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Build successful and effective multi-cultural teams with our practical, bespoke training courses. Help team members to embrace and harness the skills and abilities their different ages, nationalities, generations and life experiences bring. Courses include: Knowing your team Communication styles Communicative competency in multi-cultural teams Cultural intelligence – understanding our strengths A global mindset Breaking down barriers for better team working Experiential learning – a session in a second language Team dynamics

This course will introduce you to the international sales negotiation process, outline the importance of pre-negotiation research, and explain why concession planning is essential to international sales negotiations. This course will introduce you to the international sales negotiation process, outline the importance of pre-negotiation research, and explain why concession planning is essential to international sales negotiations. This course will then explore how to conduct pre-negotiations research by assessing the factors influencing buying decisions, determining the reason for a buyer’s interest in your product, and analyzing competitors to inform your concession planning. Finally, the course will outline how culture influences international sales negotiation, and why all of the above are key components when constructing an international sales negotiation plan.

We've all sat through far more bad presentations than good ones, but knowing what 'good' looks like is easier than successfully replicating it. Sales presentations are a performance and, as salespeople, fluffing our lines can cost us a lot more than hurt pride. Having discovered and understood the specific needs and burning issues our prospect has, then this course will help any salesperson avoid dropping the ball and instead wowing their prospects with a high-impact, tailored and compelling case for purchase. This course will help participants: Prepare mentally and physically for stand-up presentations Use voice modulation and bullet-pointing to demand attention Avoid boring their prospects Master the do's and don'ts of PowerPoint Deal more effectively with technical hitches and prospect's interruptions Use eye contact and engagement to avoid prospects 'tuning out' Deploy best practice essentials for presenting with colleagues Steer through the toughest Q&A 1 Preparing your presentation Mindset Knowing your objective(s) Vocal warm-up techniques Assembling pre-agreed benefits Time management Room set-up Technical preparation 2 How to open your presentation Vocal energy Summary and agreement of prospect's needs How to have posture and confidence Use of humour What to do with those dreaded hands Confident v non-confident body language 3 How to get and keep people's attention Bullet pointing Linking benefits to specific, stated needs Practical exercise - formulating and delivering tailored benefits Being selective with features Third party reinforcement and case studies 'Watering the garden' eye contact technique Practical exercise - participants practise 'sharing out' eye contact to audience How to handle a prospect's negative body language Handling interruptions 4 Presenting in groups Credentialing all participants Role delineation for group presentations Edifying other participants' messages - do's and don'ts How to maintain energy when not speaking Practical exercise - good and bad practice when not speaking Teamwork in Q&A sessions How to hand over professionally 5 PowerPoint do's and don'ts Use of visual aids Good and bad PowerPoint slides How to make PowerPoint work for you Classic PowerPoint errors Avoiding and handling technical problems Good and bad flipchart practice 6 Closing and / or achieving next action steps Power of summary Good Q&A practice Handling objections Practical exercise - handling objections on one's feet Creating consensus among prospect panel What to do when prospects disagree with each other When to trial close How to close on next action steps 7 Wrap-up Key learning points from each participant Action steps to be implemented on next presentations

Fraud should not happen, but it does. It can happen at the highest to lowest levels in an organisation. Recent surveys show that incidents of fraud are not decreasing. Fraud costs companies money and, perhaps even more importantly, reputational damage. The losers are not just the shareholders, suppliers, customers, etc, but society as a whole. This programme shows why frauds happen, how organisations put themselves at risk and what they can do to prevent it. This programme will help directors and others understand: The motives for committing fraud Directors' responsibilities for identifying and reporting fraud What types of frauds there are How frauds are perpetrated How they can be prevented How regulators deal with fraud Above all, the principal objective of this programme is to help make your organisation as secure as possible from the threat of fraud. 1 Motives for committing fraud - drivers of fraud Session objective: to understand why people might commit fraud Drivers of fraudulent behaviourAmbitionGreedTheftConceit? And more! 2 Accounting mechanisms that allow fraud Session objective: to review the elements of the accounting, internal control and management processes that allow creative accounting Income or liability? Asset or expense? Coding errors and misclassification Netting off and grossing up Off-balance sheet items 3 Structures that allow fraud Session objective: to consider company and trading structures that allow frauds to be perpetrated Group structures Trading structures Tax havens Importing and exporting 4 Interpretations and other non-compliance that allow fraud Session objective: to look at how creative interpretations of law and accounting practice may permit fraud The place of accounting standards Accounting policies Trading methods The place of auditing standards 5 Money laundering Session objective: to review what constitutes money laundering Types of money laundering Identifying laundering Preventing laundering 6 Preventing fraud - proper management structures Session objective: to review the place of proper corporate governance Corporate governance Company management structure Audit committees The place of internal audit 7 Preventing fraud - proper accounting Session objective: to review best accounting and auditing practice Accounting standards Internal accounting policies Adequacy of internal controls Internal audit 8 Preventing fraud - regulation Session objective: to look at how regulators aim to prevent fraud The regulatory environment Financial services regulation 9 Conclusion Course review Open forum Close 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

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

Conflict is a word that conjures up many emotions. It is something that most people would prefer to avoid, if possible. Work can be an emotive place. Positive relationships can make your life at work exciting, motivating and challenging, whilst relationships that do not hold value to you could make your life very difficult and stressful, especially if there is conflict between you and your manager. This course is essential for people who want to understand where conflict can be used to positive effect and how to manage conflict in your working relationships and see it as something positive that can stimulate the environment. Research has shown that relationships at work are an extremely high motivational factor, and for a lot of people it has a higher importance that salary! Therefore, it is essential that we invest in relationships and search out new ways to make them better in order to have a more positive influence on our surroundings. By understanding why other people are in conflict we can manage the conversation a lot better, with outcomes managed more effectively so the 'conflict' will add value to the organisation. This participative event will cover a wide variety of exercises and personal stories, and leave course participants with a clear strategy to identify when they are in conflict with someone and how they will structure their approach to get to a satisfactory outcome. This is a workshop that targets anyone where conflict needs to be managed and cannot seem to resolve it, whether internally or externally. At the end of the day, participants will: Know their key relationships and the strength of those relationships Complete the Strengths Deployment Inventory (SDI) to identify where you deploy your strengths Understand what is important to you and your key stakeholders Know how motivational value systems can influence behaviour Tailor your communication style to match that of your opposite party Know conflict strategies to resolve conflict in others Learn to be more assertive when challenging Achieve key personal, departmental and organisational objectives 1 Where are you now? How effective are your current working relationships? Can I work effectively without the input from others? Who do you need to be a success? 2 The Strengths Deployment Inventory (SDI) Completion of the SDI questionnaire An understanding of the theory A 'trip around the triangle' Predicting relationship interaction Your scores and what they mean in your relationships 3 Conflict theory What is conflict? The 3 flags of conflict What are your conflict triggers? Your conflict scores plotted The conflict sequence 4 Conflict resolution strategies Early warning signs Most productive behaviours Least productive behaviours Preventable / unwarranted conflict Review of the dynamic triangle Review of the day, personal learning and action planning

Learn how to use Microsoft Project to create and resource robust project plans and how to maintain and track throughout the project lifecycle. Course overview Duration: 1 day (6.5 hours) Our Project Planning and Control course gives you the essential skills to use Microsoft Project to build, resource and monitor project schedules. It looks at initial setup, building plans, using a work breakdown structure and managing resources through to baselining and progressing your schedule. This course is designed for new or existing users of Microsoft Project, and no previous experience of Project is required. Knowledge of planning techniques would be an advantage. Objectives  By the end of the course you will be able to: Create project schedules Build a Work Breakdown Structure Create relationships Set baselines Manage resources Set deadlines and task properties Print and report on your project Update and track project schedules Content Creating a new project Project defaults Project start date Setting default hours per day/week Setting daily working times Project timeline Building a project Creating a work breakdown structure Adding tasks and durations Estimated durations Setting milestones Recurring tasks Linking, Baselining and Resourcing Setting start dates and dependencies Task Inspector Resourcing Assigning resources Filtering available resources Baseline Setting a baseline Removing a baseline Managing resources Resource properties Dealing with over allocations Tasking information Constraint dates Setting deadline Assigning task specific calendars Task types Updating your project Completing work Completing work per resource Updating tasks Updating the project Rescheduling work Change highlighting Printing and reporting Setup and Printing Visual reports Using the Timeline Creating Dashboard reports

Expand your Power BI knowledge and take your reports to the next level. Course overview Duration: 1 day (6.5 hours) This course is aimed at existing users who want to expand their skills to use advanced reporting techniques and use DAX to create calculated columns and measures. Participants should have either attended our Power BI – Introduction course or have equivalent knowledge. You should be able to import and transform data and create simple reports. Objectives  By the end of the course you will be able to: Import and connect data tables Create and use date calendars Create calculated columns Create and use measures Use drill down and drill through Create Tooltip pages Add and customise slicers Add action buttons Streamline your report for use in the Power BI Service Content Review of importing and loading data Importing data Transforming data Adding custom columns Creating data models Building visuals Creating date calendars Building date tables Creating Financial Year information Including Month and Day information Creating calculated columns Power Query custom columns vs DAX columns Creating DAX calculated columns Creating measures Implicit vs Explicit Measures Building measures Using DAX Common DAX functions Drill Down vs Drill Through Review of drill down Creating drill through pages Using drill through Creating ToolTips Pages Adding pages to use for Tooltips Linking ToolTip pages to visuals Using action buttons Adding images Adding buttons Setting actions Working with slicers Adding slicers Changing slicer settings Syncing slicers between pages Showing what has been sliced Setting slicer interactions Techniques in the Power BI Service Hiding the navigation bar Stopping users manually filtering

Use some of the hidden statistical analysis tools within Excel to build complex data models. Course overview Duration: 1 day (6.5 hours) This advanced Excel course looks at some of the statistical analysis tools available and gives examples of when they might be used. This course is aimed at advanced users of Excel who work with statistical data often and have a good knowledge of Maths. Objectives By the end of the course you will be able to: Use Goal Seek Create and view scenarios Use Forecast Sheet Create single and double input data tables Create models using the solver Install and use the Analysis Toolpak Create and use array formulas Use a range of advanced Financial and Statistical Excel functions Content Using what if analysis options Using Goal Seek Creating, saving and viewing scenarios Generating a Forecast sheet Solver Creating models Projecting scenarios with Solver Using data tables Creating single input data tables Creating double input data tables Projecting with data tables Financial functions PMT FV NPV Analysing data with Analysis ToolPak Installing the Analysis Toolpak Generating statistical analysis Visualising data using Histograms Array formulas Using embedded Excel Array formulas Create an Array formula Multi and single cell Array formula Using TRANSPOSE to flip rows or columns Use the FREQUENCY Function Use an array to count unique entries in a range Dynamic arrays Spilling data Using the new dynamic array functions Advance functions in formulas Statistical functions:MEAN, MEDIAN and MODERANKLARGE and SMALLMODPERCENTILE Use the AGGREGATE function to sum data in ranges with errors