2626 Evaluation courses

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Who is this course suitable for? Required to undertake asbestos fibre counting as part of their work Considering a career in asbestos analysis Responsible for managing asbestos analysts Prior Knowledge and Understanding Candidates for this course are expected to be aware of HSG 248 Asbestos: The Analysts' Guide (July 2021), and in particular Appendix 1, Fibres in air: sampling and evaluation of by phase contrast microscopy. Candidates will preferably have prior experience of analysing fibre count samples and may already be participating in a quality control scheme. In addition, candidates are expected to have had training to cover the core competencies outlined within the foundation material detailed within Table A9.1 of HSG248 Asbestos: The Analysts' Guide (July 2021). This may be achieved by In -house learning or through the P400 foundation module.

Effecting Business Process Improvement: Virtual In-House Training Business analysts facilitate the solution of business problems. The solutions are put into practice as changes to the way people perform in their organizations and the tools they use. The business analyst is a change agent who must understand the basic principles of quality management. This course covers the key role that business analysts play in organizational change management. What you will Learn You will learn how to: Define and document a business process Work with various business modeling techniques Perform an enterprise analysis in preparation for determining requirements Analyze business processes to discern problems Foundation Concepts Overview of business analysis and process improvement Defining the business process Introducing the proactive business analyst Focusing on business process improvement for business analysts Launching a Successful Business Process Improvement Project Overview of the launch phase Understanding and creating organizational strategy Selecting the target process Aligning the business process improvement project's goals and objectives with organizational strategy Defining the Current Process Overview of current process phase Documenting the business process Business modeling options: work-flow models Business modeling options: Unified Modeling Language (UML) model adaptations for business processes Analyzing the Current Process Process analysis overview Evaluation: establishing the control group Opportunity techniques: multi-discipline problem-solving Opportunity techniques: matrices Building and Sustaining a Recommended Process Overview of the recommended process and beyond Impact analysis Recommended process Transition to the business case Return to proactive state

About this training Mature fields differ from green field developments in that major infrastructure is in place, static reservoir data has accumulated from development drilling and a growing volume of production and processing performance data has become available. Decisions therefore relate to incremental projects, which may be small in scope and are often economically marginal. A firm understanding of the technical fundamentals associated with reservoir, wells and surface facilities is therefore required to make quality decisions in this environment, supported by realistic uncertainty ranges, and consistent application of incremental project economics and risk analysis. Various strategies may be considered to manage the mature asset, from harvest to divest, and the selected incremental activities should support a clear chosen strategy. Training Objectives Upon completion of this course, participants will be able to: Characterize the overall challenges associated with mature field developments Evaluate critical insights from subsurface data and apply this to modelling options and recovery methods Assess associated well data, typical late life issues and drilling and completion options for mature developments Manage the role of risk and uncertainty when making mature field development planning decisions Prepare a strategy and implementation plan Target Audience The course is intended for individuals who play a part in evaluating, screening and maturing oil and gas field development opportunities. The following personnel will benefit from the knowledge shared in this course: Petroleum engineers Geoscientist Facilities engineers Commercial staffs Reservoir engineer Production engineer Drilling engineer Project manager Asset manager Field engineer Exploration manager Course Level Basic or Foundation Trainer Your expert course leader, boasts nearly four decades of experience in the upstream oil & gas industry. He began his career in the back in 1982, spending 13 years with Shell International across several global locations. During his tenure, he served primarily as a reservoir engineer, contributing to exploration prospect evaluation, field development planning, corporate business planning, and drilling operations. Throughout his career, he has executed a diverse range of reservoir engineering projects for multiple UK and international firms, and has successfully led several PE study teams. Furthermore, he has continuously provided reservoir engineering and commercial training to oil company staff on a national and international scale. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Overview This comprehensive course on Data Science & Machine Learning with Python will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Data Science & Machine Learning with Python 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? 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? There is no experience or previous qualifications required for enrolment on this Data Science & Machine Learning with Python. It is available to all students, of all academic backgrounds. Requirements Our Data Science & Machine Learning with Python 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 Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 2 sections • 90 lectures • 10:24:00 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: 00:08:00 •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: 00:07:00 •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

This course aims to provide learners with a comprehensive understanding of employee rewards and reward systems, and their role in promoting workplace fulfillment. By the end of this course, learners will be able to understand the concept of employee rewards and the different types of rewards available, including healthcare-specific systems. Learn about the different components of pay and how they can be used to motivate and reward employees. Develop skills in identifying development needs for healthcare employees and designing effective employee reward and recognition programs. Gain an understanding of the factors that influence employee remuneration, and how job evaluation processes can be used to evaluate and manage pay scales. After the successful completion of the course, you will be able to learn about the following, The Concept of Employee Rewards and Reward System. Objectives and Types of Types of Reward Systems. Types of Healthcare Reward Systems. Four Pillars of Workplace Fulfilment. Concept of Job Evaluation. Methods and Process of Job Evaluation. Concept of Employee Remuneration. Factors Influencing Employee Remuneration. Factors Influencing Healthcare Employee Remuneration. Components of Identifying Development Needs of The Healthcare Employees. This course aims to provide learners with a comprehensive understanding of employee rewards and reward systems. By the end of this course, learners will be able to understand the different types of rewards and reward systems available, including those specific to healthcare organizations. Develop skills in job evaluation and identify the factors that influence employee remuneration. Learn about the different components of pay and how they can be used to motivate and reward employees. Gain insight into identifying the development needs of healthcare employees and designing effective employee reward and recognition programs to promote workplace fulfilment. This course aims to provide learners with a comprehensive understanding of employee rewards and reward systems, and their role in promoting workplace fulfillment. By the end of this course, learners will be able to understand the concept of employee rewards and the different types of rewards available, including healthcare-specific systems. Learn about the different components of pay and how they can be used to motivate and reward employees. Develop skills in identifying development needs for healthcare employees and designing effective employee reward and recognition programs. Gain an understanding of the factors that influence employee remuneration, and how job evaluation processes can be used to evaluate and manage pay scales. VIDEO - Course Structure and Assessment Guidelines Watch this video to gain further insight. Navigating the MSBM Study Portal Watch this video to gain further insight. Interacting with Lectures/Learning Components Watch this video to gain further insight. Reward Management in Health and Social Care - N Self-paced pre-recorded learning content on this topic. Reward Management in Health and Social Care Put your knowledge to the test with this quiz. Read each question carefully and choose the response that you feel is correct. All MSBM courses are accredited by the relevant partners and awarding bodies. Please refer to MSBM accreditation in about us for more details. There are no strict entry requirements for this course. Work experience will be added advantage to understanding the content of the course. The certificate is designed to enhance the learner's knowledge in the field. This certificate is for everyone eager to know more and get updated on current ideas in their respective field. We recommend this certificate for the following audience. Compensation and Benefits Manager Human Resources Manager Payroll Specialist Total Rewards Manager Employee Benefits Coordinator Talent Management Specialist Performance and Rewards Analyst Remuneration Specialist Employee Relations Consultant Healthcare Rewards Program Manager Average Completion Time 2 Weeks Accreditation 3 CPD Hours Level Advanced Start Time Anytime 100% Online Study online with ease. Unlimited Access 24/7 unlimited access with pre-recorded lectures. Low Fees Our fees are low and easy to pay online.

About this Training Energy insurance is a type of insurance designed to protect businesses that work in the energy industry. This type of insurance covers a wide range of risks that are unique to the energy industry, such as damage to oil rigs, power plants, pipelines, or other energy infrastructure, as well as accidents, explosions, fires, and environmental damage. Energy insurance can also provide coverage for business interruption caused by unforeseen events that can disrupt energy production or supply, such as natural disasters, equipment breakdown, and cyber-attacks. It may also include coverage for liability and loss of income resulting from lawsuits and legal claims. Training Objectives Upon completion of this course, participants will be able to: Understand the risk sharing between oil companies and contractors Know how this is dealt within the insurance products available Understand insurer's perception of risk Create awareness of how market insurance products meet industry needs Be familiar with insurer's pricing methodologies Better understanding of the broker interface Understand technical evaluation of the coverage wordings Putting technical knowledge into practice with claims workshop Target Audience The course is intended for individuals who work in the energy industry, particularly those who are involved in managing risk or making decisions related to insurance coverage. The following personnel will benefit from the knowledge shared in this course: Insurers Brokers Adjusters Lawyers Risk Managers Treasury Contracts Legals Contract Adjustor Project Managers Course Level Basic or Foundation Trainer Your expert course leader has worked in the insurance sector for 59 years. He has worked as a broker for reputable firms, such as Marsh, where he served as the managing director of Energy Construction. He has also participated in peer review for different Lloyds Syndicates. He also served as a broker for Sedgwick, AAA, and Miller in the offshore energy sector. He has helped businesses including Shell, BP, Chevron, ConocoPhillips, Petrofina, Woodside, ENI, and Brunei Shell for their policy reviews during his career. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Expert consultant to conduct Fire Risk Assessment of site/ workplace/ relevant building

About this Training Course The 3-day hands-on petroleum economics training course provides a comprehensive overview of the practices of exploration and development petroleum economics and its application in valuing oil and gas assets to aid corporate decisions. Participants will gain a thorough understanding of the principles of economic analysis as well as practical instruction in analytical techniques used in the industry. The participants will learn how to construct economic models, to include basic fiscal terms, production and cost profiles and project timing. The resulting model will provide insights of how the various inputs affect value. Example exercises will be used throughout the course. Training Objectives Upon completion of this course, participants will be able to: Understand and construct petroleum industry cash flow projections Calculate, understand and know how to apply economic indicators Learn and apply risk analysis to exploration and production investments Evaluate and model fiscal/PSC terms of countries worldwide Target Audience The following oil & gas company personnel will benefit from the knowledge shared in this course: Geologists Explorationists Reservoir Engineers Project Accountants Contract Negotiators Financial Analysts New Venture Planners Economists Course Level Basic or Foundation Intermediate Trainer Your expert trainer has over 40 years' experience as a petroleum economist in the upstream oil and gas industry. He has presented over 230 oil and gas industry short courses worldwide on petroleum economics, risk, production sharing contracts (PSC) and fiscal analysis. In over 120 international oil industry consulting assignments, he has advised companies and governments in the Asia Pacific region on petroleum PSC and fiscal terms. He has prepared many independent valuations of petroleum properties and companies for acquisition and sale, as well as economics research reports on the oil and gas industry and including commercial support for oil field operations and investments worldwide. He has been involved in projects on petroleum royalties, design of petroleum fiscal terms, divestment of petroleum assets, and economic evaluation of assets and discoveries since the early 1990s to date. He has been working on training, consultancy, research and also advisory works in many countries including USA, UK, Denmark, Switzerland, Australia, New Zealand, Indonesia, India, Iran, Malaysia, Thailand, Vietnam, Brunei, Egypt, Libya, and South Africa. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.