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Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 2 Days 12 CPD hours From Playgrounds to protocols?discover, explore, and demonstrate how to use the fundamental building blocks of the Swift programming language. This 2-day, hands-on course teaches you the basic concepts of Swift programming, including syntax, logic, structures, functions, and patterns. It also includes detailed explanations of language syntax and coding exercises Introduction to Swift and Playgrounds Learn about the origin of Swift and some of its basic syntax Constants, Variables, and Data Types Learn how to define constants for values that don?t change and variables for values that do. Learn the data types that are included in Swift and how they can help you write better code Operators Learn about some of the operators in the Swift language, including basic math operators. Control Flow Learn how to use logical operators in Swift to check conditions; learn how to use control flow statements. Strings Learn how to create and store text using the string type. You'll learn a variety of string methods that allow you to compare two strings, access specific characters within a string, and insert and remove values Functions Learn how to declare functions with different parameters and return types Structures Learn how to create structures in Swift. Classes Learn what makes classes different from structures and when to use classes instead of structures. Also learn about inheritance, superclasses, and subclasses. Optionals Learn to use ?optionals? to properly handle situations when data may or may not exist Collections Learn about the various collection types available in Swift and how to choose the appropriate one for your program. Loops Learn how to create loops in Swift, control the conditions for looping, and specify when to stop. Type Casting Learn why some data can be expressed using only a broader type, and how you can test for specific kinds of data before using it. Guard Learn to use guard statements to better manage control flow. Scope Learn to write nicely structured code that's easy to read. You'll do this by properly scoping your constants and variables Enumerations Learn when enumerations are commonly used, how to define an enumeration, and how to work with enumerations using switch statements. Protocols Learn what protocols are, when to use them, and how to write your own. Learn how to enable objects to communicate with each other and how to extend protocols to provide shared functionality across multiple types Closures Learn about closures, how to define them, how to use them as function arguments, and how to use some of the common functions that take closures as arguments. Extensions Learn how to define an extension, as well as how and why to use extensions. Additional course details: Nexus Humans Introduction to Swift 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 Introduction to Swift 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 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 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. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 3 Days 18 CPD hours This course is intended for This certification is appropriate for anyone interested in becoming familiar with the concepts and practices of Agile Project Delivery, and who wants to have a working knowledge of the various Agile methodologies. Overview Objectives are: To provide an understanding of Agile philosophy and practices To provide a working knowledge of various Agile methodologies To provide students with the ability to compare and choose which methodology is appropriate in a given situation To prepare participants to pass the SAMC™ exam Course outcomes: Participants will be familiar with the concepts and practices of Agile project delivery. Participants will be equipped with detailed knowledge and understanding of available Agile methodologies. Participants will be able to compare and choose the methodologies or parts of methodologies that are most relevant to their current and future situations. Participants will be armed with the proper tools to take the lead in Agile projects and to address and resolve Agile issues in their organizations. Participants will be SAMC certified. Agile relies on adaptive planning and iterative development and delivery. It focuses primarily on the value of people in getting the job done effectively.Successful candidates will be awarded the SCRUMstudy Agile Master Certified (SAMC?) certification by SCRUMstudy after passing the included certification exam. The certification exam voucher is included in this course so you can take the exam at your convenience. IntroductionAgile Overview Agile Defined Why Use Agile? Adaptive Project Management The Agile Manifesto Principles of the Agile Manifesto Declaration of Interdependence Difference between Waterfall and Agile Domains of Agile Practices Value-Driven Delivery Stakeholder Engagement Team Performance Practices Adaptive Planning Problem Detection and Resolution Continuous Improvement Agile Tools and Artifacts Lean Kanban Software Development Introduction Core Values Practices Understanding Lean Software Development Understanding Kanban Software Development Scrum Overview of Scrum Brief History of Scrum Why Use Scrum? Scalability of Scrum Scrum Principles Scrum Aspects Scrum Processes Scrum and Kanban Extreme Programming (XP) Introduction Core Values? Roles Practices XP Artifacts XP Events XP Release Adopting XP Test-Driven Development (TDD) Introduction The Process Dynamic Systems Development Methods (DSDM) Introduction Core Values Roles Practices Crystal Introduction Core Values Roles Practices The Process Feature Driven Development (FDD) Introduction Core Values Roles Practices The Process Comparison of Agile MethodsBest Fit Analysis ToolBlitz PlanningNote SCRUMstudy has authored the SBOK? Guide as a comprehensive guide to deliver successful projects using Scrum. SCRUMstudy works through its large global partner network of Authorized Training Providers (A.T.P.s) to deliver trainings and certifications. New Horizons is a proud Authorized Training Provider of SCRUMstudy. Additional course details: Nexus Humans SCRUMstudy Agile Master Certified (SAMC) 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 SCRUMstudy Agile Master Certified (SAMC) 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 1 Days 6 CPD hours This course is intended for This course is intended for the following participants:Cloud professionals interested in taking the Data Engineer certification exam.Data engineering professionals interested in taking the Data Engineer certification exam. Overview This course teaches participants the following skills: Position the Professional Data Engineer Certification Provide information, tips, and advice on taking the exam Review the sample case studies Review each section of the exam covering highest-level concepts sufficient to build confidence in what is known by the candidate and indicate skill gaps/areas of study if not known by the candidate Connect candidates to appropriate target learning This course will help prospective candidates plan their preparation for the Professional Data Engineer exam. The session will cover the structure and format of the examination, as well as its relationship to other Google Cloud certifications. Through lectures, quizzes, and discussions, candidates will familiarize themselves with the domain covered by the examination, to help them devise a preparation strategy. Rehearse useful skills including exam question reasoning and case comprehension. Tips and review of topics from the Data Engineering curriculum. Understanding the Professional Data Engineer Certification Position the Professional Data Engineer certification among the offerings Distinguish between Associate and Professional Provide guidance between Professional Data Engineer and Associate Cloud Engineer Describe how the exam is administered and the exam rules Provide general advice about taking the exam Sample Case Studies for the Professional Data Engineer Exam Flowlogistic MJTelco Designing and Building (Review and preparation tips) Designing data processing systems Designing flexible data representations Designing data pipelines Designing data processing infrastructure Build and maintain data structures and databases Building and maintaining flexible data representations Building and maintaining pipelines Building and maintaining processing infrastructure Analyzing and Modeling (Review and preparation tips) Analyze data and enable machine learning Analyzing data Machine learning Machine learning model deployment Model business processes for analysis and optimization Mapping business requirements to data representations Optimizing data representations, data infrastructure performance and cost Reliability, Policy, and Security (Review and preparation tips) Design for reliability Performing quality control Assessing, troubleshooting, and improving data representation and data processing infrastructure Recovering data Visualize data and advocate policy Building (or selecting) data visualization and reporting tools Advocating policies and publishing data and reports Design for security and compliance Designing secure data infrastructure and processes Designing for legal compliance Resources and next steps Resources for learning more about designing data processing systems, data structures, and databases Resources for learning more about data analysis, machine learning, business process analysis, and optimization Resources for learning more about data visualization and policy Resources for learning more about reliability design Resources for learning more about business process analysis and optimization Resources for learning more about reliability, policies, security, and compliance Additional course details: Nexus Humans Preparing for the Professional Data Engineer Examination 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 Preparing for the Professional Data Engineer Examination 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 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 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. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 3 Days 18 CPD hours This course is intended for This class is intended for the following participants: Cloud architects, administrators, and SysOps/DevOps personnel Individuals using Google Cloud Platform to create new solutions or to integrate existing systems, application environments, and infrastructure with the Google Cloud Platform. Overview This course teaches participants the following skills: Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Platform Understand how pod networking works in Kubernetes Engine Create and manage Kubernetes Engine clusters using the GCP Console and gcloud/ kubectl commands Launch, roll back and expose jobs in Kubernetes Manage access control using Kubernetes RBAC and Google Cloud IAM Managing pod security policies and network policies Using Secrets and ConfigMaps to isolate security credentials and configuration artifacts Understand GCP choices for managed storage services Monitor applications running in Kubernetes Engine This class introduces participants to deploying and managing containerized applications on Google Kubernetes Engine (GKE) and the other services provided by Google Cloud Platform. Through a combination of presentations, demos, and hands-on labs, participants explore and deploy solution elements, including infrastructure components such as pods, containers, deployments, and services; as well as networks and application services. This course also covers deploying practical solutions including security and access management, resource management, and resource monitoring. Introduction to Google Cloud Platform Use the Google Cloud Platform Console Use Cloud Shell Define cloud computing Identify GCPs compute services Understand regions and zones Understand the cloud resource hierarchy Administer your GCP resources Containers and Kubernetes in GCP Create a container using Cloud Build Store a container in Container Registry Understand the relationship between Kubernetes and Google Kubernetes Engine (GKE) Understand how to choose among GCP compute platforms Kubernetes Architecture Understand the architecture of Kubernetes: pods, namespaces Understand the control-plane components of Kubernetes Create container images using Google Cloud Build Store container images in Google Container Registry Create a Kubernetes Engine cluster Kubernetes Operations Work with the kubectl command Inspect the cluster and Pods View a Pods console output Sign in to a Pod interactively Deployments, Jobs, and Scaling Create and use Deployments Create and run Jobs and CronJobs Scale clusters manually and automatically Configure Node and Pod affinity Get software into your cluster with Helm charts and Kubernetes Marketplace GKE Networking Create Services to expose applications that are running within Pods Use load balancers to expose Services to external clients Create Ingress resources for HTTP(S) load balancing Leverage container-native load balancing to improve Pod load balancing Define Kubernetes network policies to allow and block traffic to pods Persistent Data and Storage Use Secrets to isolate security credentials Use ConfigMaps to isolate configuration artifacts Push out and roll back updates to Secrets and ConfigMaps Configure Persistent Storage Volumes for Kubernetes Pods Use StatefulSets to ensure that claims on persistent storage volumes persist across restarts Access Control and Security in Kubernetes and Kubernetes Engine Understand Kubernetes authentication and authorization Define Kubernetes RBAC roles and role bindings for accessing resources in namespaces Define Kubernetes RBAC cluster roles and cluster role bindings for accessing cluster-scoped resources Define Kubernetes pod security policies Understand the structure of GCP IAM Define IAM roles and policies for Kubernetes Engine cluster administration Logging and Monitoring Use Stackdriver to monitor and manage availability and performance Locate and inspect Kubernetes logs Create probes for wellness checks on live applications Using GCP Managed Storage Services from Kubernetes Applications Understand pros and cons for using a managed storage service versus self-managed containerized storage Enable applications running in GKE to access GCP storage services Understand use cases for Cloud Storage, Cloud SQL, Cloud Spanner, Cloud Bigtable, Cloud Firestore, and Bigquery from within a Kubernetes application

Duration 2 Days 12 CPD hours This course is intended for It is appropriate for Managers, Executives, Project Managers, Business Analysts, Business and IT stakeholders working with analysts, Quality and process engineers, technicians, managers; supervisors, team leaders, and process operators. Overview Describe business process improvement (BPI) business drivers.Plan, manage and close requirements for a Business Process Improvement project Understand the essential elements of a successful BPI initiative.Identify candidate business processes for improvement.Understand the essential elements of a successful BPI initiative.Identify candidate business processes for improvement.Apply a methodology to business process improvement projects. This 2-day course aims at introducing its attendees to the core values, principles, and practices of Business Process Improvement. Introduction - A Business Process Improvement (BPI) Overview Why are we here today? What is BPI? Benefits of BPI Specific challenges/obstacles and successes Process improvement examples: Industry specific examples Famous debacles to avoid and successes to emulate Your role in helping to identify problems Overview of the Joiner 7-Step Method What is the Joiner 7-Step Method? Walkthrough of the Joiner 7-Step Method Template: Introduce and review Process Improvement Template Case Study Exercise: Read and discuss introduction to the Case Study Step #1: Initiate the Project Types of business problems typically encountered at insurance companies and banks How to recognize a business-related problem Identifying the gaps (delta between current and future states) Ownership of the project and the business problem Defining measurable success criteria Case Study Exercise: Complete the Problem Statement section (Step #1) of the template Step #2: Define Current Situation What are symptoms of a problem? Looking for symptoms of the problem Performing Stakeholder Analysis Technique: View a RACI Matrix Defining the impacts caused by the problem Technique: Business Process Modeling (As-Is) Understand how to draw an As-Is Business Process Model Case Study Exercise: Complete the Define Current Situation section (Step #2) of the template Step #3: Identify Root Causes What are root causes? Performing Root Cause Analysis Technique: Fishbone Diagram using the cafeteria example Case Study Exercise: Discuss a Fishbone Diagram Technique: Pareto Chart (discuss and show example) Case Study Exercise: Complete the Identify Root Causes section (Step #3) of the template Step #4: Develop Solutions Identifying options for problem resolution Avoid jumping to conclusions Technique: Brainstorming Case Study Exercise: Conduct a Brainstorming Session Recognizing pros and cons for each option Technique: Kempner-Tregoe (?Must-Have? vs. ?Nice-to-Have?) Case Study Exercise: Determine best solution using a ?simple? Kempner-Tregoe model Case Study Exercise: Complete the Develop Solutions section (Step #4) of the template Step #5: Define Measurable Results Prototyping the solution Technique: Business Process Modeling (To-Be) Measuring results against the success criteria (Step #1) Case Study Exercise: Review changes to an As-Is Business Process Model Case Study Exercise: Complete the Define Measurable Results section (Step #5) of the template Step #6: Standardize Process Defining how the process will be documented Plan and understand organizational readiness Discuss how employees are empowered to identify and act upon their ideas Identifying follow-up needs (i.e., training) for the staff that will be impacted Technique: Communication Plan Case Study Exercise: Complete the Standardize Process section (Step #6) of the template Step #7: Determine Future Plans Monitoring the process for Continuous Process Improvement (The ?Plan-Do-Check-Act? Cycle) Understand how to sustain the improvements made by the Joiner 7-Step Method Technique: PDCA form Case Study Exercise: Complete the Determine Future Plans section (Step #7) of the template Going Forward with a Plan of Action Identifying process problems in your organization Individual Exercise: Name three (3) possible areas for improvement Prioritize and define the next steps Individual Exercise: Using a new template complete Step 2 & Step 3 for one possible area for improvement you have identified

Duration 3 Days 18 CPD hours This course is intended for Network Security Operations Workload Application Administrators Security Operations Field Engineers Network Engineers Systems Engineers Technical Solutions Architects Cisco Integrators and Partners Overview After taking this course, you should be able to: Define the Cisco telemetry and analytics approach. Explore common scenarios that Cisco Tetration Analytics can solve. Describe how the Cisco Tetration Analytics platform collects telemetry and other context information. Discuss how relative agents are installed and configured. Explore the operational aspects of the Cisco Tetration Analytics platform. Describe the Cisco Tetration Analytics support for application visibility or application insight based on the Application Dependency Mapping (ADM) feature. List the concepts of the intent-based declarative network management automation model. Describe the Cisco Tetration policy enforcement pipeline, components, functions, and implementation of application policy. Describe how to use Cisco Tetration Analytics for workload protection in order to provide a secure infrastructure for business-critical applications and data. Describe Cisco Tetration Analytics platform use cases in the modern heterogeneous, multicloud data center. List the options for the Cisco Tetration Analytics platform enhancements. Explain how to perform the Cisco Tetration Analytics administration. This course teaches how to deploy, use, and operate Cisco© Tetration Analytics? platform for comprehensive workload-protection and application and network insights across a multicloud infrastructure. You will learn how the Cisco Tetration Analytics platform uses streaming telemetry, behavioral analysis, unsupervised machine learning, analytical intelligence, and big data analytics to deliver pervasive visibility, automated intent-based policy, workload protection, and performance management. Exploring Cisco Tetration Data Center Challenges Define and Position Cisco Tetration Cisco Tetration Features Cisco Tetration Architecture Cisco Tetration Deployment Models Cisco Tetration GUI Overview Implementing and Operating Cisco Tetration Explore Data Collection Install the Software Agent Install the Hardware Agent Import Context Data Describe Cisco Tetration Operational Concepts Examining Cisco Tetration ADM and Application Insight Describe Cisco Tetration Application Insight Perform ADM Interpret ADM Results Application Visibility Examining Cisco Tetration Intent-Based Networking Describe Intent-Based Policy Examine Policy Features Implement Policies Enforcing Tetration Policy Pipeline and Compliance Examine Policy Enforcement Implement Application Policy Examine Policy Compliance Verification and Simulation Examining Tetration Security Use Cases Examine Workload Security Attack Prevention Attack Detection Attack Remediation Examining IT Operations Use Cases Key Features and IT Operations Use Cases Performing Operations in Neighborhood App-based Use Cases Examining Platform Enhancement Use Cases Integrations and Advanced Features Third-party Integration Examples Explore Data Platform Capabilities Exploring Cisco Tetration Analytics Administration Examine User Authentication and Authorization Examine Cluster Management Configure Alerts and Syslog Additional course details: Nexus Humans Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 Cisco Implementing Cisco Tetration Analytics v1.0 (DCITET) 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 Experienced system administrators, system engineers, and system integrators Overview By the end of the course, you should be able to meet the following objectives: Configure and manage vSphere networking and storage for a large and sophisticated enterprise Use VMware vSphere Client™ to manage certificates Use Identity Federation to configure VMware vCenter Server to use external identity sources Use VMware vSphere Trust Authority™ to secure the infrastructure for encrypted VMs Use host profiles to manage VMware ESXi™ host compliance Create and manage a content library for deploying virtual machines Manage VM resource usage with resource pools Monitor and analyze key performance indicators for compute, storage, and networking resources for ESXi hosts Optimize the performance in the vSphere environment, including vCenter Server This five-day course teaches you advanced skills for configuring and maintaining a highly available and scalable virtual infrastructure. Through a mix of lecture and hands-on labs, you configure and optimize the VMware vSphere© 7 features that build a foundation for a truly scalable infrastructure, and you discuss when and where these features have the greatest effect. Attend this course to deepen your understanding of vSphere and learn how its advanced features and controls can benefit your organization. As an exclusive benefit, those who participate in this course will receive additional premium recorded lecture material on vSphere security. Course Introduction Introductions and course logistics Course objectives Network Scalability Configure and manage vSphere distributed switches Describe how VMware vSphere© Network I/O Control enhances performance Explain distributed switch features such as port mirroring and NetFlow Storage Scalability Explain why VMware vSphere© VMFS is a high-performance, scalable file system Explain VMware vSphere© Storage APIs - Array Integration, VMware vSphere© API for Storage Awareness?, and vSphere APIs for I/O filtering Configure and assign virtual machine storage policies Create VMware vSAN? storage policies Recognize components of the VMware vSphere© Virtual Volumes? architecture Configure VMware vSphere© Storage DRS? and VMware vSphere© Storage I/O Control Host and Management Scalability Use the vSphere Client to manage vSphere certificates Describe identity federation and recognize its use cases Configure identity federation to allow vCenter Server to use external identity provider Describe the benefits and use cases of vSphere Trust Authority Configure vSphere Trust Authority Use host profiles to manage ESXi configuration compliance Create a local content library and subscribe to a published content library Deploy VMs from a content library Create and manage resource pools in a cluster Describe how scalable shares work CPU Optimization Explain the CPU scheduler operation and other features that affect CPU performance Explain NUMA and vNUMA support Use esxtop to monitor key CPU performance metrics Memory Optimization Explain ballooning, memory compression, transparent page sharing, and host-swapping techniques for memory reclamation when memory is overcommitted Use esxtop to monitor key memory performance metrics Storage Optimization Describe storage queue types and other factors that affect storage performance Discuss vSphere support for NVMe and iSER technologies Use esxtop to monitor key storage performance metrics Network Optimization Explain performance features of network adapters Explain the performance features of vSphere networking Use esxtop to monitor key network performance metrics vCenter Server Performance Optimization Describe the factors that influence vCenter Server performance Use VMware vCenter© Server Appliance? tools to monitor resource use Supplemental Content Appendix A: vSphere Auto Deploy Explain the purpose of VMware vSphere© ESXi ? Image Builder CLI Explain the purpose of VMware vSphere© Auto Deploy? Describe how an autodeployed ESXi host boots Configure a vSphere Auto Deploy environment Appendix B: vSphere Security Configure ESXi Host Access and Authentication Recognize strategies for securing vSphere components, such as vCenter Server, ESXi hosts, and virtual machines Describe vSphere support for security standards and protocols Describe virtual machine security features Describe the components of a VM encryption architecture Create, manage, and migrate encrypted VMs Encrypt core dumps List VM encryption events and alarms