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Creating Your Organization's Business Agility Strategy Optimally, your organization's business strategy and business agility strategy are completely coupled together, one seamlessly supporting the other. Practically, what many organizations experience today is a tug-of-war between their business strategy and this thing called 'business agility.' Or, a lack of business agility strategy altogether, leaving the business strategy more susceptible, and even fragile, when unforeseen changes inevitably occur. We need a way to think about business agility, coupled with business strategy, so that we can live into the reality of harnessing change for good. This session will expose you to a recently published body of work, Domains of Business Agility, which serves as a model for creating business agility strategy. Think of it as a skeleton, or a thinking tool. Used this way, the model allows leaders to answer the question, 'How much business agility do we need in various parts of our organization as a seamless support to our overall business strategy?' In this session, Lyssa Adkins, author of Coaching Agile Teams and Agile/Leadership Coach, leads you through the key steps for creating such a business agility/business strategy. This and other IIL Learning in Minutes presentations qualify for PDUs. Some titles, such as Agile-related topics may qualify for other continuing education credits such as SEUs, or CEUs.

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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

We have completed a training course to teach you everything you need to know about Microneedling. Microneedling, also known as skin needling or collagen induction therapy, leads the way in skin rejuvenation. Safely delivered, this treatment carries no risk or downtime to the client. It is a procedure whereby a device, such as a derma roller or an automated pen device, is passed across the skin's surface, creating superficial punctures that stimulate a healing response. We work with Dr Pen and Beautier needling pen and the Clinicare range as part of this course and cover appropriate products from this range. Skills and knowledge from this course will be transferable to other brands. Our Microneedling Training Course will provide you with everything you need to know about the history and the use of microneedling, as well as all the technical aspects of it: safety, indications, technique and post-treatment tips for a successful outcome. This course will primarily focus on the face, although we touch on the scalp & body benefits of this treatment. Learn how to safely, confidently and expertly deliver this treatment with our fully accredited microneedling training course Course prerequisites Minimum 18 years of age Good command of English Be able to learn independently A strong desire to build a career in aesthetics Previous skin and facial training are desirable; we suggest that learners new to the industry enrol on our facial and skincare course before enrolling on our ClinicCare skin peel course. Course structure The online study, virtual lecture and in-person practical training All courses are intimate with four learners in class 2-1 ratio. One day of on-site training Online learning includes: Anatomy and physiology of the skin and tissues Infection control Sharps and hazardous waste training History of skin needling Treatable skin conditions Contraindications Consultation Aftercare Introduction to automated Pen Device and Dermaroller Pre-study microneedling (health and safety) Onsite training day includes: Face-to-face practical training with 1 model per treatment area Clinical set up Live treatment demonstrations Frequently Asked Questions Where is the Cosmetic College The Cosmetic College is located at: 3 Locks Court, 429 Crofton Road, Orpington, BR6 8NL How can I book? We have a few options for you to book. You can book by selecting an available training date above here on our website, by contacting us through email at hello@cosmetic.college or by contacting us on 0333 015 5117. Is a deposit required to book? All enrolments are charged an administration fee which is non-refundable. When you enrol you can elect to pay a deposit of 10% plus the administration fee or pay the total training course in full. We have full details of the terms and conditions of training course enrolments here What is the course duration? 1 day + pre-study via our online learning platform.

Are you interested in sharing knowledge with others and gaining insightful conclusions from data? This online Power BI course is your comprehensive and in-depth introduction to this powerful software if you've ever been curious about the fascinating, artistic field of data analysis and visualisation. Microsoft Power BI has the potential to be your most effective tool. It comes with all of the features found in MS Excel, as well as many more. Power BI has a wide range of features and functionalities. By obtaining this Power BI training, you'll learn to make the most of all of Microsoft Power BI's features and produce insightful tables, graphs, and reports. Along with this Power BI course, you also receive a number of additional practical and supplemental courses in a package that will assist you in becoming an expert in the field of business and IT. With the supplementary business management course, you will acquire the technical abilities and business knowledge needed to put your skills to use in the industry. Working with various platform data, algorithms, and data structures all are part of the job description of a data analyst. As the course includes Data Structure and Graph theory algorithm courses, this package will aid in your skill improvement as a data analyst. These courses in this bundle will advance your Power BI proficiency and allow you to perform more reasonable experimentation with this Power BI tool. Along with this Power BI course, you will get 10 premium courses, an original hardcopy, 11 PDF Certificates (Main Course + Additional Courses) Student ID card as gifts. This Power BI Bundle Consists of the following Premium courses: Course 01: Complete Microsoft Power BI 2021 Course 02: Data Structures Complete Course Course 03: Computer Science: Graph Theory Algorithms Course 04: Introduction to Data Analysis Course 05: Business Analysis Level 3 Course 06: Strategic Business Management Course 07: Enterprise Risk: Identification and Mitigation Level 2 Course 08: GDPR Data Protection Level 5 Course 09: Functional Skills IT Course 10: Decision Making and Critical Thinking Course 11: Time Management With the help of this excellent package, you can develop a professional career in the IT sector while also increasing your own personal growth. To summarise your learning objectives of this bundle: you will be able to, Identify the Power BI components and workflows you need to know Create a data model in Power BI Translate data into graphics using Power BI features Understand different types of data structures and algorithms Perform data analysis and measure the performance of your model Acquire business knowledge about the IT industry Gain time management skills for working as a professional in the corporate sector Your new data visualisation abilities can be used in almost any field of study or employment, including data science, finance, and even graphic design. This can lead to a wide range of professional prospects. So enrol in this course right away to jumpstart your career. Benefits you'll get choosing Apex Learning: Pay once and get lifetime access to 11 CPD courses Free e-Learning App for engaging reading materials & helpful assistance Certificates, student ID for the course included in a one-time fee Free up your time - don't waste time and money travelling for classes Accessible, informative modules of course designed by expert instructors Learn about course at your ease - anytime, from anywhere Study the course from your computer, tablet or mobile device CPD accredited course - improve the chance of gaining professional skills Curriculum: Course 01: Complete Microsoft Power BI 2021 Introduction Preparing our Project Data Transformation - The Query Editor Data Transformation - Advanced Creating a Data Model Data Visualization Power BI & Python Storytelling with Data DAX - The Essentials DAX - The CALCULATE function Power BI Service - Power BI Cloud Row-Level Security More data sources Next steps to improve & stay up to date How will I get my Certificate? After successfully completing the Power BI course you will be able to order your CPD Accredited Certificates (PDF + Hard Copy) as proof of your achievement. PDF Certificate: Free (Previously it was £6*11 = £66) Hard Copy Certificate: Free (For The Title Course: Previously it was £10) PS The delivery charge inside the UK is £3.99, and the international students have to pay £9.99. CPD 115 CPD hours / points Accredited by CPD Quality Standards Who is this course for? There are no strict requirements, and any experienced computer user is expected to be able to finish the project. This bundle will also be beneficial for, Students from all academic disciplines Students interested in powerful data analysis techniques Anyone in business who wants to transform data into information Users of Excel who want to advance their reporting and analysis Requirements This Power BI course has been designed to be fully compatible with tablets and smartphones. Career path These different credentials will add value to your resume and give you access to a wide range of industries, including, but not limited to, Data analysis IT industry Business and management Freelance worker Entrepreneur Certificates Certificate of completion Digital certificate - Included Certificate of completion Hard copy certificate - Included You will get the Hard Copy certificate for the title course (Complete Microsoft Power BI) 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.

Peer coaching is a cost-effective way to provide quality employee development to high-potential and emerging leaders. Learn about the behaviours that will ensure you are a great peer coach. You will understand why questions play an important role for effective coaching and how to use the correct questioning sequence for an effective coaching session. Learn the behaviours and steps under your control for achieving the end goal. Learning Objectives Explain the benefits of peer coaching, Implement behaviors of effective peer coaches, Apply the GROW model for sequencing questions in a coaching session Target Audience Managers, Team Leaders, Young Professionals, Sales Professionals, Customer Service Teams

Discover the power of data science and machine learning with Python! Learn essential techniques, algorithms, and tools to analyze data, build predictive models, and unlock insights. Dive into hands-on projects, from data manipulation to advanced machine learning applications. Elevate your skills and unleash the potential of Python for data-driven decision-making.

Basic Needs Coaching Sessions The basic needs model uses the principal of Maslow’s hierarchy to help you gain clarity on  What your needs are How they are prioritised This is helpful for people who are goal orientated and want to build a consistent pathway to meeting those goal and potential other aspirations. If you keep trying to meet your goals and find you keep falling-off-the-track as-it-were, an easy way to find out whether this approach could work for you before you invest is to download the Personal Wellbeing Audit give yourself 30 minutes to answer the questions and make notes before deciding whether you  Need support in some of the highlighted areas What that support may look like? What you can realistically budget for Clients who have has success with me in the past usually invest in sessions bi-monthly over a 12-month period

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About this Virtual Instructor Led Training (VILT) Governments, regulators and energy companies are pursuing CO2 storage technologies to meet their net-zero carbon commitments as well as targets set by the international Paris Agreement on climate change. For successfully executing Carbon Capture & Storage (CCS) projects, various technical, operational, economic and environmental risks and associated stakeholders need to be managed. In this 5 half-day Virtual Instructor Led Training (VILT) course, the methods for managing risk in CCS projects are addressed with a focus on CO2 injection and storage. The VILT course will also demonstrate how to assess storage capacity of a potential CO2 storage reservoir, model framing techniques, and well injectivity issues related to CO2 injection. The potential leak paths will be discussed such as reservoir seals, leakage along faults and aspects of well integrity. In the VILT course, the design of a monitoring programme will also be discussed. The VILT course will be supported by various case studies. This VILT course will cover the following modules: CCS projects in an international context Site selection and site characterization Storage capacity assessment Injectivity assessment Containment assessment Measurement, monitoring & verification Training Objectives On completion of this VILT course, participants will be able to: Uncover the functions and associated components required to capture, transport and store CO2 in subsurface aquifers and (depleted) hydrocarbon reservoirs Find a systematic and integrated approach to risk identification and assessment for CO2 storage projects (maturation) Appreciate the requirements (physics modelling) and uncertainties to assess the CO2 storage capacity of a selected site. Understand the challenges, data and methods to assess CO2 well injectivity and well integrity Identify the leakage pathways of a selected storage site, and understand the assessment methods and associated uncertainties Learn how to design a monitoring program Target Audience This VILT course is intended for all surface and subsurface engineers such as facility engineers, geologists, geophysicists, reservoir engineers, petrophysicists, production technologists/engineers, well engineers and geomechanical specialists. Also, (sub)surface team leads, project managers, business opportunity managers, decision executives, and technical risk assessment & assurance specialists will benefit from this VILT course as it provides a common framework and workflow to develop a CCS project. For each class, it is highly recommended that a mix of disciplines mentioned above are represented to facilitate discussions from different perspectives. Course Level Basic or Foundation Training Methods This VILT course is built around cases in which teams work to identify and assess CO2 storage site issues using a systematic thought approach in this course. In addition, exercises are used to practise the aspects of the CCS risk assessment process. The VILT course provides a venue for discussion and sharing of good practices as well as opportunities to practise multi-discipline co-operation and facilitation. Participants are encouraged to bring their own work issues and challenges and seek advice from the expert course leaders and other participants about all aspects of CCS. This VILT course will be delivered online in 5 half-day sessions comprising 4 hours per day, with 2 breaks of 10 minutes per day. Trainer Trainer 1: Your expert course leader has more than 36 years of experience in the oil & gas industry with Shell. He gained broad experience in petroleum engineering, with expertise in integrated production systems from subsurface, wells and surface. He has had assignments in Production Technology, R&D, Production Chemistry, Rock Mechanics and Reservoir Engineering cEOR, with a proven track record in technology screening, development and deployment, field development planning, conceptual well design and Production System Optimization (PSO) of gas and oil fields as well as preparing Well, Reservoir & Facility Management (WRFM) strategies and plans. He had also worked on assignments in NAM and did fieldwork in Oman, Gabon and Shell Nigeria. He is a skilled workshop facilitator. He discovered his passion for teaching following an assignment in Shell Learning. During his time in Shell, he developed and taught technical courses to Shell professionals via blended learning. Trainer 2: Your second expert course leader has over 30 years of experience identifying, assessing and mitigating technical risks with Shell. The main focal point of his experience is in subsurface and Geomechanical risks. He is the the founding father of various innovations in how we assess risks by tool development (for bore hole stability, 3D geomechanical field evaluations and probabilistic assessment). He also developed an eye for people motivation, change management and facilitation. He was also responsible for the Geomechanical competence framework, and associated virtual and classroom training programme in Shell for 10 years. Trainer 3: Your third expert course leader has more than 30 years of experience in Shell, focusing on research and development in drilling and offshore systems. His areas of expertise is in project management, finance, business planning, investment, development studies and economics models. In 2021, he worked on a project that looked into the economic evaluation of P18A field complex for CO2 storage. He has an MSc in Mechanical Engineering (M.E.) TU Delft Netherlands (Hons) and a baccalaureate from Erasmus University Rotterdam. 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 about post training coaching support and fees applicable for this. Accreditions And Affliations