8699 Art courses in Southwick delivered Online

Duration 2 Days 12 CPD hours This course is intended for System architects, system administrators, IT managers, VMware partners, and individuals responsible for implementing and managing vSphere architectures who want to deploy vSphere 8.0 into their existing vSphere environment. Overview By the end of the course, you should be able to meet the following objectives: Recognize the importance of key features and enhancements in vSphere 8.0 Describe vCenter Server, VMware ESXi, storage, virtual machine, and security enhancements in vSphere 8.0 Describe the purpose of vSphere Distributed Services Engine Update an ESXi host equipped with a Data Processing Unit (DPU) using vSphere Lifecycle Manager Identify devices supported for system storage on ESXi 8.0 Recognize enhancements to VM hardware compatibility settings VMware vSphere Memory Monitoring and Remediation and the improvements to vSphere DRS Recognize the new Virtual Non-Uniform Memory Access (vNUMA) topology settings of a VM in vSphere Client Use vSphere Lifecycle Manager and Auto Deploy to manage the configuration specifications for the hosts in a cluster Recognize the vSphere Lifecycle Manager and Auto Deploy enhancements in vSphere 8.0 Recognize the cloud benefits that VMware vSphere+ brings to on-premises workloads Recognize technology that is discontinued or deprecated in vSphere 8.0 In this two-day course, you explore the new features and enhancements following VMware vCenter Server 8.0 and VMware ESXi 8.0. Real-world use-case scenarios, hands-on lab exercises, and lectures teach you the skills that you need to effectively implement and configure VMware vSphere 8.0. Course Introduction Introductions and course logistics Course objectives Artificial Intelligence and Machine Learning Describe how device groups support AI and ML in vSphere 8 Describe how device virtualization extensions support AI and ML in vSphere 8 vSphere Distributed Services Engine Describe the benefits of Distributed Services Engine Explain how Distributed Services Engine works Recognize use cases for Distributed Services Engine Install ESXi on a host equipped with a DPU View DPU information in vSphere Client Add an ESXi host equipped with a DPU to a cluster Update an ESXi host equipped with a DPU using vSphere Lifecycle Manager Create a vSphere Distributed Switch for network offloads Add a host with a DPU to the vSphere Distributed Switch Configure a VM to use Uniform Passthrough Mode vSphere and vCenter Management Review the improvements to the communication between vCenter and ESXi hosts Review the enhancements to the vCenter recovery process ESXi Enhancements Describe the function of the central configuration store in ESXi Explain how ConfigStore affects your interaction with ESXi configuration files Recognize the supported system storage partition configuration on ESXi 8.0 Identify devices supported for system storage on ESXi 8.0 Configure an RDMA host local device on ESXi vSphere Storage Describe the vSAN Express Storage Architecture Recognize the benefits of using vSAN Express Storage Architecture Describe the benefits of using NVMe Recognize the support for NVMe devices in vSphere Guest OS and Workloads Review the enhancements of the latest virtual hardware versions Describe the features introduced with virtual hardware version 20 Create a snapshot of a VM with an NVDIMM device Resource Management View energy and carbon emission metrics in vRealize Operations Manager Describe the VMware vSphere Memory Monitoring and Remediation (vMMR) functionality Describe how vMMR enhances the performance of vSphere DRS Security and Compliance Describe how to handle vTPM secrets when cloning a VM Manage OVF templates for VMs that are configured with vTPM Deploy an OVF template with vTPM Describe the enhancements to trusted binary enforcement in ESXi Describe ESXi 8 enhanced security features vSphere Lifecycle Manager Describe the enhancements to life cycle management of standalone ESXi hosts Manage the configuration profiles of ESXi hosts in a cluster with vSphere Lifecycle Manager Use Auto Deploy to boot a host with the desired image and configuration specifications Upgrade multiple ESXi hosts in a cluster in parallel Stage an ESXi host image prior to remediation Auto Deploy Manage custom host certificates using Auto Deploy vSphere with Tanzu Describe the features of the Tanzu Kubernetes Grid 2.0 offering Announcing vSphere+ Describe the functionality and benefits of vSphere+

Duration 4 Days 24 CPD hours This course is intended for This class is designed for enterprise architects, solution architects, and business analysts working to earn their Salesforce Application Architect credential, or for application architects looking to get more hands-on experience. Overview Design data models that scale gracefully Leverage Salesforce sharing mechanisms at an advanced level Evaluate the nuances of field types and pick the right one for the circumstances Make data model decisions that minimize record locking and other performance degradations Dive into the two cornerstone domains of being an Application Architect: Data Architecture and Management, and Sharing and Visibility. In this 4-day workshop, our Architect experts will present you with a case study scenario that will be broken down and digested through iterative exploration. Learn how to design and build secure, scalable, and high-performing solutions through a combination of lecture, demos, hands-on exercises, and workshop presentations and discussions. Application Architect Overview Assess the Requirements to Become an Application Architect Understand the Real-World Expectations of Application Architects Review the Core Characteristics of Successful Application Architects Scenario Introduction Review the Application Architecture Scenario Identify Scenario Problem Areas Identify Scenario Actors and Licensing Architecture Documentation Understand Key Architecture Documentation Requirements Learn About Best Practices for Artifact Documentation Produce Architecture Documents Data Modeling Identify Relationship Types and Their Impact on Record Access, User Interface, and Reporting Review the Considerations for Changing Field Types Review the Considerations for Modifying Data Models with Schema Builder Review the Considerations for Importing and Exporting Data Identify Use Cases of External Objects Determine an Appropriate Data Model Understand Design Implications with Complex Environments and Large Data Volumes (LDV) Data Management Review the Considerations for Working with LDV Review Data Lifecycle Concepts and Mechanisms Review Master Data Management and System of Record Concepts Review Data Migration, Planning, Preparation, and Execution Identify Potential LDV and Calculate Expected Volumes Go Further with Indexes Standard and Custom Skinny Tables Lock Records Security Model Review the Considerations for Working with Internal and External Sharing Models Restrict and Extend Object and Field Access Determine Sharing Solutions Identify Record Sharing Mechanisms (Declarative, Programmatic, Implicit) Understand Teams Concepts (Account, Opportunity, Case) Understand Person Accounts and Its Implication on Sharing Encrypt Data Sharing in Communities Understand Community Security Mechanisms Secure Integration Endpoints Integrate and Specify Connected Apps and Named Credentials Advanced Security & Visibility Concepts Implement Security & Visibility Controls with Apex and Visualforce Review Territory Management and Its Implication on Data Management, Sharing, and Visibility Review Divisions and Its Implication on Data Management, Sharing, and Visibility Understand Security and Visibility Controls on 'Special' Objects Solution Design Determine When to Leverage Standard Products Functionality vs. Custom Build vs. AppExchange Understand Declarative and Programmatic Configuration Get to Know the Order of Execution Automate Business Processes Consider Reporting and Analytics Needs Consider How to Store and Access Content/Files Apply Solution Design Concepts to Real-World Problems and Scenarios Deployment & Integration Best Practices Review the Application Lifecycle Understand How Sandboxes Should Be Used Review Deployment Options Identify Integration Patterns Wrap-up Review a Practice Scenario Review What Was Covered Additional course details: Nexus Humans Salesforce Build Application Architect Expertise (ARC901) 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 Salesforce Build Application Architect Expertise (ARC901) 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 API developers. Overview Configure services in Cloud Manager for an on-premises installation of API Connect Create a catalog and Developer Portal Create consumer and provider organizations Create, test, and publish SOAP, REST, and GraphQL APIs Create message processing policies that transform API requests and responses Authorize client API requests with security definitions Enforce an OAuth flow with an OAuth 2.0 API security provider Perform advanced testing of APIs by using the Test tab and the Local Test Environment Define products and plans in API Manager Stage, publish, version, migrate, deprecate, and retire products and APIs Manage member roles and permissions in the Developer Portal Create an application and subscribe to a plan Review API analytics in the Developer Portal Review analytics dashboards and visualizations in API Manager Customize the Developer Portal This course teaches you how to configure a newly built API Connect 10 environment. You are taught how to configure a catalog with the gateway, portal, and analytics services and set up the environment for API development. You then define API interfaces according to the OpenAPI specification. You build SOAP and REST based APIs along with a GraphQL API. You assemble message processing policies and define client authorization schemes, such as OAuth 2.0, in the API definition. You verify the proper sequencing of policies in the assembly tester and further test your APIs in the new Test tab and Local Test Environment. After building and testing your APIs, you publish them and make them available on the Developer Portal. You manage all aspects of the provider organization in the API Manager user interface to create, publish, version, and retire API artifacts such as products, plans and APIs themselves. You also learn how to manage consumer organizations who use the APIs that are made available on the Developer Portal. You learn how to add members to the consumer organization that provides access to the APIs on the Developer Portal. You learn how the layout of the Developer Portal can be customized. Finally, you call the APIs on the secure gateway and you view the graphs and metrics of API usage. Course Outline Introduction to IBM API Connect V10 Exercise: Reviewing the API Connect development and runtime environments Managing catalogs and organizations Exercise: Managing catalogs and consumer organizations Defining APIs in API Manager Exercise: Defining an API that calls an existing SOAP service Defining a REST API in API Manager Exercise: Defining a REST API from a target service Assembling message processing policies Exercise: Assembling message processing policies Declaring client authorization requirements Creating an OAuth 2.0 provider Exercise: Implementing OAuth 2.0 security Testing and debugging APIs Exercise: Introduction to the Test tab Creating and testing a GraphQL API Exercise: Creating and testing a GraphQL API Testing an API in the Local Test Environment Exercise: Testing an API in the Local Test Environment Publishing and managing products and APIs Exercise: Define and publish an API product The product lifecycle Exercise: Subscribing and testing APIs in the Developer Portal Exercise: Managing and approving API Products Subscribing and testing APIs in the Developer Portal API Analytics Exercise: Calling an API on the gateway and monitoring API usage Customizing the Developer Portal Exercise: Customizing the Developer Portal Additional course details: Nexus Humans WD515G Create, Secure, and Publish APIs with IBM API Connect 10 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 WD515G Create, Secure, and Publish APIs with IBM API Connect 10 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 intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with solid Python skills who wish to learn and use basic machine learning algorithms and concepts Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below Getting Started & Optional Python Quick Refresher Statistics and Probability Refresher and Python Practice Probability Density Function; Probability Mass Function; Naive Bayes Predictive Models Machine Learning with Python Recommender Systems KNN and PCA Reinforcement Learning Dealing with Real-World Data Experimental Design / ML in the Real World Time Permitting: Deep Learning and Neural Networks Machine Learning Essentials with Python is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern machine learning practices. This course is geared for attendees experienced with Python, but new to machine learning, who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use casesWorking in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:Popular machine learning algorithms, their applicability and limitationsPractical application of these methods in a machine learning environmentPractical use cases and limitations of algorithms Getting Started Installation: Getting Started and Overview LINUX jump start: Installing and Using Anaconda & Course Materials (or reference the default container) Python Refresher Introducing the Pandas, NumPy and Scikit-Learn Library Statistics and Probability Refresher and Python Practice Types of Data Mean, Median, Mode Using mean, median, and mode in Python Variation and Standard Deviation Probability Density Function; Probability Mass Function; Naive Bayes Common Data Distributions Percentiles and Moments A Crash Course in matplotlib Advanced Visualization with Seaborn Covariance and Correlation Conditional Probability Naive Bayes: Concepts Bayes? Theorem Naive Bayes Spam Classifier with Naive Bayes Predictive Models Linear Regression Polynomial Regression Multiple Regression, and Predicting Car Prices Logistic Regression Logistic Regression Machine Learning with Python Supervised vs. Unsupervised Learning, and Train/Test Using Train/Test to Prevent Overfitting Understanding a Confusion Matrix Measuring Classifiers (Precision, Recall, F1, AUC, ROC) K-Means Clustering K-Means: Clustering People Based on Age and Income Measuring Entropy LINUX: Installing GraphViz Decision Trees: Concepts Decision Trees: Predicting Hiring Decisions Ensemble Learning Support Vector Machines (SVM) Overview Using SVM to Cluster People using scikit-learn Recommender Systems User-Based Collaborative Filtering Item-Based Collaborative Filtering Finding Similar Movie Better Accuracy for Similar Movies Recommending movies to People Improving your recommendations KNN and PCA K-Nearest-Neighbors: Concepts Using KNN to Predict a Rating for a Movie Dimensionality Reduction; Principal Component Analysis (PCA) PCA with the Iris Data Set Reinforcement Learning Reinforcement Learning with Q-Learning and Gym Dealing with Real-World Data Bias / Variance Tradeoff K-Fold Cross-Validation Data Cleaning and Normalization Cleaning Web Log Data Normalizing Numerical Data Detecting Outliers Feature Engineering and the Curse of Dimensionality Imputation Techniques for Missing Data Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE Binning, Transforming, Encoding, Scaling, and Shuffling Experimental Design / ML in the Real World Deploying Models to Real-Time Systems A/B Testing Concepts T-Tests and P-Values Hands-on With T-Tests Determining How Long to Run an Experiment A/B Test Gotchas Capstone Project Group Project & Presentation or Review Deep Learning and Neural Networks Deep Learning Prerequisites The History of Artificial Neural Networks Deep Learning in the TensorFlow Playground Deep Learning Details Introducing TensorFlow Using TensorFlow Introducing Keras Using Keras to Predict Political Affiliations Convolutional Neural Networks (CNN?s) Using CNN?s for Handwriting Recognition Recurrent Neural Networks (RNN?s) Using an RNN for Sentiment Analysis Transfer Learning Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters Deep Learning Regularization with Dropout and Early Stopping The Ethics of Deep Learning Learning More about Deep Learning Additional course details: Nexus Humans Machine Learning Essentials with Python (TTML5506-P) 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 Machine Learning Essentials with Python (TTML5506-P) 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.

Developing the high performing team takes time and effort. But above all, it requires an understanding of the dynamics of high performing teams. This programme helps managers and leaders understand what high performing teams do and how they do it. It focuses on enabling managers to see their teams from different perspectives, allowing them to adapt their styles to maximise team outputs. A core theme is the need for managers of teams to 'hold up the mirror' to themselves and to see themselves as a leader of people, to reflect on how others see them and to modify their style accordingly. This programme will help managers / team leaders: Analyse the constituents of a 'high performing' team Apply essential influencing techniques Use a range of communication techniques to support effective teamwork Create and articulate team vision Generate common values Assess team effectiveness and take/recommend the appropriate actions Make more efficient use of team time Understand and agree on techniques to manage conflict Define and implement team meeting protocols that will facilitate team effectiveness Use the Prime Focus model to create the environment and framework for a high performing team Draft your team strategy to take them to the next level Day 1 1 Welcome and introduction Participants are welcomed to the programme and invited to share their personal objectives and people challenges Participants are given an action plan template to complete throughout the workshop 2 Your team The concept of 'positive intention' The difference between a team and a high performing team Assess your team effectiveness What is your 'interference'? 3 The team environment Setting the scene Building rapport Active listening Team goals and role profiling 4 Your style Tuckman model of team stages - how do you manage each stage? Team standards and goals Your team vision 5 Effective team meetings Influencing in team meetings How to make them interesting and relevant The pure role of the chair Day 2 1 Effective communication techniques Giving and receiving feedback Your communication style How to adapt, pace and lead to build rapport The Mehrabian theory of communication 2 How to manage conflict What is conflict? What is your default conflict approach? Tools and tips for managing conflict Practice sessions 3 Team skills Undertake a team skills analysis Types of team member Motivating team members Reframing situations 4 Setting your strategy Seeing the bigger picture The Prime Focus Model Your strategy for success Articulating your strategy Action plans revisited

Do you hear yourself saying the same thing over and over again? Do you want to bring some new skills to your role? If you have been in a sales role for some time it is easy to fall into a comfortable pattern. This workshop will help you incorporate advanced techniques drawn from NLP, behavioural science and social psychology into your existing skills. This course will help you: Use the consultative sales process to achieve more cross-sales Employ advanced rapport-building skills Assess the buying preferences of a customer Articulate the link between customer goals and needs Identify your customer's needs and wants Use advanced questioning techniques to gather information Resist the temptation to tell when it would be better to ask Identify communication preferences Given various scenarios, present a product to the explicit need of a customer Appreciate the impact of the language used during this stage of the sale and decide what language is appropriate with a variety of customers Handle objections positively Close the sale or gain commitment to further action 1 Introduction Aims and objectives of the training Personal introductions and objectives Self-assessment of existing sales skills Overview of content 2 Understanding yourself and your customers Personal communication style and what this means in a sales situation Wants versus needs What motivates people to buy Using social media tools such as LinkedIn Managing your portfolio to maximise sales Preparing to sell 3 The sales process Overview of the consultative sales process Review personal strengths and weaknesses as a salesperson Habits of top-performing sales people Common pitfalls Articulate sales goals 4 Building rapport 11 decisions that customers make in the first 9 seconds Spotting buyer communication preferences Building rapport with a wide variety of customers Dealing with emotions Keeping control 5 Questioning and listening Assumptions and how they trip us up Structured questioning Looking for cross-sales Honing your listening skills Identifying buyers' motivation Using summaries to move the customer forward 6 Presenting products and services to customers Choosing the right time to present Using features, advantages and benefits Tailoring your presentation of products and services to match buyer preferences and motivations 7 Gaining commitment When to close Dealing with difficult customers 5 things to avoid when handling a customer objection 8 Managing your business The link between service and sales Using customer surveys Winning back lost business 9 Putting it all together Skills practice Personal learning summary and action plans

Agility has become a prized business attribute. Although Agile methods were once most associated with software development, they are now applied in a host of different areas. Agile continues to find new applications because it is primarily an attitude. This programme delivers a solid grounding in both the Agile mindset and Agile methods. It covers three methods, illustrates the benefits of each and shows how they can be integrated. It includes practical techniques as well as background knowledge. By the end of the session, participants will be able to: Apply Agile concepts to self-manage their work Understand the roles people take on in Agile teams Use a variety of techniques to help deliver customer satisfaction Focus on delivering against priorities Employ a range of estimating techniques 1 Introduction Overview of the programme Review of participants' needs and objectives 2 The basics of Agile What makes Agile different Agile Manifesto and Principles Using feedback to deliver what is needed 3 Agile teams Multi-disciplinary teams Team size and empowerment Agile values 4 Agile at the team level - Scrum Scrum roles Scrum 'events' Scrum 'artifacts' 5 Agile for teams juggling multiple demands - Kanban Taking control of the work Improving throughput Dealing with bottlenecks 6 Agile in projects - AgilePM The phases of an Agile project Managing change requests Delivering on time 7 Estimating T-shirt / Pebble sizing Yesterday's weather Planning poker 8 Pick 'n' mix - some useful techniques The daily stand-up User stories Retrospectives Work-in-process limits Burndown charts Minimum viable product A / B testing 9 Review and action planning Identify actions to be implemented individually Conclusion

Software comes in a variety of guises - application software, firmware, middleware, system software. Increasingly, however, it doesn't necessarily present that way, especially as the boundaries between software, data and source code are becoming more and more blurred. And as software becomes more complex and more difficult to disentangle, so it becomes harder to manage and to value. But as it becomes more integral to every aspect of a business, so it is ever more important to keep on top of the technical, legal and commercial issues that arise, issues such as: To address these issues, organisations need a process for evaluating their current situation from all perspectives and for identifying the key actions they need to take to ensure holistic management of their software. This very practical programme will help set your organisation on the right path. Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. This programme is designed to give you a deeper understanding of: The technical, legal and commercial risks associated with software development, procurement, use and commercial exploitation The most appropriate processes and responsibilities for managing those risks Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. 1 Software business model What is the software business model? What options exist? Has the software business model been thoroughly reviewed to ensure its viability? This means fully understanding the market opportunity, the business environment and customer and end-user expectations. 2 Technology What are the technologies? How has the technology selection been validated considering the competitiveness, structure, and potential for future innovation? 3 UI and UX What is the UI and UX? How to best articulate this? Has the user interface and user experience been studied from both a subjective and objective view to give insight into customer behaviour? 4 Legal framework / commercial aspects Has the necessary legal framework or commercial aspects that may impact upon use or operation of the software been understood and risks identified and mitigated? 5 Software development What is the software development process? Are both the business management and development team's processes resilient in order to improve the company's capability and the maturity of the software? 6 Software quality What is quality? What are the metrics around software quality? What is the maturity level, based around a qualitative and quantitative assessment? 7 Intellectual property associated What IP should be considered when it comes to software? Does the company understand both the intellectual property risks and potential opportunities associated with this software? 8 Security What does software security mean in this context? How is it being addressed? 9 An holistic approach Review of roles and responsibilities to ensure appropriate management and protection

Learn the basics of Data Science, combining a supported #CISCO Skills for All online course with practical learning and a project to help consolidate the learning.