50209 Courses delivered Online

How to protect your cash flow In the current economic climate more and more companies are finding that their customers are taking longer to pay - or are not even paying at all. As cash flow is key to the survival of any business, effective debt collection tactics are vital for all businesses. This workshop concentrates on the telephone skills and techniques you can use to achieve the most positive outcome in any debt collection situation - payment of money owed, as soon as possible, whilst keeping the collection cost as low as possible. The course will help you: Understand your debtors and communicate with them accordingly Develop a strategy for more effective debt collection Make every call count Handle difficult calls Reduce the amount of time you need to spend on chasing payment Increase your collection rates 1 The debt collection process Understanding the reasons behind payment default Looking at the debt situation from the customer's point of view Developing a strategy for effective debt collection 2 Advanced telephone communication skills Techniques for speaking to the person responsible for paying the debt How to gain the customer's trust when discussing debt Telephone collection skills best practice Key phrases that keep the conversation positive and open 3 Questioning and listening skills for gathering information Different types of question Using high-gain questions to uncover key information Active listening that will help you understand what customers are really saying Leading with examples and high-impact questions Summarising and restating 4 Overcoming objections and excuses Identifying objections Preparing suitable responses Probing objections and ways to overcome them 5 Gaining commitment and ending the call Learn how to negotiate an agreement to suit both parties Summarising actions for you and the customer Ending the call professionally 6 Dealing with difficult and challenging situations Understand different personality types The correct way to respond to an upset customer Ways to calm angry customers (and handle verbal attacks) 7 Action plans Course summary and presentation of action plans

The course helps participants understand the role of demand and inventory planning in the wider context of supply chain management. It aims to demonstrate how to improve the alignment between supply and demand to maintain good levels of customer service and on-shelf availability whilst eliminating excess stock and reducing inventory investment. PARTICIPANTS WILL LEARN HOW TO: • Understand the role of demand management and its benefits • Identify the key demand characteristics and patterns; learn how to use them to improve forecast accuracy • Develop an understanding of key qualitative and quantitative forecasting methods • Learn how to conduct fundamental inventory analyses with a view to achieving the appropriate trade-off between stock and service level COURSE TOPICS INCLUDE: The role of Demand Management • The end-to-end view of Supply Chain Management • Demand Characteristics and the Product Life Cycle • Demand patterns • Push and pull systems Background to forecasting • The forecasting Process • Time-series methods of forecasting • Calculating forecast errors Inventory Analysis • Categorisation of stock • ABC Analysis • Economic order quantity and minimum order quantity • Safety stock and stock cover Inventory Management • Materials requirements planning (MRP) • Stock replenishment systems • Practical inventory management • The cost of managing stock

It is essential that those charged with responsibility for credit control and debt recovery have a full appreciation of the relevant law: no-one can negotiate effectively to recover a debt if they don't understand the ultimate sanctions they can apply. This programme is designed to give them a practical, up-to-date understanding of the law as it applies to your particular organisation. This course will help ensure that participants: Understand the relevant laws Know how and when to invoke legal processes Avoid legal pitfalls in debt collection negotiations Specific, practical learning points include: Definition of 'harassment' How to set up an in-house collection identity Whether cheques in 'full and final settlement' are binding The best steps to trace a 'gone away'... and many, many more. 1 Data protection and debt recovery There are a whole range of things which can be checked on members of the public and which are not affected by the restraints of the Data Protection Act. These will be explained in simple, clear terms so that staff can use this information immediately. 2 County Court suing The expert trainer will show how to sue for money owed, obtain judgment and commence enforcement action without leaving your desk. This module is aimed at showing how to make the Courts work for you instead of the other way around! 3 Enforcement of judgments There are many people who have a County Court Judgment (CCJ) against their debtor but who still remain unpaid. This session explains each of the enforcement methods and how to use them to best effect. Enforcement methods covered include: Warrant of Execution Using the sheriff (now known as High Court Enforcement Officers) Attachment of earnings Third Party Debt Orders Charging Orders (over property and goods) Winding-up companies and making individuals bankrupt 4 Office of Fair Trading rules on debt recovery Surprisingly few people are aware of the Office of Fair Trading rules on debt recovery and many of those that do know think they don't apply to them - but they do. Make sure you know what you need to! 5 New methods to trace elusive, absentee and 'gone away' debtors Why write the money off when you can trace the debtor and collect the money you are owed? 6 Credit checking of new and existing customers It makes sense to credit check would-be, new and existing customers to evaluate the likelihood of payment delays or perhaps not being paid at all. This session shows a range of credit checking steps, many of which can be done completely free of charge, including a sample credit application/ account opening form. 7 Late Payment of Commercial Debts Regulations Do your staff understand this legislation and how to use it to make people pay quicker than ever before? The trainer shows how. 8 The Enterprise Act The Enterprise Act made some startling changes to corporate and personal insolvency. What are the implications for credit control and debt recovery within your organisation?

This programme provides an intensive, one-day overview of the key concepts and techniques of project management. The project management methods presented can be applied to a wide range of projects and the course emphasises both the task and the team-related aspects of project management. The aims of the programme are to: Present the key concepts of project management Provide a structured approach for managing projects Demonstrate tools and techniques for planning and controlling project work Enable participants to apply the techniques to their own projects At the end of the programme, participants will: Recognise the benefits of a structured approach to project work Be able to apply a range of practical tools and techniques to improve their personal effectiveness in project work Have a means of determining the status of current projects and know what actions are needed to ensure success 1 Introduction Why this programme has been developed Review of participants' needs and objectives 2 Key concepts The characteristics of projects and project work The four key phases of a project Essential lessons from past projects Key success factors Achieving success through the 'Team-Action Model' Challenges of the multi-project situation 3 Setting project goals Understanding 'customer' requirements Managing project stakeholders and gaining commitment Using questioning skills to define goals and success criteria Defining and documenting the scope of the project 4 Project planning Defining what has to be done Creating a work breakdown Agreeing roles and responsibilities for the work Developing a programme using networks and bar charts Estimating timescales, costs and resource requirements Planning exercise: participants develop a project plan Identifying and managing project risks Using project planning software Managing and updating the plan 5 Project implementation and control Creating a pro-active monitoring and control process Techniques for monitoring progress Using latest estimates Managing project meetings Resolving problems effectively Managing multiple projects Personal time management 6 Course review and action planning Identify actions Sponsor-led review and discussion of proposals Conclusion

To help you make the most of your learning experience, we would like to offer you a complimentary 1-to-1 session with one of our experienced English language experts. During this personalised session, you will have the opportunity to: Discuss the course details. Identify specific areas you would like to focus on, whether it's grammar, speaking, listening, writing, or vocabulary. Receive tailored advice on which course or learning path aligns best with your objectives. In the meantime, we recommend taking our placement test on our website. This will help our expert understand your current proficiency level and tailor the course to your specific needs. The test is a crucial step to ensure we provide you with the most effective support https://virtualeducators.co.uk/test-your-english If you have any questions, please do not hesitate to reach out. Have a great day, Best regards, Customer Services Virtual Educators Ltd. customerservices@virtualeducators.co.uk www.virtualeducators.co.uk

Duration 3 Days 18 CPD hours

To explore the factors which affect and influence feacal continence when supporting individuals in order to effectively manage bowel incontinence.

This one-day workshop is designed to build upon skills already acquired using Microsoft Word, whether participants are self-taught or have previously attended a course. It gives participants a good understanding of managing different types of paragraph indents, and managing automated numbered paragraph lists. This course will help participants: Create and manage the layout of paragraphs Create and manage multi-level numbered paragraphs Effectively insert, use and manage section breaks Create and manage columns Find, create and apply styles Create and update a table of contents from styles Work with styles to manage formatting Work with sums in tables and use table headings Input and edit text using AutoText Work with SmartArt graphics Link to other files using paste special 1 Managing paragraph layout Controlling paragraph layouts with indents Creating and managing paragraph hanging indents 2 Numbering paragraphs Creating a new multi-level numbered paragraph Managing existing multi-level numbered paragraphs 3 Inserting columns Creating columns from existing paragraphs Setting up columns Managing columns 4 Document section breaks Controlling document layout using section breaks Managing page orientation using section breaks Managing page numbering using section breaks 5 Using styles Applying quick styles Finding existing styles Creating and saving styles Modifying styles Creating a table of contents from styles Updating a table of contents 6 Using quick parts Saving content to quick parts Modifying saved quick parts Inserting content using AutoText Editing AutoText entries Inputting text using AutoCorrect 7 Advanced table features Sorting table columns Running sums in tables Repeating table headings at top of page Importing Excel content in tables Using table properties 8 Creating a mail merge Creating letters, labels and emails Merging addresses from external files Using Outlook's address book to merge Merging to email 9 SmartArt graphic Visually communicate content with SmartArt Choosing a SmartArt graphic Inputting into SmartArt Modifying and formatting SmartArt graphics 10 Linking to other files Linking to files using paste special Linking an Excel chart to a document Managing linked content from Word

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

Mental Health First Aid (MHFAider) is an internationally recognised training course that teaches people how to spot the signs and symptoms of mental ill health and provide help on a first aid basis. We don't teach people to be therapists, but we do teach people how to respond in a crisis, and how to reach out before a crisis happens. The training gives people tools to support themselves and each other, so everyone can talk about mental health and seek help when needed. As an MHFAider you will be able to: Recognise those that may be experiencing poor mental health and provide them with first-level support and early intervention Encourage a person to identify and access sources of professional help and other support Practise active listening and empathy Have a conversation with improved mental health literacy around language and stigma Discuss the role in depth, including boundaries and confidentiality Practise self-care This course is ideal for those who would like to become an MHFAider to: Gain the knowledge and skills to spot signs of people experiencing poor mental health Be confident starting a conversation and signpost a person to appropriate support Alongside the best evidence-based Mental Health First Aid (MHFA) training, MHFAiders are also provided with three-year access to ongoing learning and support through the MHFAider Support App 1 Introduction to MHFAider (3 hours 30 mins) MHFA and the MHFAider role Introduction to the MHFAider Action Plan 'ALGEE' What is Mental Health? Helpful and unhelpful language Undersign our Frame of Reference, understanding how we make sense of the world Understanding stress & the Stress Container 2 Understanding Mental Health (4 hours) What influences mental health? The Mental Health Continuum What is anxiety? What is a traumatic event? Active listening and empathy What are eating disorders? What is self-harm? What is substance misuse? MHFA conversation practice 3 MHFAider in practice (4 hours) Applying ALGEE What is depression? What is suicide? What is psychosis? MHFA conversation practice 4 Next steps (3 hours) Recovery and lived experience Applying ALGEE Boundaries in the MHFAider role MHFA conversation practice Moving forward in the MHFAider role and your MHFA action plan Self-care