45096 Courses

Course Title: Train the Trainer – Designing and Delivering Effective Training Course Duration: 2 days (or modular format over 3–4 half-day sessions) Target Audience: New or aspiring trainers, facilitators, team leads, or subject-matter experts who deliver training or knowledge-sharing sessions. Course Objectives By the end of this course, participants will be able to: Understand the principles of adult learning and training design. Confidently plan and structure engaging training sessions. Deliver content clearly using effective facilitation techniques. Manage group dynamics and encourage learner participation. Evaluate training effectiveness and improve performance. Course Outline Day 1: Designing Training for Adult Learners Module 1: Understanding the Trainer’s Role Role and responsibilities of an effective trainer Differences between training, presenting, and facilitating Characteristics of great trainers Module 2: Adult Learning Principles How adults learn: motivation, barriers, and learning preferences Learning styles and engagement strategies Applying adult learning theory to real training contexts Module 3: Training Needs Analysis Identifying learning needs and performance gaps Defining clear learning objectives Aligning training outcomes with organisational goals Module 4: Structuring a Training Session Designing training using ADDIE or the 4MAT model Creating session plans and timelines Balancing content delivery with interaction Day 2: Delivering and Evaluating Engaging Training Module 5: Facilitation Skills and Training Delivery Verbal and non-verbal communication Creating a safe and inclusive learning environment Encouraging participation and managing learner resistance Techniques for in-person and online delivery Module 6: Using Training Tools and Technology Using visuals and presentation aids effectively Incorporating activities, case studies, and role plays Facilitating discussions, group work, and Q&A Tips for hybrid and online delivery (Zoom, Teams, etc.) Module 7: Handling Group Dynamics and Challenges Managing difficult participants or situations Reading the room and adjusting on the fly Building confidence as a trainer Module 8: Evaluating Training Effectiveness Gathering and using learner feedback (Kirkpatrick Model) Self-reflection and peer observation Continual improvement of training materials and delivery Delivery Style Practical, hands-on workshops with active participation Peer feedback, group work, and presentation practice Real-time coaching and confidence building Assessment and Certification (Optional) Mini training delivery by each participant with peer and trainer feedback Completion of a training session plan Certificate of completion (customisable to organisation) Course Materials Provided Participant workbook and templates Sample training session plans and evaluation forms Trainer’s checklist and facilitation guide Resource list for further development

Course Duration: 2 days (or modular format over 3–4 half-day sessions) Target Audience: New or aspiring trainers, facilitators, team leads, or subject-matter experts who deliver training or knowledge-sharing sessions. Course Objectives By the end of this course, participants will be able to: Understand the principles of adult learning and training design. Confidently plan and structure engaging training sessions. Deliver content clearly using effective facilitation techniques. Manage group dynamics and encourage learner participation. Evaluate training effectiveness and improve performance. Course Outline Day 1: Designing Training for Adult Learners Module 1: Understanding the Trainer’s Role Role and responsibilities of an effective trainer Differences between training, presenting, and facilitating Characteristics of great trainers Module 2: Adult Learning Principles How adults learn: motivation, barriers, and learning preferences Learning styles and engagement strategies Applying adult learning theory to real training contexts Module 3: Training Needs Analysis Identifying learning needs and performance gaps Defining clear learning objectives Aligning training outcomes with organisational goals Module 4: Structuring a Training Session Designing training using ADDIE or the 4MAT model Creating session plans and timelines Balancing content delivery with interaction Day 2: Delivering and Evaluating Engaging Training Module 5: Facilitation Skills and Training Delivery Verbal and non-verbal communication Creating a safe and inclusive learning environment Encouraging participation and managing learner resistance Techniques for in-person and online delivery Module 6: Using Training Tools and Technology Using visuals and presentation aids effectively Incorporating activities, case studies, and role plays Facilitating discussions, group work, and Q&A Tips for hybrid and online delivery (Zoom, Teams, etc.) Module 7: Handling Group Dynamics and Challenges Managing difficult participants or situations Reading the room and adjusting on the fly Building confidence as a trainer Module 8: Evaluating Training Effectiveness Gathering and using learner feedback (Kirkpatrick Model) Self-reflection and peer observation Continual improvement of training materials and delivery Delivery Style Practical, hands-on workshops with active participation Peer feedback, group work, and presentation practice Real-time coaching and confidence building Assessment and Certification (Optional) Mini training delivery by each participant with peer and trainer feedback Completion of a training session plan Certificate of completion (customisable to organisation) Course Materials Provided Participant workbook and templates Sample training session plans and evaluation forms Trainer’s checklist and facilitation guide Resource list for further development

Course Duration: 2 days (or modular format over 3–4 half-day sessions) Target Audience: New or aspiring trainers, facilitators, team leads, or subject-matter experts who deliver training or knowledge-sharing sessions. Course Objectives By the end of this course, participants will be able to: Understand the principles of adult learning and training design. Confidently plan and structure engaging training sessions. Deliver content clearly using effective facilitation techniques. Manage group dynamics and encourage learner participation. Evaluate training effectiveness and improve performance. Course Outline Day 1: Designing Training for Adult Learners Module 1: Understanding the Trainer’s Role Role and responsibilities of an effective trainer Differences between training, presenting, and facilitating Characteristics of great trainers Module 2: Adult Learning Principles How adults learn: motivation, barriers, and learning preferences Learning styles and engagement strategies Applying adult learning theory to real training contexts Module 3: Training Needs Analysis Identifying learning needs and performance gaps Defining clear learning objectives Aligning training outcomes with organisational goals Module 4: Structuring a Training Session Designing training using ADDIE or the 4MAT model Creating session plans and timelines Balancing content delivery with interaction Day 2: Delivering and Evaluating Engaging Training Module 5: Facilitation Skills and Training Delivery Verbal and non-verbal communication Creating a safe and inclusive learning environment Encouraging participation and managing learner resistance Techniques for in-person and online delivery Module 6: Using Training Tools and Technology Using visuals and presentation aids effectively Incorporating activities, case studies, and role plays Facilitating discussions, group work, and Q&A Tips for hybrid and online delivery (Zoom, Teams, etc.) Module 7: Handling Group Dynamics and Challenges Managing difficult participants or situations Reading the room and adjusting on the fly Building confidence as a trainer Module 8: Evaluating Training Effectiveness Gathering and using learner feedback (Kirkpatrick Model) Self-reflection and peer observation Continual improvement of training materials and delivery Delivery Style Practical, hands-on workshops with active participation Peer feedback, group work, and presentation practice Real-time coaching and confidence building Assessment and Certification (Optional) Mini training delivery by each participant with peer and trainer feedback Completion of a training session plan Certificate of completion (customisable to organisation) Course Materials Provided Participant workbook and templates Sample training session plans and evaluation forms Trainer’s checklist and facilitation guide Resource list for further development

'Interventional Pain Medicine applied to Palliative Care Patients' by Dr Andrew Jones, Consultant Anaesthetist. This presentation will cover a brief background of the Hospice movement, the mechanisms of pain in the brain and spinal cord will be discussed. The limitations of pain medication will be presented. Thus, interventional pain techniques may have value for patients whose pain is not controlled. The interventional techniques available will be shown. Dr Jones, qualified in 1982 from Barts Hospital in London and after a wide range of junior roles in different specialties he started training in Anaesthesia in Merseyside. He had always been interested in the treatment of pain and was able to pursue further training in Pain Medicine. Andy became a consultant anaesthetist in 1994 and in addition a consultant in Pain Medicine in 1996. The rest is history.

Investigate and recognise the role of project sponsor and the importance of the relationship between sponsor and project manager and how this can be improved. Course overview Duration: 1 day (6.5 hours) This workshop is designed to investigate, understand and develop the role of the project sponsor. By taking elements of effective governance from the guidance published by the APM (Association for Project Management) and Axelos Managing Successful Programmes, an improved project sponsor capability can be developed. The workshop will also explore the importance of the relationship between sponsor and project manager and how this can be improved. Whilst predominately designed for the project sponsor role, there is provision for a discussion with project manager representatives to agree an action plan to improve the delivery of projects within an organisation. Objectives  By the end of the course you will be able to: Describe and commit to the role of the Project Sponsor Identify the key principles of governance that can contribute to success Describe the environment in which the projects are delivered Explain the financial and resource constraints within the project environment Define the criteria used for project selection Identify the expectations of key stakeholders and develop a clear communication plan to engage with them Identify and resolve areas of conflict Describe the relationship with the Project Manager Create an effective environment and relationship for project success Content Introduction The Project Environment The Principles of Governance The background to the training Roles The Project Sponsor The Project Manager The Project Board Sponsor responsibilities Defining Project Selection Criteria and Prioritisation Business Case and Justification Finance and Funding Resourcing the projects Stakeholder Engagement Project Governance Resolving conflict between stakeholders Benefits Realisation Capitalising the lessons learned during project reviews Delivering success The relationship with the Project Manager and team Action Plan for the future The workshop will use case studies drawn from previous client projects, both successful and less successful.

Businesses that don't control their costs don't stay in business. How well are you doing? Is everyone in your organisation sufficiently aware of costs, managing them effectively and maximising opportunities to reduce them? If there is scope for improvement, this course will help get you back on track. It will demonstrate that cost reduction is so much more than cost control and cost cutting. True cost management is about being aware of costs, seeking to reduce them through good design and efficient operating practices whilst taking continuing action on overspending. This course will develop the participants' skills in: Being aware of costs at all times Seeking cost reduction from the start (including life-cycle costing) Appraising projects / production to identify and take out risk Understanding real budgeting Using techniques such as ZBB and ABC where appropriate Ensuring cost reports lead to action Managing a cost reduction process that delivers Benefits to the organisation will include: Identification of cost reduction and business improvement opportunities Better reporting and ownership of costs Greater awareness and control of everyday costs 1 Introduction - the cost management process The risks of poor cost control Capital and revenue costs The importance of cost awareness The importance of cost reduction Cost management - the key aspects How to build a cost management and control process checklist for your areas of responsibility 2 Cost removal - taking out costs Cost awareness Costs of poor design / poor processes Value engineering Removing redundant costs 3 The need for commercial, technical and financial appraisals Understand the problems before cash is committed and costs incurred Making the effort to identify commercial and technical risk The time value of money - DCF techniques for long term projects Cost models for production processes and projects Costing models - project appraisals The use of spreadsheets to identify sensitivity and risk How to focus on risk management 4 Budgeting - proper budgeting challenges costs The philosophy of the business - are costs an issue? The importance of having the right culture The need for detailed business objectives Budgetary control measures Designing budget reports - for action 5 Zero-based budgeting (ZBB) - the principles Much more than starting with a clean sheet of paper What ZBB can achieve The concept of decision packages - to challenge business methods and costs Only necessary costs should be incurred A review of an operating budget - demonstrating what ZBB challenges and the costs it may lead to being taken out 6 Awareness of overheads and other costs Definitions of cost - direct and indirect Dealing with overheads - what is meant by allocation, absorption or apportionment? The apparent and real problems with overheads Different ways of dealing with overheads Review of overhead allocation methods and accounting and reporting issues 7 Overheads and product costing Activity-based costing (ABC) - the principles Where and how the ABC approach may be helpful Know the 'true' cost of a product or a project Should you be in business? Will you stay in business? Identifying weaknesses in a traditional overhead allocation How ABC will help improve product or service costing Identifying which products and activities should be developed and which abandoned 8 Cost reduction culture The need for cost reports What measures can be used to identify over-spends as early as possible Cost control performance measures and ratios 9 Design of cost control reports Reports should lead to action and deliver Selecting cost control measures which can be acted upon Practice in designing action reports 10 Course summary - developing your own cost action plan Group and individual action plans will be prepared with a view to participants identifying their cost risks areas and the techniques which can be immediately applied to improve costing and reduce costs

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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

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These events are designed to introduce the BOOK & basic ideas behind Understanding & Dealing with Everyday Racism The Six Stages Framework