you will receive a certificate of achievement. This certificate will help you create an impressive resume. So join today! How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? This course Data Science with Python course is ideal for beginners in data science. It will help them develop a solid grasp of Python and help them pursue their dream career in the field of data science. Requirements The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush. Career Path This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as Data Analyst Data Scientist Data Manager Business Analyst And much more! Course Curriculum 90 sections • 90 lectures • 10:19:00 total length •Course Overview & Table of Contents: 00:09:00 •Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types: 00:05:00 •Introduction to Machine Learning - Part 2 - Classifications and Applications: 00:06:00 •System and Environment preparation - Part 1: 00:04:00 •System and Environment preparation - Part 2: 00:06:00 •Learn Basics of python - Assignment 1: 00:10:00 •Learn Basics of python - Assignment 2: 00:09:00 •Learn Basics of python - Functions: 00:04:00 •Learn Basics of python - Data Structures: 00:12:00 •Learn Basics of NumPy - NumPy Array: 00:06:00 •Learn Basics of NumPy - NumPy Data: 00:08:00 •Learn Basics of NumPy - NumPy Arithmetic: 00:04:00 •Learn Basics of Matplotlib: 00:07:00 •Learn Basics of Pandas - Part 1: 00:06:00 •Learn Basics of Pandas - Part 2: 00:07:00 •Understanding the CSV data file: 00:09:00 •Load and Read CSV data file using Python Standard Library: 00:09:00 •Load and Read CSV data file using NumPy: 00:04:00 •Load and Read CSV data file using Pandas: 00:05:00 •Dataset Summary - Peek, Dimensions and Data Types: 00:09:00 •Dataset Summary - Class Distribution and Data Summary: 00:09:00 •Dataset Summary - Explaining Correlation: 00:11:00 •Dataset Summary - Explaining Skewness - Gaussian and Normal Curve: 00:07:00 •Dataset Visualization - Using Histograms: 00:07:00 •Dataset Visualization - Using Density Plots: 00:06:00 •Dataset Visualization - Box and Whisker Plots: 00:05:00 •Multivariate Dataset Visualization - Correlation Plots: 00:08:00 •Multivariate Dataset Visualization - Scatter Plots: 00:05:00 •Data Preparation (Pre-Processing) - Introduction: 00:09:00 •Data Preparation - Re-scaling Data - Part 1: 00:09:00 •Data Preparation - Re-scaling Data - Part 2: 00:09:00 •Data Preparation - Standardizing Data - Part 1: 00:07:00 •Data Preparation - Standardizing Data - Part 2: 00:04:00 •Data Preparation - Normalizing Data: 00:08:00 •Data Preparation - Binarizing Data: 00:06:00 •Feature Selection - Introduction: 00:07:00 •Feature Selection - Uni-variate Part 1 - Chi-Squared Test: 00:09:00 •Feature Selection - Uni-variate Part 2 - Chi-Squared Test: 00:10:00 •Feature Selection - Recursive Feature Elimination: 00:11:00 •Feature Selection - Principal Component Analysis (PCA): 00:09:00 •Feature Selection - Feature Importance: 00:06:00 •Refresher Session - The Mechanism of Re-sampling, Training and Testing: 00:12:00 •Algorithm Evaluation Techniques - Introduction: 00:07:00 •Algorithm Evaluation Techniques - Train and Test Set: 00:11:00 •Algorithm Evaluation Techniques - K-Fold Cross Validation: 00:09:00 •Algorithm Evaluation Techniques - Leave One Out Cross Validation: 00:05:00 •Algorithm Evaluation Techniques - Repeated Random Test-Train Splits: 00:07:00 •Algorithm Evaluation Metrics - Introduction: 00:09:00 •Algorithm Evaluation Metrics - Classification Accuracy: 00:08:00 •Algorithm Evaluation Metrics - Log Loss: 00:03:00 •Algorithm Evaluation Metrics - Area Under ROC Curve: 00:06:00 •Algorithm Evaluation Metrics - Confusion Matrix: 00:10:00 •Algorithm Evaluation Metrics - Classification Report: 00:04:00 •Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction: 00:06:00 •Algorithm Evaluation Metrics - Mean Absolute Error: 00:07:00 •Algorithm Evaluation Metrics - Mean Square Error: 00:03:00 •Algorithm Evaluation Metrics - R Squared: 00:04:00 •Classification Algorithm Spot Check - Logistic Regression: 00:12:00 •Classification Algorithm Spot Check - Linear Discriminant Analysis: 00:04:00 •Classification Algorithm Spot Check - K-Nearest Neighbors: 00:05:00 •Classification Algorithm Spot Check - Naive Bayes: 00:04:00 •Classification Algorithm Spot Check - CART: 00:04:00 •Classification Algorithm Spot Check - Support Vector Machines: 00:05:00 •Regression Algorithm Spot Check - Linear Regression: 00:08:00 •Regression Algorithm Spot Check - Ridge Regression: 00:03:00 •Regression Algorithm Spot Check - Lasso Linear Regression: 00:03:00 •Regression Algorithm Spot Check - Elastic Net Regression: 00:02:00 •Regression Algorithm Spot Check - K-Nearest Neighbors: 00:06:00 •Regression Algorithm Spot Check - CART: 00:04:00 •Regression Algorithm Spot Check - Support Vector Machines (SVM): 00:04:00 •Compare Algorithms - Part 1 : Choosing the best Machine Learning Model: 00:09:00 •Compare Algorithms - Part 2 : Choosing the best Machine Learning Model: 00:05:00 •Pipelines : Data Preparation and Data Modelling: 00:11:00 •Pipelines : Feature Selection and Data Modelling: 00:10:00 •Performance Improvement: Ensembles - Voting: 00:07:00 •Performance Improvement: Ensembles - Bagging: 00:08:00 •Performance Improvement: Ensembles - Boosting: 00:05:00 •Performance Improvement: Parameter Tuning using Grid Search: 00:08:00 •Performance Improvement: Parameter Tuning using Random Search: 00:06:00 •Export, Save and Load Machine Learning Models : Pickle: 00:10:00 •Export, Save and Load Machine Learning Models : Joblib: 00:06:00 •Finalizing a Model - Introduction and Steps: 00:07:00 •Finalizing a Classification Model - The Pima Indian Diabetes Dataset: 00:07:00 •Quick Session: Imbalanced Data Set - Issue Overview and Steps: 00:09:00 •Iris Dataset : Finalizing Multi-Class Dataset: 00:09:00 •Finalizing a Regression Model - The Boston Housing Price Dataset: 00:08:00 •Real-time Predictions: Using the Pima Indian Diabetes Classification Model: 00:07:00 •Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset: 00:03:00 •Real-time Predictions: Using the Boston Housing Regression Model: 00:08:00 •Resources - Data Science & Machine Learning with Python: 00:00:00

Duration 5 Days 30 CPD hours This course is intended for Experienced system administrators and network administrators Overview By the end of the course, you should be able to meet the following objectives: Describe the NSX Advanced Load Balancer architecture Describe the NSX Advanced Load Balancer components and main functions Explain the NSX Advanced Load Balancer key features and benefits Deploy and configure the NSX Advanced Load Balancer infrastructure within private or public clouds using Write and No Access Cloud Connectors Explain, deploy, and configure Service Engines Explain and configure local load balancing constructors such as virtual services, pools, health monitors, and related components Understand and modify application behavior through profiles, policies, and DataScripts Configure advanced services such as global server load balancing Describe how to use NSX Advanced Load Balancer REST API interfaces and related automation capabilities Describe and configure NSX Advanced Load Balancer application and infrastructure monitoring Gather relevant information and perform basic troubleshooting of applications that use built-in NSX Advanced Load Balancer tooling This five-day, fast-paced course provides comprehensive training to install, configure, and manage a VMware NSX© Advanced Load Balancer? (Avi Networks) solution. This course covers key NSX Advanced Load Balancer (Avi Networks) features and functionality offered in the NSX Advanced Load Balancer 20.x release. Features include the overall infrastructure, virtual services and application components, global server load balancing, various cloud connectors, application troubleshooting, and solution monitoring. Hands-on labs provide access to a software-defined data center environment to reinforce the skills and concepts presented in the course. Course Introduction Introduction and course logistics Course objectives Introduction to NSX Advanced Load Balancer Introduce NSX Advanced Load Balancer Discuss NSX Advanced Load Balancer use cases and benefits Explain NSX Advanced Load Balancer architecture and components Explain the management, control, data, and consumption planes and their respective functions Virtual Services Configuration Concepts Explain Virtual Service components Explain Virtual Service types Explain and configure basic Virtual Service components such as Application Profiles, Network Profiles Profiles and Policies Explain and deep dive on Advanced Virtual Service creation Explain and deep dive on Application Profiles and Types such as L4, DNS, Syslog, and HTTP Explain and configure advanced application HTTP Profile options Deep dive on Network Profiles and Types Explain and configure SSL Profiles and Certificates Explain and Configure HTTP and DNS policies Pools Configuration Concepts Explain and deep dive on Pools configuration options Describe available Load Balancing algorithms Explain multiple Health Monitor types Explain multiple Persistence Profiles Explain and configure Pool Groups Modifying Application Behavior Design and apply application solutions leveraging application profiles Design and apply application solutions leveraging Network and HTTP Policies and DataScripts Explain DataScript fundamentals Explain and leverage NSX Advanced Load Balancer analytics to understand application behavior Describe and configure Client SSL Certificate Validation Describe and configure Virtual Service DDoS, Rate Limiting, and Throttling capabilities Modify Network Profiles properties such as TCP connection properties Design and apply application solutions leveraging Persistence Profiles NSX Advanced Load Balancer Infrastructure Architecture Deep dive on the management, control, data, and consumption planes and functions Describe Control Plane Clustering and High Availability Describe Controller Process Sharding Describe Controller Sizing Describe Service Engine CPU and NIC Architecture Explain Tenants Deep dive and configure properties of Service Engine Groups Explain Service Engine Group High Availability Modes Describe and configure Active/Standby High Availability Mode Describe and configure Elastic HA High Availability Mode (Active/Active, N+M) Explain Service Engine Failure Detection and Self-Healing Describe Service Engine as a Router Deep dive on Virtual Service scale out options, such as Layer 2 (Native), Layer 3 (BGP), and DNS-based Introduction to Cloud Connector Introduce Cloud Connectors Review Cloud Connector integration modes Introduce Cloud Connector types Install, Configure and Manage NSX Advanced Load Balancer in No-Access Cloud Explain No Access Cloud concepts Configure No Access Cloud integration Explain and Configure Linux Server Cloud Describe the Advanced Configuration options available in Bare-Metal (Linux Server Cloud) Install, Configure and Manage NSX Advanced Load Balancer in VMware Environment: Cloud Configuration Introduce VMware integration options Explain and configure VMware No Access Cloud Connector Explain and configure VMware Write Access Cloud Connector Describe VMware Write with NSX-V Access Cloud Connector Describe VMware NSX-T integration AWS Cloud Configuration Describe NSX Advanced Load Balancer Public Cloud integrations Explain and demonstrate AWS Public Cloud Integration DNS Foundations Review, discuss, and explain DNS fundamentals Describe NSX Advanced Load Balancer DNS and IPAM providers Global Server Load Balancing Introduce Global Server Load Balancing Concepts and Benefits Explain and configure NSX Advanced Load Balancer infrastructure Explain and configure DNS Virtual Service components Explain and configure GSLB Service Engine Group Describe and configure GSLB Sites Explain and configure basic GSLB Services to include pools and health monitors Describe GSLB Service Load Balancing algorithms Explain and configure Data and Control Plane-based Health Monitors Describe GSLB Health Monitor Proxy NSX Advanced Load Balancer: Troubleshooting Introduce Infrastructure and Application Troubleshooting Concepts Describe Control Plane and Data Plane-based Troubleshooting Explain Application Analytics and Logs Describe client logs analysis Explain Headers troubleshooting and Packet Capture mechanism Leverage CLI for detailed data plane troubleshooting Explain Service Engine Logs Explain Health Monitors troubleshooting Explain BGP session troubleshooting Describe Control Plane Troubleshooting, Clustering, and Cloud Connector issues Events and Alerts Describe NSX Advanced Load Balancer Events Describe and configure NSX Advanced Load Balancer Alerts Describe NSX Advanced Load Balancer monitoring capabilities, leveraging SNMP, Syslog, and Email Introduction to NSX Advanced Load Balancer Rest API Introduce NSX Advanced Load Balancer REST API interface Describe REST API Object Schema Explain and interact with REST API interface, leveraging browser and command line utility Explain Swagger-based API documentation Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware NSX Advanced Load Balancer: Install, Configure, Manage [V20.x] training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the VMware NSX Advanced Load Balancer: Install, Configure, Manage [V20.x] course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

In today's rapidly evolving digital era, the fusion of finance and technology has paved the way for unprecedented opportunities. Enter the world of FinTech, Cryptocurrency, and the power of Data Analysis. With this 'Data Analytics (Data Analysis), FinTech and Cryptocurrency' bundle, you're taking the first step into a realm where Data Analysis isn't just a tool-it's the core of decision-making. Dive deep into the nuances of modern finance, learn the intricacies of Cryptocurrency, and harness the might of Data Analysis to make informed strategies. In the UK, professionals in these fields can enjoy impressive salary ranges, with earnings starting from £35,000 per year and reaching up to £80,000 per year, making it an enticing career choice. This bundle includes three courses that will equip you with the essential knowledge and skills to excel in this domain. This comprehensive Data Analysis bundle provides a valuable opportunity to explore the world of finance, technology, and data. By enrolling in these Data Analysis bundles, you will gain a deep understanding of the innovations shaping the financial industry, such as blockchain and artificial intelligence, and how they intersect with technology. Each Data Analytics (Data Analysis) course in FinTech and Cryptocurrency bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This Data Analysis bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Immerse yourself in these diverse, enthralling subjects, each designed to fuel your curiosity and enhance your knowledge. Dive in now! The courses in this Data Analysis bundle include: Course 1: FinTech Course 2: Cryptocurrency Course 3: Data Analytics Learning Outcomes: By completing this Data Analysis bundle, you will achieve the following learning outcomes: Understand the principles and applications of FinTech in the financial industry. Leverage Data Analysis for informed decision-making in finance and digital currencies. Use Data Analysis to forecast market trends in FinTech and Cryptocurrency. Apply statistical analysis techniques to interpret data effectively. Elevate financial proficiency by integrating insights from Data Analysis. Develop a strategic mindset for leveraging data analytics in FinTech and Cryptocurrency. The first course, FinTech, delves into the fascinating intersection of finance and technology. Gain a deep understanding of the technological innovations that are revolutionising the financial industry, including blockchain, artificial intelligence, and mobile banking. Explore the impact of digital currencies, peer-to-peer lending, and robo-advisors on traditional financial systems. The second course, Cryptocurrency, uncovers the secrets of this decentralised digital currency phenomenon. Discover the fundamentals of cryptocurrencies, such as Bitcoin and Ethereum, and explore the underlying blockchain technology. Dive into topics like mining, digital wallets, smart contracts, and the future of cryptocurrencies. Develop a solid foundation to navigate the complex world of digital assets. The third course, Data Analytics, equips you with the essential skills to extract insights from vast amounts of data. Learn the techniques and tools used to collect, clean, and analyze data, allowing you to make informed decisions and predictions. Dive into statistical analysis, data visualisation, and machine learning algorithms. Harness the power of data to drive business growth and enhance decision-making processes. CPD 15 CPD hours / points Accredited by CPD Quality Standards Who is this course for? This Data Analytics (Data Analysis) in FinTech and Cryptocurrency course is suitable for: Professionals aspiring to work in the FinTech or Cryptocurrency sectors. Financial analysts seeking to enhance their data analytics skills. Entrepreneurs who are interested in leveraging technology to innovate in the financial industry. Graduates looking to enter the finance or technology sectors with a competitive edge. Business professionals aiming to stay ahead of industry trends. Requirements You can delightfully enrol in this Data Analytics (Data Analysis) in FinTech and Cryptocurrency course without any formal requirements. Career path You can pursue various exciting career paths in FinTech and Cryptocurrency, including: Financial Data Analyst: £35,000 - £50,000 per year. Blockchain Developer: £45,000 - £75,000 per year. Cryptocurrency Investment Analyst: £50,000 - £80,000 per year. FinTech Consultant: £40,000 - £65,000 per year. Data Scientist (Financial Sector): £55,000 - £90,000 per year. Certificates Certificate Of Completion Digital certificate - Included Certificate Of Completion Hard copy certificate - £9.99