382 Sciences courses delivered Live Online

Nowadays not only do we rely on our commercial and sales staff to hit that bottom line but we expect our engineers and project teams to play their part too - not only through their engineering and management skills but by behaving in a commercially minded way in their dealings with their counterparts in customer or supplier organisations. This means understanding, amongst other things, the issues surrounding the commencement of work ahead of contract, having a clear contract baseline, recognising the broader implications of contract change, the need for timeliness and the consequences of failing to meet the contracted timetable. This practical one-day programme has been designed specifically to give engineers, project staff and others just that understanding. The course is designed principally to provide engineers and project staff with an appreciation of contractual obligations, liabilities, rights and remedies so that they understand the implications of their actions. It is also suitable for business development staff who are negotiating contracts on behalf of the business. The main focus of the day is on creating an awareness of when a situation may have commercial implications that would harm an organisation's business interests if not recognised and handled appropriately and how taking a positive but more commercial approach to those situations can lead to a more positive outcome for the business. As well as providing an understanding of the commercial imperatives the day also focuses on specific areas affecting engineers and project staff, such as the recognition and management of change, the risks when working outside the contract and managing delays in contracts. The course identifies the different remedies that may apply according to the reasons for the delay and provides some thoughts on pushing back should such situations arise. On completion of this programme the participants will: appreciate the need for contractual controls and will have a better understanding of their relevance and how they can be applied, particularly the issues of starting work ahead of contract, implementing changes and inadvertently creating a binding contract by their behaviour; have gained an understanding of the terminology and procedural issues pertaining to contracting within a programme; and be more commercially aware and better equipped for their roles. 1 Basic contract law - bidding and contract formation Purpose of a contract Contract formation - the key elements required to create a legally binding agreement Completeness and enforceability Express and implied terms Conditions v warranties The use of, and issues arising from, standard forms of sale and purchase Use of 'subject to contract' Letters of intent Authority to commit 2 Change management Recognising changes to a contracted requirement Pricing change Implementation and management of change 3 Key contracting terms and conditions By the end of this module participants will be able to identify the key principles associated with: Pricing Getting paid and retaining payment Cashflow Delivery and acceptance Programme delaysExamining some reasons for non-performance...Customer failureContractor's failureNo fault delays ... and the consequences of non-performance: Damages claimsLiquidated damagesForce majeureContinued performance Waiver clauses and recent case law Use of best/reasonable endeavours Contract termination 4 Warranties, indemnities and liability Express and implied warranties Limiting liability 5 Protection of information Forms of intellectual property Background/foreground intellectual property Marking intellectual property Intellectual property rights Copyright Software Confidentiality agreements Internet

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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 course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Course Overview: It is important that everybody who works in the care environment recognises the signs of potential eating and drinking difficulties and is able to support service users to eat and drink. This course combines both theory and practical sessions to equip those who work in care settings with this knowledge.   Course Aims: Define Dysphagia Identify the main parts of the human mouth and pharynx Recognise signs and symptoms of aspiration Know when to refer a service user Recognise good positions at mealtimes Experience food textures and being fed in different positions Management responsibilities

M&O of engineering plant and services is becoming more important to the success of the core business. To select the right M&O technique for specific plant and equipment it is necessary to know what options are available, what they deliver and how they should be implemented. This course will help you consider options and techniques that provide best value based on that thorough understanding of the business need. M&O will increasingly be required to demonstrate it is delivering the optimum of cost and value and the main purpose of the course is to show how this can be achieved. Note: this is a purely indicative list of topics that can be covered. The content, duration, objectives and material used would all be adapted to match your specific requirements. This course will help you: Determine what the business needs from the M&O service Determine the cost and value of the various M&O options Prepare and present the business case for the selected M&O strategy Introduce meaningful KPIs based on performance-based service by the M&O provider Undertake a review of current M&O and make recommendations for improvement Introduce energy and carbon management performance criteria in M&O Better deal with project risk and uncertainties Motivate others to deliver a reliable and cost effective M&O service Note: this is a purely indicative list of topics that can be covered. The content, duration, objectives and material used would all be adapted to match your specific requirements. 1 What is maintenance and what is operation? - their relative importance to the business 2 Comparison of the various maintenance options and techniques, including: Planned preventative Run to failure Condition monitoring Business focused Business critical Total productive Reliability centred maintenance 3 Forms of contracts and service, including: Comprehensive Input driven Output driven Limited replacement Performance based M&O 4 Selecting the right options and making the business case 5 Continuous commissioning as a tool for delivering best value 6 Case studies

Duration 5 Days 30 CPD hours This course is intended for Operators, administrators, and architects for VMware Horizon should enroll in this course. These individuals are responsible for the creation, maintenance, and or delivery of remote and virtual desktop services. Additional duties can include the implementation, support, and administration of an organization's end-user computing infrastructure. Overview By the end of the course, you should be able to meet the following objectives: Assess the business and application requirements of an environment Analyze design choices and design an EUC infrastructure architecture that addresses the needs of the environment Design an EUC architecture that addresses the needs of the organization and follows VMware best practices Document a design that can be implemented Design a comprehensive VMware EUC solution This five-day course presents a methodology for designing a VMware end-user computing (EUC) solution. The design methodology includes recommendations for the types of information and data that must be gathered and analyzed to make sound design decisions for the client systems, the desktop options, the VMware vSphere© infrastructure, VMware Horizon©, VMware Horizon© Cloud ServiceTM, VMware Workspace ONE© UEM, VMware Workspace ONE© AccessTM, VMware NSX-TTM, and VMware Unified Access GatewayTM. VMware best practices are presented for each phase of the design process. In this course, you apply your new knowledge by working with other participants to design a VMware EUC solution for a real-world project. Course Introduction Introductions and course logistics Course objectives VMWare EUC Design Methodology Recognize the characteristics of a good design. Identify the phases of VMware EUC Design Methodology Presenting Solutions to Executive Management Identify behaviors that can affect team performance negatively List common mistakes when working in a team Describe how to tailor a presentation to the company?s values, and stakeholders? backgrounds and roles List questions you should ask to identify how to address a problem a client is trying to solve Infrastructure Assessment and Services Definition Define customer business objective Define a use case for your virtual desktop and application infrastructure Convert customer requirements to use-case attributes Horizon Architecture and Components Determine the components required for a Horizon on-premises or Horizon Cloud deployment Implement the design considerations and recommendations for various Horizon components Analyze the use-case scenarios for various the Horizon deployment options Component Design: Horizon Pods Design a single-site Horizon desktop block and pod configuration for a given use case Design Cloud Pod Architecture for multisite pool access Design Control Plane Services Architecture for multisite pool access Component Design: App Volumes and Dynamic Environment Manager Identify the functionalities of the components in App Volumes Logical Architecture Implement the recommended practices when designing an application delivery mechanism using App Volumes Component Design: Workspace ONE Map the Horizon desktop building block and the Horizon management building block to VMware vSphere. Identify factors and design decisions that determine the sizing for ESXi hosts Workspace ONE & Horizon Infrastructure Design Design the environment resources required to support a Workspace ONE and Horizon deployment Identify factors and design decisions that determine the sizing for ESXi host Discuss the factors that determine the sizing for shared storage Identify the design decisions related to bandwidth utilization Discuss the implications of using load balancing and traffic management Identify factors and design decisions that determine the sizing of the Azure pods Design Integration and Delivery List the platform components that needs to be integrated along with their dependent services. Integrate Workspace ONE and Horizon platform components. Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware End User Computing: Design 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 VMware End User Computing: Design 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 2 Days 12 CPD hours This course is intended for This introductory-level course is intended for Business Analysts and Data Analysts (or anyone else in the data science realm) who are already comfortable working with numerical data in Excel or other spreadsheet environments. No prior programming experience is required, and a browser is the only tool necessary for the course. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Throughout the hands-on course students, will learn to leverage Python scripting for data science (to a basic level) using the most current and efficient skills and techniques. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore (to a basic level): How to work with Python interactively in web notebooks The essentials of Python scripting Key concepts necessary to enter the world of Data Science via Python This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it?s often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. This course introduces data analysts and business analysts (as well as anyone interested in Data Science) to the Python programming language, as it's often used in Data Science in web notebooks. This goal of this course is to provide students with a baseline understanding of core concepts that can serve as a platform of knowledge to follow up with more in-depth training and real-world practice. Additional course details: Nexus Humans Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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 Python for Data Science Primer: Hands-on Technical Overview (TTPS4872) 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.

I cover a range of subjects, including mathematics, English and science, as well as targeted preparation for entrance exams such as the 11 plus, ISEB, and more. Additionally, I provide focused support for verbal reasoning and non-verbal reasoning assessments. Importantly, all tuition is bespoke, tailored to your child's unique learning needs and requirements. My goal is to offer comprehensive assistance tailored to the primary education curriculum and specific entrance requirements. I also support children in their transition to secondary school through secondary private tuition and GCSE private tuition in certain subjects. I can always recommend excellent private tutors if I am not able to support a particular subject, so get in touch!

A thorough account of the UK and European legal framework and its requirements as regards managing environmental performance. This course will help staff to understand: The framework of UK and European legislation and its enforcement The principal features of the legislation as they apply to your organisation's activity/product/service The benefit of having an Environmental Management System such as ISO 14001 How their own actions and decisions can either expose or protect the organisation in relation to its legal obligations 1 Introduction and objectives 2 Introduction to environmental law and enforcement Sources of law (European and UK) Structure and enforcement Key legislation 3 Integrated Pollution Prevention and Control (IPPC) and Local Air Pollution and Control (LAPC) Pollution and Prevention Control Act 1999 EC Directives on PPC The meaning of BAT Transitional provisions Fit and proper persons Control of emissions to air National Air Quality Strategy 4 Packaging and producer responsibilities Who, what and how The Producer Responsibility Obligations (Packaging Waste) Regulations Obligations and exemptions Registration Recycling and recovery obligations Records Duties of the Environment Agency Offences Developments 5 Waste management National Waste Strategy Waste minimisation (re-use/recycling) Waste definition Disposal and recovery Controlled waste management Hazardous waste management 6 Proposed Legislation and EC Directives EU Commission's waste and resources strategies Implementation of ELV (End of Life Vehicles) Directive WEEE (Waste Electrical and Electronic Equipment) Directive transposition into UK legislation Other producer responsibility initiatives Other proposals from the EU 7 Conclusion Open forum Summary Close