358 FITT courses

The Plumbing Skills Training Course is organised into 2 modules and includes everything you need to become successful in this profession. To make this course more accessible for you, we have designed it for both part-time and full-time students. You can study at your own pace or become an expert in just 9 hours! If you require support, our experienced tutors are always available to help you throughout the comprehensive syllabus of this course and answer all your queries through email. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Mock exams Multiple-choice assessment Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who is this course for? Plumbing Skills Training Course is suitable for anyone who want to gain extensive knowledge, potential experience and professional skills in the related field. This is a great opportunity for all student from any academic backgrounds to learn more on this subject.

This route is aimed as commercial vehicle tyre technicians and covers the following competencies: Truck tyre fitting Specialist and multi-piece wheels Agricultural tyre fitting Earth mover tyre fitting Industrial tyre fitting INTERESTED? PFTP are proud to have been awarded approval by the NTDA to offer this valuable licence to our customers. To find out more, please either telephone us on 024 76325880, use the live talk function to talk to one of our sales operatives or visit our contact page to leave a message. We look forward to hearing from you!

The IMI Roadside Safety programme is a blended learning course consisting of an online learning package followed by a face to face training and assessment session. The course is aimed at Truck and Light Vehicle Tyre Technicians involved with working on the roadside and whilst not essential, we advise that candidates should have completed a recognised course in Heavy or Light Vehicle Tyre Fitting and possess good communication skills. Successful candidates will receive the IMI Quality Approved Award in Roadside Safety as well as the NTDA REACT Licence to Work on the Roadside. Course Details:  Online training program to be worked through at the candidates own pace. Up to one day training and assessment session. Course Content Theoretical Identification of essential service vehicle equipment Personal Protective Equipment to be used at the roadside The main requirements of PAS43 and the REACT licence Pre-breakdown planning and dynamic risk assessment Attending a breakdown and making the area safe Clearing up and leaving the scene safely Practical Service vehicle inspection and equipment condition reporting Simulated attendance a motorway breakdown for the purposes of making the scene safe (please note, there is no wheel removal or tyre fitting activities included in this course or test. (Please view the High Performance Car Tyre or Truck Tyre Fitting courses for this) IMI assessment and test A multi-choice theory question paper Two practical assessments, observed by an IMI approved assessor covering a service vehicle and equipment inspection and attending a simulated motorway breakdown. It is required that each candidate supply their own fully equipped service vehicle for this assessment What’s included Fully qualified and experienced trainers Online training program Examinations, Certification and REACT licence fees To find out more, please use the live chat function, visit our contact page or call us on 024 76325880

Duration 5 Days 30 CPD hours This course is intended for Test Programme Managers, Test Managers, and anyone else wishing to take the ISTQB© Certified Tester Advanced Level Test Manager examination. Overview Whilst this course is focused on the syllabus, giving participants the maximum chance of passing the examination, it also contains many real world practical examples. On completion of this course, attendees will have an advanced understanding of test design techniques and will be fully prepared to take the ISTQB© Certified Tester Advanced Level Test Manager examination. ISTQB© is the standard for international qualifications in software testing at an advanced level. The course thoroughly prepares attendees for the ISTQB© Certified Tester Advanced Level Test Manager examination. Testing Process The fundamental test process Test levels and test types Test planning, monitoring and control Test analysis Test design Test implementation Test execution Evaluating exit criteria and reporting Test closure activities Test Management Test management in context Risk-based testing and other approaches for test prioritization and effort allocation Test documentation and other work products Project risk management Other test work products Test estimation Defining and using test metrics Business value of testing Distributed, outsourced, and insourced testing Managing the application of industry standards Reviews Management reviews and audits Managing reviews Metrics for reviews Managing formal reviews Defect Management The defect lifecycle and the software development lifecycle Cross-functional defect management Defect report information Assessing process capability with defect report information Improving the Testing Process Introduction Test improvement process Improving the testing process Improving the testing process with TMMI Improving the testing process with TPI Next Improving the testing process with CTP Improving the testing process with STEP Test Tools and Automation Tool selection Return on investment (ROI) Selection process Tool lifecycle Tool metrics People Skills ? Team Composition Individual skills Test team dynamics Fitting testing within an organization Motivation Communication

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

REFERENCE CODE 603/5285/1 COURSE LEVEL NVQ Level 2 THIS COURSE IS AVAILABLE IN Course Overview Who is this qualification for? This is a work-based learning qualification for those involved in laying floorcoverings in a Construction related working environment. Although many of the skills and knowledge across floorcovering occupations are generic, not all those employed to fit floorcoverings will be fitting the same product. The group of optional units will allow specialisms of timber based, textile and resilient floorcoverings. What is required from candidates? To achieve this pathway unit the survey work must be carried out in ways that will minimise the risk of damage to the work and surrounding area and using and maintaining equipment effectively. This qualification is made up of 9 mandatory units and a group of optional units. The minimum credit value of this qualification is 127 credits. Qualifications are now required to indicate the total qualification time (TQT), this is to show the typical time it will take someone to attain the required skills and knowledge to meet the qualification criteria, this qualification has a TQT of 1,270 hours. Qualifications are also required to indicate the number of hours of teaching someone would normally need to gain the skills and knowledge to achieve the qualification. These are referred to as Guided Learning Hours (GLH). The GLH for this qualification is 543 Mandatory units Level Credit Conforming to General Health, Safety and Welfare in the Workplace Conforming to Productive Working Practices in the Workplace Moving, Handling and Storing Resources in the Workplace Surface preparation to receive floorcoverings in the workplace Setting out for laying floorcoverings in the workplace Preparing and fitting underlays for floorcoverings in the workplace Develop customer relationships Assessing and preparing background surfaces for floor-covering in the workplace Assessing and evaluating conditions for floorcoverings in the workplace Optional Units (Minimum of 18 credits) Installing timber–based floorcoverings in the workplace Summary of the: GQA NVQ LEVEL 2 DIPLOMA IN FLOORCOVERING OCCUPATIONS (CONSTRUCTION) Installing textile floorcoverings in the workplace Installing resilient floorcoverings in the workplace Types of evidence: Evidence of knowledge is required. Evidence of knowledge through performance can contribute and if applicable should be demonstrated by completing projects and reports, by responding to questions or through Professional/Guided Discussions. Quantity of evidence: Evidence should show that you can meet the requirements of the units consistently over an appropriate period of time. Potential sources of evidence: Suggested sources of evidence are shown above, these can be supplemented by physical or documentary evidence, e.g.: Accident book/reporting system Notes and memos Safety record Telephone/e-mail records Training record Customer and colleague feedback Audio evidence Records of equipment and materials Witness testimonies Work records Photographic/ video evidence Please Note that photocopied or downloaded documents such as manufacturers or industry guidance, H&S policies, Risk Assessments etc, are not normally acceptable evidence for GQA qualifications unless accompanied by a record of a professional discussion or assessor statement confirming candidate knowledge of the subject. If you are in any doubt about the validity of evidence, please contact Oscar Onsite Academy

ChatGPT, along with other AI tools, aims not to replace the human touch in management, but to enhance it. By addressing repetitive, daily tasks, these tools free up managers to concentrate on core responsibilities like strategic decision-making, team development, and innovation. As we move further into the digital age, integrating tools such as ChatGPT isn't a luxury; it's the future of proactive leadership. In this guide, we'll delve into 10 practical ways through which AI can elevate your efficiency and refine the quality of your work. Gain familiarity with prominent AI tools in the market Efficiently compose and respond to emails Generate concise summaries of complex reports and data. Obtain quick insights, data, and research across varied topics Streamline the writing of articles, training notes, and posts Craft interview tests, form relevant questions, and design checklists for the hiring process 1 Streamlining emails An inbox can be a goldmine of information but also a significant time drain for managers. Here's how to optimise it: Drafting responses: Give the AI a brief, and watch it craft a well-structured response. Sorting and prioritising: By employing user-defined rules and keywords, ChatGPT can flag important emails, ensuring no vital communication slips through the cracks. 2 Efficient report writing Reports, especially routine ones, can be time-intensive. Here's a smarter approach: Automate content: Supply key data points to the AI, and let it weave them into an insightful report. Proofreading: Lean on ChatGPT for grammar checks and consistency, ensuring each report remains crisp and error-free. 3 Rapid research From competitor insights to market trends, research is a pivotal part of management. Data synthesis: Feed raw data to the AI and receive succinct summaries in return. Question-answering: Pose specific questions about a dataset to ChatGPT and extract swift insights without diving deep into the entire content. 4 Reinventing recruitment Hiring can be a lengthy process. Here's how to make it more efficient: Resume screening: Equip the AI to spot keywords and qualifications, ensuring that only the most fitting candidates are shortlisted. Preliminary interviews: Leverage ChatGPT for the initial rounds of interviews by framing critical questions and evaluating the responses. 5 Enhancing training Especially for extensive teams, training can be a monumental task. Here's how ChatGPT can assist: Customised content: Inform the AI of your training goals, and it will draft tailored content suitable for various roles. PowerPoint design: Create visually appealing slide presentations on any topic in minimal time.

This course provides both underpinning knowledge of stoma care and practical techniques to deliver person centred support for someone who requires stoma care.

A licensed retail tyre technician is likely to come from a number of backgrounds, e.g. apprentice, trainee or from another company where they have gained experience, but may not hold the relevent qualification or where the qualificatiion is old and out of date. For an individual to apply for the LRTT, they must be assessed as a competent practitioner and have the relevent knowledge in the following covering car, light van and 4×4: Tyre fitting Tyre repair Wheel balancing Four-wheel alignment Tyre pressure monitoring systems (TPMS) Manual handling and safe working practices.   INTERESTED? PFTP are proud to have been awarded approval by the NTDA to offer this valuable licence to our customers. To find out more, please either telephone us on 024 76325880, use the live talk function to talk to one of our sales operatives or visit our contact page to leave a message. We look forward to hearing from you!