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Workshop is designed to support participants in using the Six Stages Framework in Board development and Diversity, Equity and Inclusion

24-hour postural care CPD training ,focussing on supported lying for hands-on workforce.

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Neuro EMDR Workshop will teach people how to use EMDR with people who have cognitive problems. 6 CPD Credits from the UK EMDR Association have been applied for.

This session will provide a clear update for governors as to the latest developments in terms of risk, updates on statutory guidance and KCSIE requirements from the governance perspective, and an overview of what the safeguarding team should be working on.

This 3 Days programme will equip you to use, price, manage and evaluate interest rate and cross-currency derivatives. The course starts with the building blocks of money markets and futures, through yield curve building to interest-rate and cross-currency swaps, and applications. The approach is hands-on and learning is enhanced through many practical exercises covering hedging, valuation, and risk management. This course also includes sections on XVA, documentation and settlement. The programme includes extensive practical exercises using Excel spreadsheets for valuation and risk-management, which participants can take away for immediate implementation.

Duration 2 Days 12 CPD hours This course is intended for Business Analysts, Technical Managers, and Programmers Overview This intensive training course helps students learn the practical aspects of the R programming language. The course is supplemented by many hands-on labs which allow attendees to immediately apply their theoretical knowledge in practice. Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning. What is R ? What is R? ? Positioning of R in the Data Science Space ? The Legal Aspects ? Microsoft R Open ? R Integrated Development Environments ? Running R ? Running RStudio ? Getting Help ? General Notes on R Commands and Statements ? Assignment Operators ? R Core Data Structures ? Assignment Example ? R Objects and Workspace ? Printing Objects ? Arithmetic Operators ? Logical Operators ? System Date and Time ? Operations ? User-defined Functions ? Control Statements ? Conditional Execution ? Repetitive Execution ? Repetitive execution ? Built-in Functions ? Summary Introduction to Functional Programming with R ? What is Functional Programming (FP)? ? Terminology: Higher-Order Functions ? A Short List of Languages that Support FP ? Functional Programming in R ? Vector and Matrix Arithmetic ? Vector Arithmetic Example ? More Examples of FP in R ? Summary Managing Your Environment ? Getting and Setting the Working Directory ? Getting the List of Files in a Directory ? The R Home Directory ? Executing External R commands ? Loading External Scripts in RStudio ? Listing Objects in Workspace ? Removing Objects in Workspace ? Saving Your Workspace in R ? Saving Your Workspace in RStudio ? Saving Your Workspace in R GUI ? Loading Your Workspace ? Diverting Output to a File ? Batch (Unattended) Processing ? Controlling Global Options ? Summary R Type System and Structures ? The R Data Types ? System Date and Time ? Formatting Date and Time ? Using the mode() Function ? R Data Structures ? What is the Type of My Data Structure? ? Creating Vectors ? Logical Vectors ? Character Vectors ? Factorization ? Multi-Mode Vectors ? The Length of the Vector ? Getting Vector Elements ? Lists ? A List with Element Names ? Extracting List Elements ? Adding to a List ? Matrix Data Structure ? Creating Matrices ? Creating Matrices with cbind() and rbind() ? Working with Data Frames ? Matrices vs Data Frames ? A Data Frame Sample ? Creating a Data Frame ? Accessing Data Cells ? Getting Info About a Data Frame ? Selecting Columns in Data Frames ? Selecting Rows in Data Frames ? Getting a Subset of a Data Frame ? Sorting (ordering) Data in Data Frames by Attribute(s) ? Editing Data Frames ? The str() Function ? Type Conversion (Coercion) ? The summary() Function ? Checking an Object's Type ? Summary Extending R ? The Base R Packages ? Loading Packages ? What is the Difference between Package and Library? ? Extending R ? The CRAN Web Site ? Extending R in R GUI ? Extending R in RStudio ? Installing and Removing Packages from Command-Line ? Summary Read-Write and Import-Export Operations in R ? Reading Data from a File into a Vector ? Example of Reading Data from a File into A Vector ? Writing Data to a File ? Example of Writing Data to a File ? Reading Data into A Data Frame ? Writing CSV Files ? Importing Data into R ? Exporting Data from R ? Summary Statistical Computing Features in R ? Statistical Computing Features ? Descriptive Statistics ? Basic Statistical Functions ? Examples of Using Basic Statistical Functions ? Non-uniformity of a Probability Distribution ? Writing Your Own skew and kurtosis Functions ? Generating Normally Distributed Random Numbers ? Generating Uniformly Distributed Random Numbers ? Using the summary() Function ? Math Functions Used in Data Analysis ? Examples of Using Math Functions ? Correlations ? Correlation Example ? Testing Correlation Coefficient for Significance ? The cor.test() Function ? The cor.test() Example ? Regression Analysis ? Types of Regression ? Simple Linear Regression Model ? Least-Squares Method (LSM) ? LSM Assumptions ? Fitting Linear Regression Models in R ? Example of Using lm() ? Confidence Intervals for Model Parameters ? Example of Using lm() with a Data Frame ? Regression Models in Excel ? Multiple Regression Analysis ? Summary Data Manipulation and Transformation in R ? Applying Functions to Matrices and Data Frames ? The apply() Function ? Using apply() ? Using apply() with a User-Defined Function ? apply() Variants ? Using tapply() ? Adding a Column to a Data Frame ? Dropping A Column in a Data Frame ? The attach() and detach() Functions ? Sampling ? Using sample() for Generating Labels ? Set Operations ? Example of Using Set Operations ? The dplyr Package ? Object Masking (Shadowing) Considerations ? Getting More Information on dplyr in RStudio ? The search() or searchpaths() Functions ? Handling Large Data Sets in R with the data.table Package ? The fread() and fwrite() functions from the data.table Package ? Using the Data Table Structure ? Summary Data Visualization in R ? Data Visualization ? Data Visualization in R ? The ggplot2 Data Visualization Package ? Creating Bar Plots in R ? Creating Horizontal Bar Plots ? Using barplot() with Matrices ? Using barplot() with Matrices Example ? Customizing Plots ? Histograms in R ? Building Histograms with hist() ? Example of using hist() ? Pie Charts in R ? Examples of using pie() ? Generic X-Y Plotting ? Examples of the plot() function ? Dot Plots in R ? Saving Your Work ? Supported Export Options ? Plots in RStudio ? Saving a Plot as an Image ? Summary Using R Efficiently ? Object Memory Allocation Considerations ? Garbage Collection ? Finding Out About Loaded Packages ? Using the conflicts() Function ? Getting Information About the Object Source Package with the pryr Package ? Using the where() Function from the pryr Package ? Timing Your Code ? Timing Your Code with system.time() ? Timing Your Code with System.time() ? Sleeping a Program ? Handling Large Data Sets in R with the data.table Package ? Passing System-Level Parameters to R ? Summary Lab Exercises Lab 1 - Getting Started with R Lab 2 - Learning the R Type System and Structures Lab 3 - Read and Write Operations in R Lab 4 - Data Import and Export in R Lab 5 - k-Nearest Neighbors Algorithm Lab 6 - Creating Your Own Statistical Functions Lab 7 - Simple Linear Regression Lab 8 - Monte-Carlo Simulation (Method) Lab 9 - Data Processing with R Lab 10 - Using R Graphics Package Lab 11 - Using R Efficiently

A popular 6-week Mindfulness Meditation and Dynamic Breathing course that provides participants with tools to support them in an increasingly challenging world.

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Discover the powerful schedule and cost risk analysis features of PRA. Course overview Duration: 2 days (13 hours) Our Primavera Risk Analysis course gives a detailed introduction to the schedule and risk analysis features of Primavera Risk Analysis. It shows the powerful features of the tool and give hands on practice throughout the course to ensure you can confidentially put your new skills into practice back in the workplace. This course is designed for new users of Primavera Risk Analysis, and no previous experience is required. You should however be familiar with risk management processes and terminology. Objectives  By the end of the course you will be able to: Import schedules into PRA Add three point estimates onto plans Perform schedule and cost analysis Use templated quick risk Run risk analysis Interpret results from the Risk Histogram and Tornado graph Add task percentiles to a Gantt chart Set up a risk register Add qualitative and quantitative risks Link risk to activities in the plan Create reports Use the Distribution Analyser Content Importing schedules Importing MSP and Primavera Schedules Running import checks Checking schedule integrity Schedule risk analysis 3 point estimating Entering uncertainly Different distributions Using quick risk Updating plan Importing plans with 3 point estimates Cost/Resource uncertainty Resource loadings Creating 3 point cost estimates Resource distributions and escalations Simple cost estimates Templated quick risk Setting up and applying templated quick risk Assessing risk at WBS level Running risk analysis Running risk analysis Interpreting results on the Risk Histogram Setting analysis options Task percentiles Setting task percentile options Including task percentiles on the Gantt chart Tornado graph Creating a Tornado graph Viewing sensitivity Analysing sensitivity against activities Setting up the risk register Setting Schema levels Defining criteria and tolerances Setting up a Risk Breakdown Structure (RBS) Working with manageability and proximity Saving scoring matrices Adding custom fields Exporting data Exporting to Excel, Word and PowerPoint Qualitive risks Setting risk IDs Adding risk cause, description and effect Setting up risk details Entering mitigation actions Quantitative risks Linking risks to activities Adding schedule and cost impacts Defining how multiple risks impact Correlation Migrating your plan Adding mitigation actions to your plan as tasks Linking tasks to mitigation actions Actioning your risk register Progressing risks Importing progressed plans Linking register to progressed plans Risk history The Waterfall chart Saving and reporting Exporting the risk register Running reports Creating new reports Building and comparing risk plans Using the distribution analyser Comparing dates and cost