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In the vast realm of digital literacy, where every keystroke holds the potential to shape the future, the Level 5 Diploma in Functional Skills IT emerges as the veritable key to unlock the doors of proficiency in Functional Skills IT. Embark on a journey that transcends the ordinary, delving into the intricate tapestry of computer sciences with a course designed to fortify your understanding across 15 meticulously crafted sections. From unraveling the mystique of System Hardware to navigating the labyrinth of IT Security Threat Mitigation, each section is a stepping stone towards a mastery of Functional Skills IT. Picture yourself seamlessly navigating the digital landscape, armed with a profound comprehension of how people interact with computers, deciphering the nuances of data storage, and configuring both PCs and mobile devices with finesse. It's a course that immerses you in the core fabric of IT, where understanding databases and developing software aren't just skills, but gateways to a realm where each line of code tells a story. The Functional Skills IT course is not just an education; it's a revelation, with every section converging to empower you with a comprehensive grasp of the essentials. Course Learning Outcomes: Immerse yourself in the intricate world of System Hardware and Device Ports, mastering the foundations of IT. Develop a nuanced understanding of Data Storage, Sharing, and Operating Systems, laying the groundwork for advanced knowledge. Navigate the complexities of Network and Internet Connectivity, fortifying your grasp on the digital highways. Tackle IT Security Threats head-on, with a comprehensive approach to mitigation strategies. Hone your skills in Computer Maintenance, Management, and Troubleshooting, ensuring a seamless digital experience. Level 5 Diploma in Functional Skills IT Section 01: How People Use Computers Section 02: System Hardware Section 03: Device Ports and Peripherals Section 04: Data Storage and Sharing Section 05: Understanding Operating Systems Section 06: Setting Up and Configuring a PC Section 07: Setting Up and Configuring a Mobile Device Section 08: Managing Files Section 09: Using and Managing Application Software Section 10: Configuring Network and Internet Connectivity Section 11: IT Security Threat Mitigation Section 12: Computer Maintenance and Management Section 13: IT Troubleshooting Section 14: Understanding Databases Section 15: Developing and Implementing Software Certificate of Achievement Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for Free to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Who is this course for? Level 5 Diploma in Functional Skills IT IT Support Specialist Data Analyst Network Administrator Systems Analyst Database Administrator Software Developer Requirements Level 5 Diploma in Functional Skills IT To enrol in this Level 5 Diploma in Functional Skills IT, Purchasing & Procurement course, all you need is a basic understanding of the English Language and an internet connection. Career path Level 5 Diploma in Functional Skills IT IT Support Technician: £20,000 to £30,000 per year Data Entry Specialist: £18,000 to £25,000 per year IT Administrator: £25,000 to £35,000 per year Desktop Support Analyst: £22,000 to £32,000 per year Technical Support Representative: £20,000 to £28,000 per year IT Helpdesk Operator: £18,000 to £26,000 per year Certificates CPD Accredited PDF Certificate Digital certificate - Included QLS Endorsed Hard Copy Certificate Hard copy certificate - Included CPD Accredited Hard Copy Certificate Hard copy certificate - £9.99 CPD Accredited Hard Copy Certificate Delivery Charge: Inside the UK: Free Outside of the UK: £9.99 each

Duration 2 Days 12 CPD hours This course is intended for This is an introductory-level course for Administrators who are new to Jira (this is NOT for experienced Jira admin or users). Students should have a background in basic administration. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment, exploring several practical use cases that provide context as to where and when to use Jira, students will learn about: user management global and project permissions project roles schemes configuration of issue types, workflows, and screens Tracking issues is a critical component of any project management strategy. JIRA provides a web based single repository for creating, tracking and reporting on feature requests, bugs reported, or managing workflow. Geared for administrators new to Jira, JumpStart to Jira for Administrators | Jira Administration is a two-day, hands-on course that explores the most important tasks required to set up Jira, providing students with ample hands-on experience using common administration tasks. This hands-on course enables the Student to administer a JIRA instance and ?learn by doing?. The focus of this course is on Best Practices, and practical skills. Getting started with JIRA Administration JIRA conceptual review Core concepts Terminology Infrastructure JIRA roles Groups vs Roles Overview Project roles Creating a role Project scaling JIRA User management Project Resolution Project status Resolved status Resolution date Schemes Overview Project scope schemes Adding users to schemes Issue type schemes Notification schemes Permission schemes Issue security schemes working with schemes JIRA as a Platform Overview What can be configured Basic JIRA project setup Advanced project setup Workflows Overview Designing a workflow Defining a workflow Implementing a workflow Deploying workflows Workflow events Transitions and sub-tasks Custom Fields Overview Field types Field context Limiting contexts Adding contexts Screens and field configuration Best practices for custom fields User Lifecycle Overview Adding users Adding third-party users Modifying users Deactivating users Remote JIRA Access Overview Emails SQL REST Webhooks XML and RSS Command Line Interface Integrating JIRA with other applications Migrating Data into JIRA Overview Migration steps The CSV importer JIRA cloud migration Summary and Best Practices Looking back at the ?Big Picture? Optional - Jira Certification Prep Review Additional course details: Nexus Humans JumpStart to Jira for Administrators | Jira Administration (TTDV7540) 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 JumpStart to Jira for Administrators | Jira Administration (TTDV7540) 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.

Duration 3 Days 18 CPD hours

Duration 1 Days 6 CPD hours

Duration 4 Days 24 CPD hours Overview Topics Include:Installation of a multi-node Kubernetes cluster using kubeadm, and how to grow a cluster.Choosing and implementing cluster networking.Various methods of application lifecycle management, including scaling, updates and roll-backs.Configuring security both for the cluster as well as containers.Managing storage available to containers.Learn monitoring, logging and troubleshooting of containers and the cluster.Configure scheduling and affinity of container deployments.Use Helm and Charts to automate application deployment.Understand Federation for fault-tolerance and higher availability. In this vendor agnostic course, you'll learn the installation, configuration and administration of a production-grade Kubernetes cluster. Introduction Linux Foundation Linux Foundation Training Linux Foundation Certifications Laboratory Exercises, Solutions and Resources Distribution Details Labs Basics of Kubernetes Define Kubernetes Cluster Structure Adoption Project Governance and CNCF Labs Installation and Configuration Getting Started With Kubernetes Minikube kubeadm More Installation Tools Labs Kubernetes Architecture Kubernetes Architecture Networking Other Cluster Systems Labs APIs and Access API Access Annotations Working with A Simple Pod kubectl and API Swagger and OpenAPI Labs API Objects API Objects The v1 Group API Resources RBAC APIs Labs Managing State With Deployments Deployment Overview Managing Deployment States Deployments and Replica Sets DaemonSets Labels Labs Services Overview Accessing Services DNS Labs Volumes and Data Volumes Overview Volumes Persistent Volumes Passing Data To Pods ConfigMaps Labs Ingress Overview Ingress Controller Ingress Rules Labs Scheduling Overview Scheduler Settings Policies Affinity Rules Taints and Tolerations Labs Logging and Troubleshooting Overview Troubleshooting Flow Basic Start Sequence Monitoring Logging Troubleshooting Resources Labs Custom Resource Definition Overview Custom Resource Definitions Aggregated APIs Labs Kubernetes Federation Overview Federated Resources Labs Helm Overview Helm Using Helm Labs Security Overview Accessing the API Authentication and Authorization Admission Controller Pod Policies Network Policies Labs

Duration 5 Days 30 CPD hours

HTML is one of the primary and vital languages you need to get your hands on if you want to pursue a career in the IT industry. The HTML Crash Course is here to provide you with the essential knowledge and ability to work with this programming language. In this comprehensive HTML Crash Course, you will learn about the fundamentals of the HTML programming language. The interactive modules will show you the steps of installing HTML. You will receive detailed lessons on HTML tags, lists, forms etc. In addition, the course will also enrich your knowledge and understanding of HTML entities. From this HTML Crash Course, you will get both a theoretical and practical understanding of HTML. This course is the perfect place to establish a solid foundation in this highly beneficial programming language. You will also receive a valuable certificate after completing the course. Join now and increase your employability in the IT sector. Learning Objectives Familiarise yourself with the core concept of HTML Understand the process of HTML installation Enrich your knowledge of HTML tags, lists, forms etc Learn about HTML entities Who is this Course for? Whether you are a beginner or an existing practitioner, our CPD accredited HTML Crash Course is perfect for you to gain extensive knowledge about different aspects of the relevant industry to hone your skill further. It is also great for working professionals who have acquired practical experience but require theoretical knowledge with a credential to support their skill, as we offer CPD accredited certification to boost up your resume and promotion prospects. Entry Requirement Anyone interested in learning more about this subject should take this HTML Crash Course. This course will help you grasp the basic concepts as well as develop a thorough understanding of the subject. The course is open to students from any academic background, as there is no prerequisites to enrol on this course. The course materials are accessible from an internet enabled device at anytime of the day. CPD Certificate from Course Gate At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £4.99 and the hard copy for £9.99. Also, you can order both PDF and hardcopy certificates for £12.99. Career path On successfully completing the HTML Crash Course, learners can progress to a more advanced program from our course list. Career opportunities in this field include freelancing or working in-house, within a range of professional settings, with the opportunity to earn a high salary. Related professions in this industry include: Computer programmer Web developers Web designers Graphic designer Course Curriculum Module 01: Setup and Installation Installation And Setup 00:06:00 Module 02: Learning HTML5 HTML Tags 00:17:00 Lists 00:12:00 Forms 00:12:00 Tables 00:05:00 Html Entities 00:07:00 Module 03: MEGA PROJECT MEGA PROJECT 00:02:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

Python Certification - Beginner to Expert offers a comprehensive journey from novice to proficient Python programmer. This course is structured into 18 sections, covering essential topics such as software installation, conditional branching, file handling, and database management.

Unlock the power of data with our Basic Data Analysis Course. Learn essential techniques to interpret and draw insights from raw information. Enroll now to kickstart your data analysis journey and make informed decisions in any field.

Overview of Data Science & Machine Learning with Python Join our Data Science & Machine Learning with Python course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Data Science & Machine Learning with Python course has everything you need to get a great start in this sector. Improving and moving forward is key to getting ahead personally. The Data Science & Machine Learning with Python course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now! This Data Science & Machine Learning with Python Course will help you to learn: Learn strategies to boost your workplace efficiency. Hone your skills to help you advance your career. Acquire a comprehensive understanding of various topics and tips. Learn in-demand skills that are in high demand among UK employers This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Data Science & Machine Learning with Python. Enrol today and complete the course to achieve a certificate that can change your career forever. Details Perks of Learning with IOMH One-To-One Support from a Dedicated Tutor Throughout Your Course. Study Online - Whenever and Wherever You Want. Instant Digital/ PDF Certificate. 100% Money Back Guarantee. 12 Months Access. Process of Evaluation After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate. Certificate of Achievement Certificate of Completion - Digital / PDF Certificate After completing the Data Science & Machine Learning with Python course, you can order your CPD Accredited Digital / PDF Certificate for £5.99.  Certificate of Completion - Hard copy Certificate You can get the CPD Accredited Hard Copy Certificate for £12.99. Shipping Charges: Inside the UK: £3.99 International: £10.99 Who Is This Course for? This Data Science & Machine Learning with Python is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.  On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.  This course has been developed with maximum flexibility and accessibility, making it ideal for people who don't have the time to devote to traditional education. Requirements You don't need any educational qualification or experience to enrol in the Data Science & Machine Learning with Python course. Do note: you must be at least 16 years old to enrol. Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course. Career Path The certification and skills you get from this Data Science & Machine Learning with Python Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors. Course Curriculum Course Overview & Table of Contents Course Overview & Table of Contents 00:09:00 Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types Introduction to Machine Learning - Part 1 - Concepts , Definitions and Types 00:05:00 Introduction to Machine Learning - Part 2 - Classifications and Applications Introduction to Machine Learning - Part 2 - Classifications and Applications 00:06:00 System and Environment preparation - Part 1 System and Environment preparation - Part 1 00:04:00 System and Environment preparation - Part 2 System and Environment preparation - Part 2 00:06:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 1 00:10:00 Learn Basics of python - Assignment Learn Basics of python - Assignment 2 00:09:00 Learn Basics of python - Functions Learn Basics of python - Functions 00:04:00 Learn Basics of python - Data Structures Learn Basics of python - Data Structures 00:12:00 Learn Basics of NumPy - NumPy Array Learn Basics of NumPy - NumPy Array 00:06:00 Learn Basics of NumPy - NumPy Data Learn Basics of NumPy - NumPy Data 00:08:00 Learn Basics of NumPy - NumPy Arithmetic Learn Basics of NumPy - NumPy Arithmetic 00:04:00 Learn Basics of Matplotlib Learn Basics of Matplotlib 00:07:00 Learn Basics of Pandas - Part 1 Learn Basics of Pandas - Part 1 00:06:00 Learn Basics of Pandas - Part 2 Learn Basics of Pandas - Part 2 00:07:00 Understanding the CSV data file Understanding the CSV data file 00:09:00 Load and Read CSV data file using Python Standard Library Load and Read CSV data file using Python Standard Library 00:09:00 Load and Read CSV data file using NumPy Load and Read CSV data file using NumPy 00:04:00 Load and Read CSV data file using Pandas Load and Read CSV data file using Pandas 00:05:00 Dataset Summary - Peek, Dimensions and Data Types Dataset Summary - Peek, Dimensions and Data Types 00:09:00 Dataset Summary - Class Distribution and Data Summary Dataset Summary - Class Distribution and Data Summary 00:09:00 Dataset Summary - Explaining Correlation Dataset Summary - Explaining Correlation 00:11:00 Dataset Summary - Explaining Skewness - Gaussian and Normal Curve Dataset Summary - Explaining Skewness - Gaussian and Normal Curve 00:07:00 Dataset Visualization - Using Histograms Dataset Visualization - Using Histograms 00:07:00 Dataset Visualization - Using Density Plots Dataset Visualization - Using Density Plots 00:06:00 Dataset Visualization - Box and Whisker Plots Dataset Visualization - Box and Whisker Plots 00:05:00 Multivariate Dataset Visualization - Correlation Plots Multivariate Dataset Visualization - Correlation Plots 00:08:00 Multivariate Dataset Visualization - Scatter Plots Multivariate Dataset Visualization - Scatter Plots 00:05:00 Data Preparation (Pre-Processing) - Introduction Data Preparation (Pre-Processing) - Introduction 00:09:00 Data Preparation - Re-scaling Data - Part 1 Data Preparation - Re-scaling Data - Part 1 00:09:00 Data Preparation - Re-scaling Data - Part 2 Data Preparation - Re-scaling Data - Part 2 00:09:00 Data Preparation - Standardizing Data - Part 1 Data Preparation - Standardizing Data - Part 1 00:07:00 Data Preparation - Standardizing Data - Part 2 Data Preparation - Standardizing Data - Part 2 00:04:00 Data Preparation - Normalizing Data Data Preparation - Normalizing Data 00:08:00 Data Preparation - Binarizing Data Data Preparation - Binarizing Data 00:06:00 Feature Selection - Introduction Feature Selection - Introduction 00:07:00 Feature Selection - Uni-variate Part 1 - Chi-Squared Test Feature Selection - Uni-variate Part 1 - Chi-Squared Test 00:09:00 Feature Selection - Uni-variate Part 2 - Chi-Squared Test Feature Selection - Uni-variate Part 2 - Chi-Squared Test 00:10:00 Feature Selection - Recursive Feature Elimination Feature Selection - Recursive Feature Elimination 00:11:00 Feature Selection - Principal Component Analysis (PCA) Feature Selection - Principal Component Analysis (PCA) 00:09:00 Feature Selection - Feature Importance Feature Selection - Feature Importance 00:06:00 Refresher Session - The Mechanism of Re-sampling, Training and Testing Refresher Session - The Mechanism of Re-sampling, Training and Testing 00:12:00 Algorithm Evaluation Techniques - Introduction Algorithm Evaluation Techniques - Introduction 00:07:00 Algorithm Evaluation Techniques - Train and Test Set Algorithm Evaluation Techniques - Train and Test Set 00:11:00 Algorithm Evaluation Techniques - K-Fold Cross Validation Algorithm Evaluation Techniques - K-Fold Cross Validation 00:09:00 Algorithm Evaluation Techniques - Leave One Out Cross Validation Algorithm Evaluation Techniques - Leave One Out Cross Validation 00:05:00 Algorithm Evaluation Techniques - Repeated Random Test-Train Splits Algorithm Evaluation Techniques - Repeated Random Test-Train Splits 00:07:00 Algorithm Evaluation Metrics - Introduction Algorithm Evaluation Metrics - Introduction 00:09:00 Algorithm Evaluation Metrics - Classification Accuracy Algorithm Evaluation Metrics - Classification Accuracy 00:08:00 Algorithm Evaluation Metrics - Log Loss Algorithm Evaluation Metrics - Log Loss 00:03:00 Algorithm Evaluation Metrics - Area Under ROC Curve Algorithm Evaluation Metrics - Area Under ROC Curve 00:06:00 Algorithm Evaluation Metrics - Confusion Matrix Algorithm Evaluation Metrics - Confusion Matrix 00:10:00 Algorithm Evaluation Metrics - Classification Report Algorithm Evaluation Metrics - Classification Report 00:04:00 Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction Algorithm Evaluation Metrics - Mean Absolute Error - Dataset Introduction 00:06:00 Algorithm Evaluation Metrics - Mean Absolute Error Algorithm Evaluation Metrics - Mean Absolute Error 00:07:00 Algorithm Evaluation Metrics - Mean Square Error Algorithm Evaluation Metrics - Mean Square Error 00:03:00 Algorithm Evaluation Metrics - R Squared Algorithm Evaluation Metrics - R Squared 00:04:00 Classification Algorithm Spot Check - Logistic Regression Classification Algorithm Spot Check - Logistic Regression 00:12:00 Classification Algorithm Spot Check - Linear Discriminant Analysis Classification Algorithm Spot Check - Linear Discriminant Analysis 00:04:00 Classification Algorithm Spot Check - K-Nearest Neighbors Classification Algorithm Spot Check - K-Nearest Neighbors 00:05:00 Classification Algorithm Spot Check - Naive Bayes Classification Algorithm Spot Check - Naive Bayes 00:04:00 Classification Algorithm Spot Check - CART Classification Algorithm Spot Check - CART 00:04:00 Classification Algorithm Spot Check - Support Vector Machines Classification Algorithm Spot Check - Support Vector Machines 00:05:00 Regression Algorithm Spot Check - Linear Regression Regression Algorithm Spot Check - Linear Regression 00:08:00 Regression Algorithm Spot Check - Ridge Regression Regression Algorithm Spot Check - Ridge Regression 00:03:00 Regression Algorithm Spot Check - Lasso Linear Regression Regression Algorithm Spot Check - Lasso Linear Regression 00:03:00 Regression Algorithm Spot Check - Elastic Net Regression Regression Algorithm Spot Check - Elastic Net Regression 00:02:00 Regression Algorithm Spot Check - K-Nearest Neighbors Regression Algorithm Spot Check - K-Nearest Neighbors 00:06:00 Regression Algorithm Spot Check - CART Regression Algorithm Spot Check - CART 00:04:00 Regression Algorithm Spot Check - Support Vector Machines (SVM) Regression Algorithm Spot Check - Support Vector Machines (SVM) 00:04:00 Compare Algorithms - Part 1 : Choosing the best Machine Learning Model Compare Algorithms - Part 1 : Choosing the best Machine Learning Model 00:09:00 Compare Algorithms - Part 2 : Choosing the best Machine Learning Model Compare Algorithms - Part 2 : Choosing the best Machine Learning Model 00:05:00 Pipelines : Data Preparation and Data Modelling Pipelines : Data Preparation and Data Modelling 00:11:00 Pipelines : Feature Selection and Data Modelling Pipelines : Feature Selection and Data Modelling 00:10:00 Performance Improvement: Ensembles - Voting Performance Improvement: Ensembles - Voting 00:07:00 Performance Improvement: Ensembles - Bagging Performance Improvement: Ensembles - Bagging 00:08:00 Performance Improvement: Ensembles - Boosting Performance Improvement: Ensembles - Boosting 00:05:00 Performance Improvement: Parameter Tuning using Grid Search Performance Improvement: Parameter Tuning using Grid Search 00:08:00 Performance Improvement: Parameter Tuning using Random Search Performance Improvement: Parameter Tuning using Random Search 00:06:00 Export, Save and Load Machine Learning Models : Pickle Export, Save and Load Machine Learning Models : Pickle 00:10:00 Export, Save and Load Machine Learning Models : Joblib Export, Save and Load Machine Learning Models : Joblib 00:06:00 Finalizing a Model - Introduction and Steps Finalizing a Model - Introduction and Steps 00:07:00 Finalizing a Classification Model - The Pima Indian Diabetes Dataset Finalizing a Classification Model - The Pima Indian Diabetes Dataset 00:07:00 Quick Session: Imbalanced Data Set - Issue Overview and Steps Quick Session: Imbalanced Data Set - Issue Overview and Steps 00:09:00 Iris Dataset : Finalizing Multi-Class Dataset Iris Dataset : Finalizing Multi-Class Dataset 00:09:00 Finalizing a Regression Model - The Boston Housing Price Dataset Finalizing a Regression Model - The Boston Housing Price Dataset 00:08:00 Real-time Predictions: Using the Pima Indian Diabetes Classification Model Real-time Predictions: Using the Pima Indian Diabetes Classification Model 00:07:00 Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset Real-time Predictions: Using Iris Flowers Multi-Class Classification Dataset 00:03:00 Real-time Predictions: Using the Boston Housing Regression Model Real-time Predictions: Using the Boston Housing Regression Model 00:08:00 Resources Resources - Data Science & Machine Learning with Python 00:00:00