619 Grid courses

Students who complete the PV351 workshop will be able to: Determine use and analyze results from various test tools used during commissioning, performance evaluation, operations and maintenance, and troubleshooting. Define the theory, procedures, and processes behind insulation resistance testing, IV curve tracing, infrared cameras and thermal imaging, performance evaluation, and troubleshooting Demonstrate proper set-up, use, and function of PV test tools including: IV curve tracers, insulation resistance testers, and thermal cameras Evaluate the performance of working systems using correct and complete field procedures Troubleshoot and locate common PV array and system faults using appropriate methodologies and testing tools

Palm Tree art class drawing and painting from start to finish on canvas board in acrylic paint.

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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

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Curriculum of Bundle Course 01: Introduction to Data Analysis Module 01: Introduction Module 02: Agenda and Principles of Process Management Module 03: The Voice of the Process Module 04: Working as One Team for Improvement Module 05: Exercise: The Voice of the Customer Module 06: Tools for Data Analysis Module 07: The Pareto Chart Module 08: The Histogram Module 09: The Run Chart Module 10: Exercise: Presenting Performance Data Module 11: Understanding Variation Module 12: The Control Chart Module 13: Control Chart Example Module 14: Control Chart Special Cases Module 15: Interpreting the Control Chart Module 16: Control Chart Exercise Module 17: Strategies to Deal with Variation Module 18: Using Data to Drive Improvement Module 19: A Structure for Performance Measurement Module 20: Data Analysis Exercise Module 21: Course Project Module 22: Test your Understanding Course 02: 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 03: Statistical Analysis Module 01: The Realm of Statistics Module 02: Basic Statistical Terms Module 03: The Center of the Data Module 04: Data Variability Module 05: Binomial and Normal Distributions Module 06: Introduction to Probability Module 07: Estimates and Intervals Module 08: Hypothesis Testing Module 09: Regression Analysis Module 10: Algorithms, Analytics and Predictions Module 11: Learning From Experience: The Bayesian Way Module 12: Doing Statistics: The Wrong Way Module 13: How We Can Do Statistics Better Course 04: SQL NoSQL Big Data and Hadoop Module 01: Introduction Module 02: Relational Database Systems Module 03: Database Classification Module 04: Key-Value Store Module 05: Document-Oriented Databases Module 06: Search Engines Module 07: Wide Column Store Module 08: Time Series Databases Module 09: Graph Databases Module 10: Hadoop Platform Module 11: Big Data SQL Engines Module 12: Distributed Commit Log Module 13: Summary Course 05: Complete Microsoft Power BI 2021 Module 01: Introduction Module 02: Preparing our Project Module 03: Data Transformation - The Query Editor Module 04: Data Transformation - Advanced Module 05: Creating a Data Model Module 06: Data Visualization Module 07: Power BI & Python Module 08: Storytelling with Data Module 09: DAX - The Essentials Module 10: DAX - The CALCULATE function Module 11: Power BI Service - Power BI Cloud Module 12: Row-Level Security Module 13: More data sources Module 14: Next steps to improve & stay up to date Course 06: Data Analysis in Excel Level 3 Course Modifying a Worksheet Working with Lists Analyzing Data Visualizing Data with Charts Using PivotTables and PivotCharts Working with Multiple Worksheets and Workbooks Using Lookup Functions and Formula Auditing Automating Workbook Functionality Creating Sparklines and Mapping Data Forecasting Data Course 07: Data Analytics with Tableau Module 01: Introduction to the Course Module 02: Project 1: Discount Mart (Sales and Profit Analytics) Module 03: Project 2: Green Destinations (HR Analytics) Module 04: Project 3: Superstore (Sales Agent Tracker) Module 05: Northwind Trade (Shipping Analytics) Module 06: Project 5: Tesla (Stock Price Analytics) Module 07: Bonus: Introduction to Database Concepts Module 08: Tableau Stories Course 08: Basic Google Data Studio Module 01: Introduction to GDS Module 02: Data Visualization Module 03: Geo-visualization Module 04: A Socio-Economic Case Study Course 09: Business Analytics Module 01: What is business analysis? Module 02: Strategy analysis Module 03: Collaboration Module 04: Requirements analysis and Design definition Module 05: Requirements lifecycle management Module 06: Solution quality Module 07: Stakeholder management Module 08: BA Governance Module 09: Legal notes and Copyright information Course 10: Complete Introduction to Business Data Analysis Level 3 Module 1: Statistics Fundamentals Module 2: Data Analysis Module 3: Probability Module 4: Random Variables and Discrete Distributions Module 5: Continuous Distributions Module 6: Sampling Distributions Module 7: Confidence Interval Module 8: Hypothesis Testing with One Sample Module 9: Hypothesis Testing with Two Samples Module 10: The Chi-Square Distribution Module 11: F Distribution and One-Way ANOVA Module 12: Correlation analysis Module 13: Simple Linear Regression Analysis Course 11: Business Intelligence and Data Mining Masterclass Module 01: What is Business Intelligence? Module 02: Starting Case in understanding BI needs in diff phase of business Module 03: Decision Making Process and Need of IT systems Module 04: Problem Structure and Decision Support System Module 05: Introduction to BI Applications Module 06: Dashboard presentation systems Module 07: Different Types of Charts used in 131 Dashboards Module 08: Good Dashboard and BSC Module 09: Examples of Bad Dashboards 1 Module 10: Examples of Bad Dashboards 2 And much more... Course 12: Research Methods in Business Section 01: Applied Project & Research Methods in Business Section 02: Writing a Purpose / Quantitative and Qualitative Research Approaches Section 03: Mixed Method Research Approaches, Ethical Considerations & Writing Effectively Written Methodology Part 3 !@@ Section 04: Writing Data Collection Tools, Qualitative & Quantitative Data Analysis Section 05: Comparing Findings to Literature and Writing the Final Paper Course 13: Computer Science: Graph Theory Algorithms Module 00: Promo Module 01: Introduction Module 02: Common Problem Module 03: Depth First Search Module 04: Breadth First Search Module 05: Breadth First Search Shortest Path on a Grid And much more... Course 14: Data Protection and Data Security Level 2 GDPR Basics GDPR Explained Lawful Basis for Preparation Rights and Breaches Responsibilities and Obligations CPD 165 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone from any background can enrol in this Data Analysis and Data Science bundle. Requirements Our Data Analysis and Data Science course is fully compatible with PCs, Macs, laptops, tablets and Smartphone devices. Career path Having this Data Analysis and Data Science expertise will increase the value of your CV and open you up to multiple job sectors. Certificates Certificate of completion Digital certificate - Included You will get the PDF Certificate for the title course (Introduction to Data Analysis) absolutely Free! Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Introduction to Data Analysis) absolutely Free! Other Hard Copy certificates are available for £10 each. Please Note: The delivery charge inside the UK is £3.99, and the international students must pay a £9.99 shipping cost.

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This Upgrade Your Excel Skills course covers everything you must know to stand against the tough competition in the Upgrade Your Excel Skills field.  The future is truly yours to seize with this Upgrade Your Excel Skills. Enrol today and complete the course to achieve a Upgrade Your Excel Skills certificate that can change your professional career forever. Additional Perks of Buying a Course From Institute of Mental Health Study online - whenever and wherever you want. One-to-one support from a dedicated tutor throughout your course. Certificate immediately upon course completion 100% Money back guarantee Exclusive discounts on your next course purchase from Institute of Mental Health Enrolling in the Upgrade Your Excel Skills course can assist you in getting into your desired career quicker than you ever imagined. So without further ado, start now. Process of Evaluation After studying the Upgrade Your Excel Skills course, your skills and knowledge will be tested with a MCQ exam or assignment. You must get a score of 60% to pass the test and get your certificate.  Certificate of Achievement Upon successfully completing the Upgrade Your Excel Skills course, you will get your CPD accredited digital certificate immediately. And you can also claim the hardcopy certificate completely free of charge. All you have to do is pay a shipping charge of just £3.99. Who Is This Course for? 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Any internet-connected device, such as a computer, tablet, or smartphone, can access this online Upgrade Your Excel Skills course. Moreover, this course allows you to learn at your own pace while developing transferable and marketable skills. Course Curriculum Unit 01: Introduction Welcome 00:02:00 Unit 02: Excel Productivity Tips -Part 1 Camera Tool 00:05:00 Add Line Break in Formula Bar 00:03:00 Advanced Copy-Paste using Office Clipboard 00:03:00 Navigation between Worksheets using Shortcut Keys 00:01:00 Flash Fill 00:02:00 Add Multiple Rows and Columns Quickly 00:02:00 Delete Blank Rows 00:03:00 Multi Level Row and Column Sorting 00:04:00 Add Sparklines 00:03:00 Paste Special - Transpose 00:03:00 Unit 03: Excel Productivity Tips - Part 2 Snap to Grid 00:03:00 Create Custom Auto fill List 00:06:00 Absolute Cell Reference 00:03:00 Import Data from Web page 00:04:00 Move Cell Direction 00:03:00 Delete Data using Mouse 00:02:00 Status Bar Quick Calculations 00:03:00 Link Text Boxes to Cell 00:04:00 Phone Number and ZIP Code Format 00:04:00 Speaking Cell 00:05:00 Unit 04: Excel Productivity Tips - Part 3 Invisible Text 00:03:00 Worksheet Grouping 00:04:00 Advanced Transpose 00:04:00 XLStart Folder 00:03:00 Use Slicers 00:05:00 Convert Text to Numbers 00:03:00 Hiding Workbook Elements 00:02:00 Useful Shortcut Keys 00:03:00 Sort data from Left to Right 00:03:00 Advanced Filter (Complex) 00:11:00 Unit 05: Excel Productivity Tips - Part 4 Email as PDF 00:02:00 Synchronous Scrolling 00:03:00 Quick Analysis of data within Excel 00:02:00 Fill blank cells with Value 00:03:00 Hidden Chart Source Data 00:03:00 Two more Shortcuts 00:03:00 Add Blank Rows 00:03:00 Custom views in Excel 00:06:00 EMBED Feature 00:04:00 Adding Country code in Mobile Numbers 00:04:00 Unit 05: Excel Productivity Tips - Part 5 Plot an Average Line to a Chart 00:04:00 3D Referencing 00:04:00 Extract Unique Values 00:03:00 Excel Array Formula 00:04:00 Forecast Sheet 00:04:00 Add Spin Controls in Excel 00:05:00 Move Data using Mouse 00:01:00 Add new entry in Auto Correct to use across Office Applications 00:05:00 Find Differences between Two Lists 00:02:00 Find formulas Quickly 00:02:00 Unit: 06 Wrap Up Thank You 00:01:00

Explore quick ways of creating simple images on this lino printing taster workshop with local artist Suzanne Kemplay at The Arienas Collective in Edinburgh City Centre

Learn to build highly sophisticated deep learning and Computer Vision applications with PyTorch

Overview This comprehensive course on Understand Piping & Instrumentation Diagrams P&IDs will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Understand Piping & Instrumentation Diagrams P&IDs comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? At the end of the course there will be an online written test, which you can take either during or after the course. After successfully completing the test you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this Understand Piping & Instrumentation Diagrams P&IDs. It is available to all students, of all academic backgrounds. Requirements Our Understand Piping & Instrumentation Diagrams P&IDs 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 qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 14 sections • 119 lectures • 08:26:00 total length •Introduction: 00:04:00 •What's a P&ID ?: 00:03:00 •Why is a P&ID so important ?: 00:02:00 •Who uses P&ID's ?: 00:06:00 •How do P&ID's look like ?: 00:08:00 •Introduction- PID READING: 00:02:00 •Anatomy of a P&ID: 00:01:00 •The title block: 00:03:00 •The drawing scale: 00:03:00 •The grid system: 00:02:00 •The revision block: 00:03:00 •Changes: 00:02:00 •Notes and legends: 00:03:00 •Valve symbols: 00:14:00 •Valve actuator symbols: 00:09:00 •Control valve designations: 00:02:00 •Standards and conventions for valve status: 00:07:00 •Process equipment symbols: 00:12:00 •Piping symbols: 00:03:00 •Pipe fitting symbols: 00:03:00 •Isolating, venting & draining symbols for ease of maintenance: 00:05:00 •Instrumentation: 00:03:00 •Sensing devices and detectors: 00:04:00 •Location symbols: 00:04:00 •Modifiers and transmitters: 00:05:00 •Indicators and recorders: 00:03:00 •Controllers: 00:03:00 •Example #1 : Identifying process equipment and flow paths: 00:05:00 •Example #2 : Identifying valve position and failure mode: 00:03:00 •Example #3 : Identifying the symbols: 00:02:00 •Piping designation code: 00:06:00 •Equipment designation code: 00:03:00 •Instrument designation code: 00:02:00 •Miscellaneous designation codes: 00:02:00 •The process: 00:01:00 •Process control: 00:06:00 •The control loop: 00:02:00 •Process control terms: 00:10:00 •Control loops : Feedback control: 00:02:00 •Pressure control loops: 00:01:00 •Flow control loops: 00:01:00 •Level control loops: 00:01:00 •Temperature control loops: 00:01:00 •Multi-variable loops: 00:02:00 •Feedforward control: 00:02:00 •Feedforward + Feedback: 00:01:00 •Cascade control: 00:08:00 •Split range control: 00:03:00 •Operations on control signals: 00:02:00 •Ratio control: 00:02:00 •Batch control: 00:01:00 •Selective control: 00:01:00 •Do we need to control at all ?: 00:01:00 •Principles of equipment-wise control: 00:10:00 •Pipe control system: 00:02:00 •Control of a single pipe: 00:02:00 •Control of pressure in a pipe: 00:03:00 •Control of flow in a pipe: 00:04:00 •Flow merging: 00:08:00 •Flow splitting: 00:05:00 •Centrifugal pump control: 00:04:00 •Control valve vs Variable Frequency Drive (VFD) for centrifugal pumps: 00:03:00 •Minimum flow control for centrifugal pumps: 00:09:00 •Positive displacement pump control: 00:02:00 •Control by a recirculation pipe for PD pumps: 00:03:00 •Variable Speed Drive (VSD) control for PD pumps: 00:01:00 •Control by stroke adjustment for PD pumps: 00:01:00 •Compressor control system: 00:02:00 •Compressor capacity control: 00:12:00 •Compressor anti-surge control: 00:03:00 •Heat transfer equipment control: 00:02:00 •Heat exchanger direct control system: 00:04:00 •Heat exchanger bypass control system: 00:04:00 •Reactor temperature control: 00:06:00 •Air cooler control: 00:02:00 •Heat exchanger for heat recovery: 00:01:00 •Heat exchanger back pressure control: 00:02:00 •Basic fired heater control: 00:08:00 •Complex fired heater control: 00:05:00 •Container and vessel control: 00:07:00 •Container blanket gas control: 00:02:00 •Safety strategies: 00:01:00 •Concept of Safety Instrumented Systems (SIS): 00:01:00 •SIS actions and types: 00:14:00 •SIS extent: 00:02:00 •SIS requirement: 00:03:00 •Anatomy of a SIS: 00:02:00 •SIS element symbols: 00:01:00 •SIS primary elements : Sensors: 00:03:00 •SIS final elements: 00:04:00 •Switching valve actuator arrangements: 00:02:00 •Valve position validation: 00:02:00 •Merging a switching valve and a control valve: 00:03:00 •SIS logics: 00:01:00 •Showing safety instrumented functions on P&ID's: 00:07:00 •Discrete control: 00:05:00 •Alarm system: 00:02:00 •Anatomy of alarm systems: 00:02:00 •Alarm requirements: 00:06:00 •Alarm system symbology in P&ID's: 00:06:00 •Concept of common alarms: 00:01:00 •Fire and Gas Detection Systems (FGS): 00:03:00 •Electric motor control: 00:07:00 •P&ID representation of commands and responses: 00:05:00 •P&ID representation of inspection and repair: 00:05:00 •P&ID example of electro-motor control: 00:04:00 •P&ID example #1 : Legend and specifications: 00:05:00 •P&ID example #2 : Hydrogen delivery station: 00:16:00 •P&ID example #3 : Acid system: 00:13:00 •P&ID example #4 : Centrifugal pump: 00:09:00 •P&ID example #5 : Utility station: 00:04:00 •P&ID example #6 : Waste water filter: 00:08:00 •P&ID example #7 : Steam separator: 00:15:00 •P&ID example #8 : Flare knock-out drum: 00:14:00 •P&ID example #9 : Centrifugal compressor: 00:05:00 •P&ID example #10 : Hydrogen production from shale gas: 00:11:00 •P&ID example #11 : Fired heater: 00:07:00 •Resources - Understand Piping & Instrumentation Diagrams P&IDs: 00:00:00 •Assignment - Understand Piping & Instrumentation Diagrams P&IDs: 00:00:00

A collection of my most popular courses and webinars to help you figure out how best to manage your fatigue challenge and move forwards towards a better level of wellness. Help for all stages of your journey - from the initial basics, to returning to work and figuring out when it's time to incorporate more movement and exercise. It also contains a webinar to share with your loved ones, so that they can better understand what you're going through. Contents £10 Communicating with others when you've got fatigue Course 4 Lessons Fatigue journeys vary from person to person - no two experiences are the same. But there's one challenging topic that seems to feature in most people's journeys at one time or another... communication! In this webinar recording I share tips and advice on how to tackle five of the most common conversation challenges when you're on a fatigue recovery journey. View product £75 Fatigue Reset: Crack pacing once and for all Course 32 Lessons View product £10 How to achieve a successful return to work Course 1 Lesson If you’ve had to take time off from your job for months or even years, contemplating a return to work can be difficult. How do you know if you’re ready? Are you well enough to return full-time or do you need to negotiate a phased approach? And what can you do to make sure the return itself goes as successfully as possible? This webinar starts to answer those questions - and many more! View product £10 How to cope better with fatigue Course 2 Lessons A recording of a live webinar held in November 2023. I share the most important areas that you should be focussing on, so that you minimise the time spent wondering what on earth you can do to improve your fatigue challenge and current situation - and get onto the right path to recovery. View product £10 How to start exercising when you have fatigue Course 3 Lessons We hear how helpful it is to exercise for health and wellbeing. But when you have fatigue it can be difficult to know if this is going to help or hinder. When *is* it the right time to start to feel confident about moving more and starting to exercise?In this webinar recording I share tips and advice on how to tackle five of the most common conversation challenges when you're on a fatigue recovery journey. View product £10 When a Loved One has fatigue...how to help them, and yourself. Course 5 Lessons A fatigue-related condition doesn’t only impact the life of the person who's ill, it often changes things for you, their loved ones. Adjusting to this can be tough for you, and it can be difficult to know how to best help them.This webinar will increase your understanding of what life feels like for your loved one, and help you know how to support them on their recovery journey, while looking after yourself too.View product

Course Overview canva is one of the most popular and effective tools for graphic designers for fast designing. If you want to use your graphic designing for marketing or put your idea online then the large library of canva can help you choose any template and edit it as you wish. Learn the effective techniques of canva to create beautiful and eye-catching graphics from this Graphic Elements of Design: Color Theory and Application course and promote your ideas effectively. This Graphic Elements of Design: Color Theory and Application course will help you to apply different colour theories and fundamentals in your project. You will learn how to use graphic theories and colours for data visualisation. You will be able to use professional colour palettes, proximity, typography and many other graphic components to beautify your design. This course will help you learn graphic designing techniques from scratch. It is a very effective course for aspiring graphic designers and marketers who wants to learn Canva techniques. Learning Outcomes Familiarize with the fundamentals and theories of graphic designing Understand colour interaction and colour harmony in designing Be able to create different types of colour combinations and apply them to your design Learn how to create a professional colour palette Gain the skill to work with colour, images and typography Learn how colour basics work for branding Learn the cultural connection of colours Who is this course for? This course is perfect for anyone who wants to learn graphic designing or strengthen their basics of designing. You will learn the basic theories and elements of graphic designing and their application from this curse. Entry Requirement This course is available to all learners, of all academic backgrounds. Learners should be aged 16 or over to undertake the qualification. Good understanding of English language, numeracy and ICT are required to attend this course. Certification After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24. PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days. Why choose us? Affordable, engaging & high-quality e-learning study materials; Tutorial videos/materials from the industry-leading experts; Study in a user-friendly, advanced online learning platform; Efficient exam systems for the assessment and instant result; The UK & internationally recognized accredited qualification; Access to course content on mobile, tablet or desktop from anywhere anytime; The benefit of career advancement opportunities; 24/7 student support via email. Career Path Graphic Elements of Design: Color Theory and Application is a useful qualification to possess and would be beneficial for any related profession or industry such as: Graphic Designers Illustrators Digital Artists Artists Social Media Marketers Logo Designers Banner Designers Introduction Module 01: Colour Theory- Basic Terms 00:03:00 Module 02: Use Values to Establish Hierarchy in Photoshop CC 00:04:00 Module 03: Class Exercise: Value 00:01:00 Module 04: Colour Fundamentals: Colour Wheel 00:02:00 Module 05: Colour Combinations 00:05:00 How to Work with Colours in Adobe Illustrator CC 2020 Module 06: Colour Modes in Adobe Illustrator CC 00:01:00 Module 07: Working with Existing Colours 00:02:00 Module 08: Customising Colours 00:01:00 Module 09: Save Colour Swatches 00:01:00 Module 10: Global Colours 00:01:00 Module 11: Duplicate Global Colours 00:01:00 Module 12: Adobe Colour Themes 00:04:00 Module 13: Colour Guides 00:01:00 Module 14: Recolour Artwork 00:03:00 Module 15: Colouring the Line Art 00:03:00 Module 16: Save as Template 00:03:00 Colour Systems Module 17: CMYK Colour System 00:02:00 Module 18: RGB Colour System 00:01:00 Module 19: Pantone Colour System 00:01:00 Different Techniques to Develop Successful Colour Palettes Module 20: Colour Technique- The Subordinate, Dominant and Accent Technique 00:01:00 Module 21: Colour Technique- Meaning Based Technique for Harmonising Colours 00:01:00 Module 22: Colour Technique- Meaning Based Colour Technique Exercise 00:03:00 Module 23: Colour Technique- One Colour Palette Technique 00:01:00 Module 24: Colour Technique- Two Colour Palette Technique 00:01:00 Module 25: Colour Technique- Progressive Colour Technique 00:01:00 Module 26: Colour Technique-Repetition or Reoccurrence or Gradient Technique 00:01:00 Module 27: Colour Progression and Repetition Exercise 00:08:00 Module 28: Colour Technique- Black and White Technique 00:02:00 Module 29: Colour as Provocateur 00:01:00 Module 30: Excessive Colour Solutions 00:02:00 Module 31: Colour Overlap Technique 00:01:00 Colour Interaction and Proximity Module 32: Introduction 00:01:00 Module 33: Colour Interaction 00:01:00 Module 34: Different Colour Types 00:04:00 Module 35: Class Exercise: Colour Interaction 00:03:00 Module 36: How Light Affect Your Colours 00:01:00 Module 37: Interaction and Proximity 00:01:00 Module 38: Colour Proximity Exercise 00:02:00 Module 39: How to Make that Geometric Grid Design to Practice Your Colour 00:08:00 Colour and Imagery Module 40:Colour in Images, Illustrations and Type 00:01:00 Module 41:Colour and Imagery 00:01:00 Module 42:Best Royalty-free Images Websites 00:02:00 Module 43: Apply Colour to Your Images in Photoshop CC 00:06:00 Module 44: Images as Element of the Composition 00:04:00 Module 45: Proofing Colours 00:05:00 Module 46: Save for Printing 00:02:00 Module 47: Class Project- Colour and Texture 00:03:00 Colours and Illustration Module 48: Colour and Illustration 00:02:00 Module 49: Best Resources to Get Customisable Illustration 00:02:00 Colour and Type Module 50: How to Use Colours and Type 00:01:00 Module 51: Colour and Hierarchy 00:01:00 Module 52: Display text 00:02:00 Module 53: Specific Fonts 00:01:00 Module 54: Body Text and Colour 00:02:00 Module 55: Apply Effects to Display Text in Adobe Illustrator 00:04:00 Module 56: Best Fonts Resources Websites 00:02:00 Module 57: Class Project- Magazine Mock-up 00:07:00 Colours in Data Visualisation Module 58: Introduction to Colours in Data Visualisation 00:01:00 Module 59: Colours in Data Visualisation 00:03:00 Module 60: Different Ways to Import Charts 00:04:00 Module 61: The Process of Decluttering 00:09:00 Colours in Brand Identity Basics Module 62: Introduction to Colours in Branding 00:01:00 Module 63: Colours in Brand Identity- Part 1 00:10:00 Module 64: Colours in Brand Identity- Part 2 00:06:00 Module 65: Class Project- Logo 00:01:00 Certificate and Transcript Order Your Certificates and Transcripts 00:00:00