1180 Model courses in Edinburgh delivered Live Online

This week long CPD accredited training is ideal for those Real Estate professionals, who are looking to bolster the DCF modelling skills. This course covers Real Estate Investment, Development, Finance & Risk.

Duration 2 Days 12 CPD hours This course is intended for The course is designed for individuals who want to gain in-depth knowledge and practice in the discipline of Modelling Business Processes (Business Analysts, Requirements Engineers, Product manager, Product Owner, Chief Product Owner, Service Manager, Service Owner, Project manager, Consultants) Overview Students should be able to demonstrate knowledge and understanding and application of Modelling Business Processes principles and techniques. Key areas are: The role of business process modelling Modelling core business processes at an organizational level Modelling business processes at the process level Documenting and analyzing tasks The use of gap analysis in improving business processes The Business Analyst role analyzes, understands and manages the requirements in a customer-supplier relationship and ensures that the right products are delivered. The course provides in-depth knowledge and practice in Modelling Business Processes Course Introduction Let?s Get to Know Each Other Course Overview Course Learning Objectives Course Structure Course Agenda Introduction to Business Process Modelling Exam Details Business Analysis Certification Scheme The Context for Business Process Modelling Purpose and benefits of business process modelling Three levels of business process hierarchy (Organization, process and task level) Process view versus the functional view of an organization Assessing the impact of a new process design (POPIT model) Implementation of a business change (Pilot, phased, direct changeover, parallel running) The Organizational Context for Business Processes Construction of an organizational level model of a business process External and internal elements of an organizational model Supporting the value proposition Modelling the Business Processes Construction of a business process model Standard notation Process, task and step OPOPOT External, internal and time-based events Internal performance measures and customer expectations of performance Documenting Tasks A task description UML activity diagram notation and structured english Improving Business Processes task automation, removal of gaps and disconnects, process reengineering business rules and assumptions Unsatisfactory performance Combinations of conditions Gap analysis

Duration 2 Days 12 CPD hours This course is intended for Project Managers, Business Analysts, Business and IT stakeholders working with analysts Overview Provide a solid foundation for applying business process modeling principles and best practices with BPMN Demonstrate how to solve practical business problems using BPMN Business Process Diagrams (BPDs) Students will learn to map business processes easily and efficiently using the industry standard - BPMN which stands for Business Process Modeling Notation from the Object Management Group (OMG). Students will learn the best practices in process mapping using the latest industry standards (BPMN) so that both the business and IT stakeholders will be able to understand the models and map processes consistently through-out their organization. Introduction What is Business Process Modeling? What is Business Process Modeling Notation (BPMN)? Benefits of BPMN An Overview of Governance An overview of governance Key governance questions to ask What happens if you don?t have effective governance? Mapping the Business Problem Define a strategic outcomes map Define a Business model and relevant processes Modeling Simple BPMN Structures When do you use BPMN? What are simple BPMN structures? AS-IS vs. TO-BE modeling Modeling Complex BPMN Structures What are complex BPMN structures When to use complex BPMN structures Analyzing Process Models Identifying poor process models Creating process models that everyone can understand Identify criteria for a well-defined process Process Mapping vs. Process Modeling Determine when to create a process map Determine when to create a process model Asking the four ?Ares? Additional Resources Learning about BPMN 2.0 More useful BPMN links BPMN 2.0 free editors

Sales and trade exist because people need and are looking for that which is better than what they have. Sometimes, they don’t even know that they have a need. The critical piece of this process is “connecting the dots.” We must demonstrate that we have listened respectfully, and, as experts, show how the needs are tied directly to our answer. Just because they have a need and we have a solution doesn’t mean that it’s a guaranteed sale. Connecting their needs to our solution is precisely what the Sales Presentation Skills class is all about. Outcomes – Participants will be able to: Research and understand each unique customer to demonstrate expertise; Conduct productive meetings to discover useful information to formulate the most effective solution(s); Propose plans that are fully aligned with the target’s situation and needs; Increase abilities to engage and motivate the prospect/client through compelling presentations; Convey emotional intelligence enthusiasm and sincerity to get client buy-in; Strengthen professionalism through dynamic story-telling, elevating the level of rapport; and Create positive messages even from negative, modeling a problem-solving, can-do attitude for the audience.   Online Format—Sales Presentation Skills is a 4-hour interactive virtual class. Register for this class and you will be sent ONLINE login instructions prior to the class date. Overall, your professionalism, your teaching style, and the content of the course kept it interesting and easy to follow. We believe in what you have taught us, having tried it first hand, I can honestly say, your course works; your methods and ideas have proven themselves. I look forward to working with you again and again in the future. Alan M. Kriegstein, PresidentALA Scientific Instruments, Inc.

This course is intended to the most ambitious modellers in the property industry who would like to be fully proficient in building complex real estate models.

Historical Association webinar series: Making history accessible Presenters: Gemma Hargraves and Sally Lonsdale From a special school perspective, Sally Lonsdale will explore how history is encountered at her school. With secondary students working at Key Stage 1 age related expectations, history is seen as an ‘enriching subject’ with a strong focus on literacy and vocabulary. The session will explore how students with Profound and Multiple Learning Disabilities and those with EHCP outcomes are supported and show how history teaching can be effective and joyful when directed by student experience and interests and focusing on skills rather than specific knowledge. To use your corporate recording offer on this webinar please fill in this form: https://forms.office.com/e/bdNUSwLNrL Image: A Squire "Old English" padlock on a gate latch in Devon (Image: Partonez/Wikimedia Commons)

Overview In this course you will learn to build a financial model by working in Excel and how to perform sensitivity analysis in Excel. You will also learn the formulas, functions and types of financial analysis to be an Excel power user. By attending this course, you will be able to effectively prepare and build financial models. Objectives Harness Excel's tools within a best practice framework Add flexibility to their models through the use of switches and flexible lookups Work efficiently with large data volumes Model debt effectively Approach modelling for tax, debt, pensions and disposals with confidence Build flexible charts and sensitivity analysis to aid the presentation of results Learn and apply Excel tools useful in financial forecasting Understand and design the layout of a flexible model Forecast financial statements of a public or private company Apply scenario analysis to the forecasted financial statements and prepare charts for data presentation

This Real Estate Financial Modelling course will enhance delegates skills in crafting sophisticated financial models tailored for debt financing within the Real Estate sector.

Explore China’s growing influence in Africa through this in-depth course. Weekly themes blend history, trends, and analysis to unpack the economic, political, and social layers of this evolving relationship. Gain a nuanced view of its impact on Africa’s global role

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints