88 Courses in Cardiff delivered Live Online

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: A technical audience at an intermediate level Overview Using Amazon SageMaker, this course teaches you how to: Prepare a dataset for training. Train and evaluate a machine learning model. Automatically tune a machine learning model. Prepare a machine learning model for production. Think critically about machine learning model results In this course, learn how to solve a real-world use case with machine learning and produce actionable results using Amazon SageMaker. This course teaches you how to use Amazon SageMaker to cover the different stages of the typical data science process, from analyzing and visualizing a data set, to preparing the data and feature engineering, down to the practical aspects of model building, training, tuning and deployment. Day 1 Business problem: Churn prediction Load and display the dataset Assess features and determine which Amazon SageMaker algorithm to use Use Amazon Sagemaker to train, evaluate, and automatically tune the model Deploy the model Assess relative cost of errors Additional course details: Nexus Humans Practical Data Science with Amazon SageMaker training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Practical Data Science with Amazon SageMaker course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Aligned with the AIGP certification program, AI Governance Professional Training is for professionals tasked with implementing AI governance and risk management in their organizations. It provides baseline knowledge and strategies for responding to complex risks associated with the evolving AI landscape. This training meets the rapidly growing need for professionals who can develop, integrate and deploy trustworthy AI systems in line with emerging laws and policies. About This Course This training teaches critical artificial intelligence governance concepts that are also integral to the AIGP certification exam. While not purely a 'test prep' course, this training is appropriate for professionals who plan to certify, as well as for those who want to deepen their AI governance knowledge. Both the training and the exam are based on the same body of knowledge.   Module 1: Foundations of artificial intelligence Defines AI and machine learning, presents an overview of the different types of AI systems and their use cases, and positions AI models in the broader socio-cultural context. Module 2: AI impacts on people and responsible AI principles Outlines the core risks and harms posed by AI systems, the characteristics of trustworthy AI systems, and the principles essential to responsible and ethical AI. Module 3: AI development life cycle Describes the AI development life cycle and the broad context in which AI risks are managed. Module 4: Implementing responsible AI governance and risk management Explains how major AI stakeholders collaborate in a layered approach to manage AI risks while acknowledging AI systems' potential societal benefits. Module 5: Implementing AI projects and systems Outlines mapping, planning and scoping AI projects, testing and validating AI systems during development, and managing and monitoring AI systems after deployment. Module 6: Current laws that apply to AI systems Surveys the existing laws that govern the use of AI, outlines key GDPR intersections, and provides awareness of liability reform. Module 7: Existing and emerging AI laws and standards Describes global AI-specific laws and the major frameworks and standards that exemplify how AI systems can be responsibly governed. Module 8: Ongoing AI issues and concerns Presents current discussions and ideas about AI governance, including awareness of legal issues, user concerns, and AI auditing and accountability issues. Accreditation The associated exam is accredited by the IAPP under its ANSI Accreditation Who Should Attend? Any professionals tasked with developing AI governance and risk management in their operations, and anyone pursuing IAPP Artificial Intelligence Governance Professional certification. Prerequisites A general understanding of AI, Corporate Governance, and Business value would be of benefit to participants. Assessment As with all IAPP exams, the AIGP is a 90 question, multiple choice exam to be completed within 150 minutes. Exams are hosted by Pearsonvue and can be taken either remotely, or via any one of hundreds of exam venues globally. A passing score is achieved at 70% Our Guarantee We are an approved IAPP training provider Exam pass guarantee, or retrain until you do, for free What's Included? Participant Guide Study Guide Practice Exam Exam voucher Breakfast, lunch, coffees and snacks (Classroom courses only) Certification Logo

Duration 3 Days 18 CPD hours This course is intended for The EXIN BCS Artificial Intelligence Foundation certification is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, education or IT services. Overview You will be able to Describe how Artificial (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Demonstrate Understanding of the Artificial Intelligence (AI) Intelligen Agent Description Explain the Benefits of Artificial Intelligence (AI) Describe how we Learn from Data - Functionality, Software and Hardware Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together Describe a ''Learning from Experience'' Agile Approach to Projects Candidates should be able to demonstrate a knowledge and understanding in the application of ethical and sustainable Artificial Intelligence (AI):- Human-centric Ethical and Sustainable Human and Artificial Intelligence (AI) Ethical and Sustainable Human and Artificial Intelligence (AI) Recall the General Definition of Human and Artificial Intelligence (AI) Describe what are Ethics and Trustworthy Artificial Intelligence (AI) Describe the Three Fundamental Areas of Sustainability and the United Nationïs Seventeen Sustainability Goals Describe how Artificial Intelligence (AI) is Part of 'Universal Design', and 'The Fourth Industrial Revolution' Understand that Machine Learning (ML) is a Significant Contribution to the Growth of Artificial Intelligence (AI) Artificial Intelligence (AI) and Robotics Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description Describe what a Robot is Describe what an intelligent Robot is Applying the Benefits of Artificial Intelligence (AI) ? Challenges and Risks Describe how Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations Explain the Benefits of Artifical Intelligence (AI) Describe the Challenges of Artificial Intelligence (AI) Projects Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects List Opportunities for Artificial Intelligence (AI) Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs) Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox ? Theory and Practice Describe how we Learn from Data - Functionality, Software and Hardware Recall which Rypical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine Learning (ML9 and Artificial Intelligence (AI) AgentsïFunctionality The Management, Roles and Responsibilities of Humans and Machines Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together List Future Directions of Humans and Machines Working Together Describe a ''Learning from Experience'' Agile Approach to Projects

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course is any IT, facilities or data centre professional who works in and around the data centre and has the responsibility to achieve and improve efficiency and environmental sustainability, whilst maintaining the availability and manageability of the data centre. Overview After completion of the course the participant will be able to: Understand the impact of data centres on the environment Describe the various environmental/energy management standards Understand the purpose and goals of the legally binding international treaties on climate change Implement various sustainable performance metrics and how to use them in the data centre environment Manage data centre environmental sustainability using international standards Set up the measurement, monitoring and reporting of energy usage Use power efficiency indicators in a variety of data centre designs Use best practices for energy savings in the electrical infrastructure and in the mechanical (cooling) infrastructure Use best practices for energy savings for the ICT equipment and data storage Understand the importance of water management and waste management Understand the different ways to use sustainable energy in the data centre Get practical tips and innovative ideas to make a data centre more sustainable The CDESS© course is aimed at providing knowledge of the standards and guidelines related to environmental sustainability, and how to move your data centre (existing or new) to a more environmentally sustainable design and operations. Impact of Data Centres on the Environment Predictions in 2010 Current situation Outlook and commitments What is Environmental Sustainability The importance of sustainability Senior management commitment Environmental sustainability framework Sustainability policies Performance standards and metrics Information policies Transparency Awareness Service charging models Environmental Management Environmental sustainability framework (ISO 14001) Standards and guidelines ? ISO 50001 / ISO 30134 Measurement and categories Baselining Trend analysis Reporting Power Effiðciency Indicators Various eðfficiency indicators Power Usage Effectiveness (PUE) PUE measurement levels Factors affecting PUE Measurement points and intervals PUE in mixed source environments Measuring PUE in a mixed-use building PUE reporting Impact of PUE after optimising IT load Electrical Energy Savings (Electrical) Identifying the starting point for saving energy Sizing of power DC power Generators UPS systems Power Factor (PF) Energy savings on lighting Electrical Energy Savings (Mechanical) Energy savings on the cooling infrastructure Temperature and humidity setpoints Various energy eðcient cooling technologies Energy savings on the airflow Liquid cooling Energy reusage PUE, ERE/ERF and Control Volume Electrical Energy Savings (ICT) Procurement IT equipment energy eðfficiency ITEEsv, SMPE, SMPO IT equipment utilisation Server virtualisation Open compute project Electrical Energy Savings (Data Storage) Data management Data storage management Data storage equipment effiðciency Water Management Water Usage Effectiveness (WUE) Improving WUE Water usage at the power generation source Energy Water Intensity Factor (EWIF) Waste Management Waste management policies Life-cycle assessment (Cradle to the grave) 3 R?s for waste management Reduce Reuse Second-hand market Recycle Sustainable Energy Usage Sustainable energy sources Power purchase agreements Energy attribute certificates Renewable Energy Factor (REF) Matching renewable energy supply and demand Sustainable energy storage Carbon trading Automated Environmental Management Systems Use of AI and machine learning Load migration Data Centre Infrastructure Management (DCIM) solutions

Duration 2 Days 12 CPD hours This course is intended for The target audience for the DevOps Foundation course includes Management, Operations, Developers, QA and Testing professionals such as: Individuals involved in IT development IT operations or IT service management. Individuals who require an understanding of DevOps principles. IT professionals working within, or about to enter, an Agile Service Design Environment The following IT roles: Automation Architects, Application Developers, Business Analysts, Business Managers, Business Stakeholders, Change Agents, Consultants, DevOps Consultants, DevOps Engineers, Infrastructure Architect, Integration Specialists, IT Directors, IT Managers, IT Operations, IT Team Leaders, Lean Coaches, Network Administrators, Operations Managers, Project Managers, Release Engineers, Software Developers, Software Tester/QA, System Administrators, Systems Engineers, System Integrators, Tool Providers. Overview The learning objectives for DevOps Foundation include an understanding of: DevOps objectives and vocabulary Benefits to the business and IT Principles and practices including Continuous Integration, Continuous Delivery, testing, security and the Three Ways DevOps relationship to Agile, Lean and ITSM Improved workflows, communication and feedback loops Automation practices including deployment pipelines and DevOps toolchains Scaling DevOps for the enterprise Critical success factors and key performance indicators Real-life examples and results The DevOps Foundation course provides a baseline understanding of key DevOps terminology to ensure everyone is talking the same language and highlights the benefits of DevOps to support organizational success. Learners will gain an understanding of DevOps, the cultural and professional movement that stresses communication, collaboration, integration, and automation to improve the flow of work between software developers and IT operations professionals. This course prepares you for the DevOps Foundation (DOFD) certification. Exploring DevOps Defining DevOps Why Does DevOps Matter? Core DevOps Principles The Three Ways The First Way The Theory of Constraints The Second Way The Third Way Chaos Engineering Learning Organizations Key DevOps Practices Continuous Testing, Integration, Delivery, Deployment Site Reliability & Resilience Engineering DevSecOps ChatOps Kanban Business and Technology Frameworks Agile ITSM Lean Safety Culture Learning Organizations Continuous Funding Culture, Behaviors & Operating Models Defining Culture Cultural Debt Behavioral Models Organizational maturity models Automation & Architecting DevOps Toolchains CI/CD Cloud, Containers, and Microservices AI and Machine Learning Automation DevOps Toolchains Measurement, Metrics, and Reporting The Importance of Measurement DevOps Metrics - Speed, Quality, Stability, Culture Change lead/cycle time Value Driven Metrics Sharing, Shadowing and Evolving DevOps in the Enterprise Roles DevOps Leadership Organizational Considerations Getting Started Challenges, Risks, and Critical Success Factors Additional course details: Nexus Humans DevOps Foundation (DevOps Institute) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the DevOps Foundation (DevOps Institute) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.

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

About this Virtual Instructor Led Training (VILT) The energy industry has started its journey to be more data centric by embracing the industry 4.0 concept. As a result, data management - which was considered until recently as a back-office service to support geoscience, reservoir management, engineering, production and maintenance - is now given the spotlight! To become an active stakeholder in this important transition in E&P data management, it is necessary to understand the new technical opportunities offered by the Cloud, Artificial Intelligence and how data governance can pave the way towards more reliable and resilient processes within E&P domain. Several key questions that need to be addressed: Why place more focus on data assets? Is data management just about serving geoscientists or engineers with fresh data? What is the value of data management in the E&P sector for decision making? How to convince the data consumers that the data we provide is reliable? Is the data architecture of my organization appropriate and sustainable? The purpose of this 5 half-day Virtual Instructor Led Training (VILT) course is to present the data challenges facing the energy organizations today and see how they practically solve them. The backbone of this course is based on the DAMA Book of Knowledge for Data Management. The main data management activities are described in sequence with a particular focus on recent technological developments. Training Objectives Upon completion of this VILT course, the participants will be able to: Understand why the data asset is now considered as a main asset by energy organizations Appreciate the importance of data governance and become an active stakeholder of it Understand the architecture and implementation of data structure in their professional environment Get familiarized with the more important data management activities such as data security and data quality Integrate their subsurface and surface engineering skills with the data managements concepts This VILT course is unique on several points: All notions are explained by some short presentations. For each of them, a set of video, exercises, quizzes will be provided to help develop an engaging experience between the trainer and the participants A pre-course questionnaire to help the trainer focus on the participants' needs and learning objectives A detailed reference manual A lexicon of terms for data-management Limited class size to encourage the interactivity Target Audience This VILT course is intended for: Junior/new data managers Geoscientists Reservoir engineers Producers Maintenance specialists Construction specialists Human resources Legal Course Level Basic or Foundation Training Methods The VILT course will be delivered online in 5 half-days consisting 4 hours per day, with 2 breaks of 10 minutes per day. Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total). Trainer Your expert course leader is a geologist by education who has dedicated his career to subsurface data management services. In 2016, he initiated a tech startup dedicated to Data Management using Artificial Intelligence (AI) tools. He is heavily involved in developing business plans, pricing strategies, partnerships, marketing and SEO, and is the co-author of several Machine Learning publications. He also delivers training on Data Management and Data Science to students and professionals. Based in France, he was formerly Vice President, Sales & Marketing at CGG where he was in charge of the Data Management Services strategy, Sales Manager at Spie O&G Services where he initiated the Geoscience technical assistance activities and Product Manager of interactive seismic inversion software design and marketing at Paradigm.       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

About this Course This 5 full-day course presents the most modern statistical and mathematical forecasting frameworks used by practitioners to tackle the load forecasting problem across short time and long time scales. The course presents practical applications to solving forecasting challenges, supported by real life examples from large control areas. It presents the weather impacts on the load forecasts and the methodologies employed to quantify the weather effect and building a repository of weather normal data. A good load forecast methodology must improve its forecasting accuracy and support a consistent load forecasting process. The load forecasting widely used in the power industry has evolved significantly with the advancement and adoption of Artificial Intelligence techniques such as Machine Learning. With the increased penetration of inverter-based resources, the operation of electric grids grew in complexity, leading to load forecasts that are updated more frequently than once a day. Furthermore, several jurisdictions adopted a smaller granularity than the hourly load forecasts in the effort to reduce the forecasting uncertainties. On the generation side, fuel forecasting professionals must meet energy requirements while making allowance for the uncertainty on both the demand and the supply side. This training course will also feature a guest speaker, who is a Ph.D candidate to provide insights into the most modern aspects of Artificial Intelligence in the context of load forecasting. Training Objectives This course offers a comprehensive approach to all aspects of load forecasting: Gain a perspective of load forecasting from both operators in the generating plant and system operators. Understand and review the advanced load forecasting concepts and forecasting methodologies Learn the application of Artificial Neural Networks and Probabilistic Forecasting methods to manage forecasting uncertainties in short time frames Appreciate market segmentation and econometric framework for long term forecasts Find out the most recent practical application of load forecasting as examples from large power companies Get access to recent industry reports and developments Target Audience Energy load forecasting professionals from power plant and system operators Energy planners and energy outlook forecasters and plant operators Fuel procurement professionals Planners and schedulers of thermal generating units Course Level Intermediate Trainer Your expert course instructor is a Utility Executive with extensive global experience in power system operation and planning, energy markets, enterprise risk and regulatory oversight. She consults on energy markets integrating renewable resources from planning to operation. She led complex projects in operations and conducted long term planning studies to support planning and operational reliability standards. Specializing in Smart Grids, Operational flexibilities, Renewable generation, Reliability, Financial Engineering, Energy Markets and Power System Integration, she was recently engaged by the Inter-American Development Bank/MHI in Guyana. She was the Operations Expert in the regulatory assessment in Oman. She is a registered member of the Professional Engineers of Ontario, Canada. She is also a contributing member to the IEEE Standards Association, WG Blockchain P2418.5. With over 25 years with Ontario Power Generation (Revenue $1.2 Billion CAD, I/S 16 GW), she served as Canadian representative in CIGRE, committee member in NSERC (Natural Sciences and Engineering Research Council of Canada), and Senior Member IEEE and Elsevier since the 90ties. Our key expert chaired international conferences, lectured on several continents, published a book on Reliability and Security of Nuclear Power Plants, contributed to IEEE and PMAPS and published in the Ontario Journal for Public Policy, Canada. She delivered seminars organized by the Power Engineering Society, IEEE plus seminars to power companies worldwide, including Oman, Thailand, Saudi Arabia, Malaysia, Indonesia, Portugal, South Africa, Japan, Romania, and Guyana. Our Key expert delivered over 60 specialized seminars to executives and engineers from Canada, Europe, South and North America, Middle East, South East Asia and Japan. Few examples are: Modern Power System in Digital Utilities - The Energy Commission, Malaysia and utilities in the Middle East, GCCIA, June 2020 Assessment of OETC Control Centre, Oman, December 2019 Demand Side management, Load Forecasting in a Smart Grid, Oman, 2019 Renewable Resources in a Smart Grid (Malaysia, Thailand, Indonesia, GCCIA, Saudi Arabia) The Modern Power System: Impact of the Power Electronics on the Power System The Digital Utility, AI and Blockchain Smart Grid and Reliability of Distribution Systems, Cyme, Montreal, Canada Economic Dispatch in the context of an Energy Market (TNB, Sarawak Energy, Malaysia) Energy Markets, Risk Assessment and Financial Management, PES, IEEE: Chicago, San Francisco, New York, Portugal, South Africa, Japan. Provided training at CEO and CRO level. Enterprise Risk methodology, EDP, Portugal Energy Markets: Saudi Electricity Company, Tenaga National Berhad, Malaysia Reliability Centre Maintenance (South East Asia, Saudi Electricity Company, KSA) EUSN, ENERGY & UTILITIES SECTOR NETWORK, Government of Canada, 2016 Connected+, IOT, Toronto, Canada September 2016 and 2015 Smart Grid, Smart Home HomeConnect, Toronto, Canada November 2014 Wind Power: a Cautionary Tale, Ontario Centre for Public Policy, 2010 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