5440 Practitioner courses delivered Online

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Solutions architects, security DevOps, and security engineers Overview In this course, you will learn to: Establish a landing zone with AWS Control Tower Configure AWS Organizations to create a multi-account environment Implement identity management using AWS Single Sign-On users and groups Federate access using AWS SSO Enforce policies using prepackaged guardrails Centralize logging using AWS CloudTrail and AWS Config Enable cross-account security audits using AWS Identity and Access Management (IAM) Define workflows for provisioning accounts using AWS Service Catalog and AWS Security Hub Security is foundational to AWS. Governance at scale is a new concept for automating cloud governance that can help companies retire manual processes in account management, budget enforcement, and security and compliance. By automating common challenges, companies can scale without inhibiting agility, speed, or innovation. In addition, they can provide decision makers with the visibility, control, and governance necessary to protect sensitive data and systems.In this course, you will learn how to facilitate developer speed and agility, and incorporate preventive and detective controls. By the end of this course, you will be able to apply governance best practices. Course Introduction Instructor introduction Learning objectives Course structure and objectives Course logistics and agenda Module 1: Governance at Scale Governance at scale focal points Business and Technical Challenges Module 2: Governance Automation Multi-account strategies, guidance, and architecture Environments for agility and governance at scale Governance with AWS Control Tower Use cases for governance at scale Module 3: Preventive Controls Enterprise environment challenges for developers AWS Service Catalog Resource creation Workflows for provisioning accounts Preventive cost and security governance Self-service with existing IT service management (ITSM) tools Module 4: Detective Controls Operations aspect of governance at scale Resource monitoring Configuration rules for auditing Operational insights Remediation Clean up accounts Module 5: Resources Explore additional resources for security governance at scale Additional course details: Nexus Humans AWS Security Governance at Scale 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 AWS Security Governance at Scale 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 Typical candidates for this course are IT Professionals who deploy small-to-medium scale enterprise network solutions based on Aruba products and technologies Overview After you successfully complete this course, expect to be able to: Explain how Aruba's wireless networking solutions meet customers' requirements Explain fundamental WLAN technologies, RF concepts, and 802.11 Standards Learn to configure the Mobility Master and Mobility Controller to control access to the Employee and Guest WLAN Control secure access to the WLAN using Aruba Firewall Policies and Roles Recognize and explain Radio Frequency Bands and channels, and the standards used to regulate them Describe the concept of radio frequency coverage and interference and successful implementation and diagnosis of WLAN systems Identify and differentiate antenna technology options to ensure optimal coverage in various deployment scenarios Describe RF power technology including, signal strength, how it is measured and why it is critical in designing wireless networks Learn to configure and optimize Aruba ARM and Client Match features Learn how to perform network monitoring functions and troubleshooting AR-AMF teaches knowledge, skills & practical exp. to set up & config a basic AR WLAN utilizing OS 8.X architecture & features.using lecture & labs,AR-AMF provides tech. & hands-on exp. of config. a single Mobility Master with 1 controller & AP WLAN WLAN Fundamentals Describes the fundamentals of 802.11, RF frequencies and channels Explain RF Patterns and coverage including SNR Roaming Standards and QOS requirements Mobile First Architecture An introduction to Aruba Products including controller types and modes OS 8.X Architecture and features License types and distribution Mobility Master Mobility Controller Configuration Understanding Groups and Subgroups Different methods to join MC with MM Understanding Hierarchical Configuration Secure WLAN configuration Identifying WLAN requirements such as SSID name, encryption, authentication Explain AP groups structure and profiles Configuration of WLAN using the Mobility Master GUI AP Provisioning Describes the communication between AP and Mobility controller Explain the AP booting sequence and requirements Explores the APs controller discovery mechanisms Explains how to secure AP to controller communication using CPSec Describes AP provisioning and operations WLAN Security Describes the 802.11 discovery, authentication and association Explores the various authentication methods, 802.1x with WPA/WPA2, Mac auth Describes the authentication server communication Explains symmetric vs asymmetric Keys, encryption methods WIPS is described along with rogue discovery and protection Firewall Roles and Policies An introduction into Firewall Roles and policies Explains Aruba?s Identity based Firewall Configuration of Policies and Rules including aliases Explains how to assign Roles to users Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores the new OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better Aps Dynamic RF Management Explain how ARM calibrates the network selecting channels and power settings Explores the new OS 8.X Airmatch to calibrate the network How Client Match and Client Insight match steers clients to better Aps Guest Access Introduces Aruba?s solutions for Guest Access and the Captive portal process Configuration of secure guest access using the internal Captive portal The configuration of Captive portal using Clearpass and its benefits Creating a guest provisioning account Troubleshooting guest access Network Monitoring and Troubleshooting Using the MM dashboard to monitor and diagnose client, WLAN and AP issues Traffic analysis using APPrf with filtering capabilities A view of Airwaves capabilities for monitoring and diagnosing client, WLAN and AP issues

Duration 2 Days 12 CPD hours This course is intended for System administrators and consultants, application owners, and system architects Overview By the end of the course, you should be able to meet the following objectives: Describe VMware Carbon Black Cloud platform Describe data flows on VMware Carbon Black Cloud Create and edit a custom role in VMware Carbon Black Cloud Recognize the impact of a user role on a console user Describe the VMware Carbon Black Cloud sensor resource usage Explain sensor usage in VMware Carbon Black Cloud Identify configuration settings for endpoints in sensor policy settings Determine requirements for initial deployment of sensors Recognize the differences between attended and unattended sensor installation methods Identify the correct deployment strategy for a given scenario Recognize the deployment process for VMware Carbon Black Cloud Workload™ Identify eligible workloads in a VMware vSphere environment Describe VMware Carbon Black Cloud sensor deployment Manage VMware vSphere workloads Identify sensor status in RepCLI This two-day hands-on training course provides you with the knowledge, skills, and tools to achieve competency in planning and deploying VMware Carbon Black Cloud in your environment. This course explains the VMware Carbon Black Cloud components, managing users and roles in VMware Carbon Black Cloud, configuring policies to support sensor deployment and management, and presents methods for deploying sensors across endpoints and workloads. Course Introduction Introductions and course logistics Course objectives Introduction to VMware Carbon Black Cloud Describe the VMware Carbon Black Cloud platform Describe VMware Carbon Black Cloud operating systems requirements Identify interesting files according to VMware Carbon Black Cloud Identify events collected Describe data flows Managing VMware Carbon Black Cloud Roles and Users Describe the use of roles in VMware Carbon Black Cloud Describe RBAC capabilities Create and edit a custom role Manage new console users Recognize the impact of a user role on a console user Describe authentication mechanisms VMware Carbon Black Cloud Sensors Describe the VMware Carbon Black Cloud sensor resource usage List the supported operating systems for VMware Carbon Black Cloud sensors Explain sensor usage in VMware Carbon Black Cloud Preparing for Deployment Identify configuration settings for endpoints in sensor policy settings Organize sensors using sensor groups to assign the desired policy based on specific criteria Compare VDI sensor settings as compared to traditional endpoint sensor settings Determine requirements for the initial deployment of sensors Evaluate the policy impact on sensors Identify best practices for deploying sensors Installing Sensors Describe how to send an installation request Recognize the features and limitations of an installation code and company code Recognize the process for successfully completing an attended installation Recognize the differences between attended and unattended sensor installation methods Identify the correct deployment strategy for a given scenario Generate logs with unattended installations Generate sensor logs Check network connectivity for sensor installation Deploying Workloads Recognize the deployment process for VMware Carbon Black Cloud Workload Identify eligible workloads in a vSphere environment Recognize how to enable the VMware Carbon Black Cloud sensor on a VM workload Managing Sensors Describe VMware Carbon Black Cloud sensor deployment Explain the differences in sensor status Describe sensor update capabilities Explain sensor actions Manage vSphere workloads Post-deployment Validation Describe the process of a sensor background scan Recognize a properly registered sensor installation Identify sensor status in RepCLI Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Carbon Black Cloud: Plan and Deploy 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 VMware Carbon Black Cloud: Plan and Deploy 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 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.

From Curiosity to Clarity: An exciting introduction to NLP Ready to dive into the world of Neuro-Linguistic Programming (NLP)? Join us for an engaging event where you'll discover the power of NLP techniques in communication, personal development, and more. Whether you're a seasoned pro or just curious, this is the perfect opportunity to explore the wonders of NLP.

Duration 3 Days 18 CPD hours This course is intended for This in an intermediate and beyond-level course is geared for experienced Python developers looking to delve into the exciting field of Natural Language Processing. It is ideally suited for roles such as data analysts, data scientists, machine learning engineers, or anyone working with text data and seeking to extract valuable insights from it. If you're in a role where you're tasked with analyzing customer sentiment, building chatbots, or dealing with large volumes of text data, this course will provide you with practical, hands on skills that you can apply right away. Overview This course combines engaging instructor-led presentations and useful demonstrations with valuable hands-on labs and engaging group activities. Throughout the course you'll: Master the fundamentals of Natural Language Processing (NLP) and understand how it can help in making sense of text data for valuable insights. Develop the ability to transform raw text into a structured format that machines can understand and analyze. Discover how to collect data from the web and navigate through semi-structured data, opening up a wealth of data sources for your projects. Learn how to implement sentiment analysis and topic modeling to extract meaning from text data and identify trends. Gain proficiency in applying machine learning and deep learning techniques to text data for tasks such as classification and prediction. Learn to analyze text sentiment, train emotion detectors, and interpret the results, providing a way to gauge public opinion or understand customer feedback. The Hands-on Natural Language Processing (NLP) Boot Camp is an immersive, three-day course that serves as your guide to building machines that can read and interpret human language. NLP is a unique interdisciplinary field, blending computational linguistics with artificial intelligence to help machines understand, interpret, and generate human language. In an increasingly data-driven world, NLP skills provide a competitive edge, enabling the development of sophisticated projects such as voice assistants, text analyzers, chatbots, and so much more. Our comprehensive curriculum covers a broad spectrum of NLP topics. Beginning with an introduction to NLP and feature extraction, the course moves to the hands-on development of text classifiers, exploration of web scraping and APIs, before delving into topic modeling, vector representations, text manipulation, and sentiment analysis. Half of your time is dedicated to hands-on labs, where you'll experience the practical application of your knowledge, from creating pipelines and text classifiers to web scraping and analyzing sentiment. These labs serve as a microcosm of real-world scenarios, equipping you with the skills to efficiently process and analyze text data. Time permitting, you?ll also explore modern tools like Python libraries, the OpenAI GPT-3 API, and TensorFlow, using them in a series of engaging exercises. By the end of the course, you'll have a well-rounded understanding of NLP, and will leave equipped with the practical skills and insights that you can immediately put to use, helping your organization gain valuable insights from text data, streamline business processes, and improve user interactions with automated text-based systems. You?ll be able to process and analyze text data effectively, implement advanced text representations, apply machine learning algorithms for text data, and build simple chatbots. Launch into the Universe of Natural Language Processing The journey begins: Unravel the layers of NLP Navigating through the history of NLP Merging paths: Text Analytics and NLP Decoding language: Word Sense Disambiguation and Sentence Boundary Detection First steps towards an NLP Project Unleashing the Power of Feature Extraction Dive into the vast ocean of Data Types Purification process: Cleaning Text Data Excavating knowledge: Extracting features from Texts Drawing connections: Finding Text Similarity through Feature Extraction Engineer Your Text Classifier The new era of Machine Learning and Supervised Learning Architecting a Text Classifier Constructing efficient workflows: Building Pipelines for NLP Projects Ensuring continuity: Saving and Loading Models Master the Art of Web Scraping and API Usage Stepping into the digital world: Introduction to Web Scraping and APIs The great heist: Collecting Data by Scraping Web Pages Navigating through the maze of Semi-Structured Data Unearth Hidden Themes with Topic Modeling Embark on the path of Topic Discovery Decoding algorithms: Understanding Topic-Modeling Algorithms Dialing the right numbers: Key Input Parameters for LSA Topic Modeling Tackling complexity with Hierarchical Dirichlet Process (HDP) Delving Deep into Vector Representations The Geometry of Language: Introduction to Vectors in NLP Text Manipulation: Generation and Summarization Playing the creator: Generating Text with Markov Chains Distilling knowledge: Understanding Text Summarization and Key Input Parameters for TextRank Peering into the future: Recent Developments in Text Generation and Summarization Solving real-world problems: Addressing Challenges in Extractive Summarization Riding the Wave of Sentiment Analysis Unveiling emotions: Introduction to Sentiment Analysis Tools Demystifying the Textblob library Preparing the canvas: Understanding Data for Sentiment Analysis Training your own emotion detectors: Building Sentiment Models Optional: Capstone Project Apply the skills learned throughout the course. Define the problem and gather the data. Conduct exploratory data analysis for text data. Carry out preprocessing and feature extraction. Select and train a model. ? Evaluate the model and interpret the results. Bonus Chapter: Generative AI and NLP Introduction to Generative AI and its role in NLP. Overview of Generative Pretrained Transformer (GPT) models. Using GPT models for text generation and completion. Applying GPT models for improving autocomplete features. Use cases of GPT in question answering systems and chatbots. Bonus Chapter: Advanced Applications of NLP with GPT Fine-tuning GPT models for specific NLP tasks. Using GPT for sentiment analysis and text classification. Role of GPT in Named Entity Recognition (NER). Application of GPT in developing advanced chatbots. Ethics and limitations of GPT and generative AI technologies.

Duration 4 Days 24 CPD hours This course is intended for Students for AZ-600: Configuring and Operating a Hybrid Cloud with Microsoft Azure Stack Hub are interested in becoming Azure Stack Hub operators who provide cloud services to end users or customers from within their own datacenter using Azure Stack Hub. Azure Stack Hub operators responsibilities include planning, deploying, packaging, updating, and maintaining the Azure Stack Hub infrastructure. They also offer hybrid cloud resources and requested services and manage infrastructure as a service (IaaS) and platform as a service (PaaS). Overview Prepare for Azure Stack Hub deployment Manage infrastructure certificates for Azure Stack Hub Manage Azure Stack Hub registration Configure an Azure Stack Hub home directory Provision a service principal for Azure Stack Hub Recommend a business continuity disaster recovery (BCDR) strategy Manage Azure Stack Hub by using privileged endpoints Manage Azure Stack Hub Marketplace Offer App Services and Event Hub resource providers Manage usage and billing This course teaches Azure administrators and Azure Stack Hub operators how to plan, deploy, package, update, and maintain the Azure Stack Hub infrastructure. Lessons include deploying Azure Stack Hub, managing the Azure Stack Hub Marketplace, offering App Services and Event Hub resource providers, managing Azure Stack Hub registration, and maintaining system health. Overview of Azure Stack Hub Azure Stack Hub Datacenter integration Azure Stack Hub PowerShell Module review questions Provide Services Manage Azure Stack Hub Marketplace Offer an App Services resource provider Offer an Event Hubs resource provider Offer services Manage usage and billing Module review questions Implement Data Center Integration Prepare for Azure Stack Hub deployment Manage Azure Stack Hub registration Module review questions Manage Identity and Access for Azure Stack Hub Manage multi-tenancy Manage access Module review questions Manage the Azure Stack Hub Infrastructure Manage system health Azure Monitor on Azure Stack Hub Plan and configure business continuity and disaster recovery Manage capacity Update infrastructure Manage Azure Stack Hub by using privileged endpoints Module review questions Additional course details: Nexus Humans AZ-600T00 Configuring and Operating a Hybrid Cloud with Microsoft Azure Stack Hub 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 AZ-600T00 Configuring and Operating a Hybrid Cloud with Microsoft Azure Stack Hub 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 2 Days 12 CPD hours This course is intended for Todas aquellas personas que tengan relaci¢n con proyectos que requieran de una gesti¢n gil: Clientes, Promotores, Project Managers, Proveedores o Subcontratas, Equipo del Proyecto: Perfiles T‚cnicos, Perfiles de apoyo o Staff. En definitiva a cualquier persona que tenga relaci¢n con un proyecto gil. Overview Existen proyectos peque¤os, otros enormes, con una complejidad tecnol¢gica extrema otros en cambio muy sencillos. ¨Debemos gestionar todo tipo de proyectos con el mismo ?m‚todo??Desde finales del siglo pasado, se viene analizando la gesti¢n de proyectos cl sica conocida como Gesti¢n de Proyectos Predictiva, comprobando que no puede/debe ser aplicada a todo tipo de proyecto. Existen multitud de proyectos donde el nivel de detalle de las caracter¡sticas de los entregables est asociado al concepto IKIWISI (I?ll Know It When I See It -> Lo sabr‚ cuando lo vea), otros proyectos que tienen muy bien definido el objetivo, pero dadas unas necesidades cambiantes, la manera de abordarlo puede ser bien diferente, otros proyectos que? En definitiva se ha puesto de manifiesto que la gesti¢n de proyectos predictiva, no es del todo til para estos tipos de proyecto. Durante este curso, analizaremos otra forma de hacer las cosas. Veremos c¢mo abordar estos otros tipos de proyectos que requieren de una gesti¢n diferente: una Gesti¢n µgil. Existen proyectos peque¤os, otros enormes, con una complejidad tecnol¢gica extrema otros en cambio muy sencillos. ¨Debemos gestionar todo tipo de proyectos con el mismo ?m‚todo?? Introducci¢n a Agile y Scrum Primeros conceptos Metodolog¡as µgiles Agile Manifesto y Principios µgiles ¨Qu‚ hay bajo el paraguas de Agile? Las 3 grandes aproximaciones a Agile: LEAN, XP y Scrum El entorno de trabajo µgil Roles µgiles Trabajando de forma gil Definir la Visi¢n del Producto Planificar la Release y los Sprints El trabajo del d¡a a d¡a La revisi¢n del producto Preparando la entrega Gestionando de forma gil Gesti¢n del Alcance y los Proveedores Gesti¢n de Tiempos y Costes Gesti¢n del Equipo y las Comunicaciones Gesti¢n de Riesgos y la Calidad Garantizando el ‚xito Construir una base s¢lida Impulsar el cambio

Duration 5 Days 30 CPD hours This course is intended for Ideal candidates include network professionals who are looking to build their foundational knowledge of the ClearPass product portfolio. Overview After you successfully complete this course, expect to be able to: Ability to setup ClearPass as a AAA server Demonstrate Configuration Guest, OnGurad, Onboard and Profiling features Integrate with External AD Server Understand Monitoring and Reporting Demonstrate Scaling and deployment of best practices Configure AAA services for both wired and wireless networks Demonstrate the configuration of Aruba Downloadable User Roles. Demonstrate the configuration of Dynamic Segmentation with Aruba switches. This course prepares participants with foundational skills in Network Access Control using the ClearPass product portfolio. This 5-day classroom session includes both instructional modules and labs to teach participants about the major features of the ClearPass portfolio. Participants will learn how to setup ClearPass as an AAA server, and configure the Policy Manager, Guest, OnGuard and Onboard feature sets. In addition, this course covers integration with external Active Directory servers, Monitoring and Reporting, as well as deployment best practices. The student will gain insight into configuring authentication with ClearPass on both wired and wireless networks. Intro to ClearPass BYOD High Level Overview Posture and Profiling Guest and Onboard ClearPass for AAA Policy Service Rules Authentication Authorization and Roles Enforcement Policy and Profiles Authentication and Security Concepts Authentication Types Servers Radius COA Active Directory Certificates Intro to NAD NAD Devices Adding NAD to ClearPass Network Device Groups Network Device Attributes Aruba Controller as NAD Aruba Switch Aruba Instant Monitoring and Troubleshooting Monitoring Troubleshooting Logging Policy Simulation ClearPass Insight Insight Dashboard Insight Reports Insight Alerts Insight Search Insight Administration Insight Replication Active Directory Adding AD as Auth Source Joining AD domain Using AD services External Authentication Multiple AD domains LDAP Static Host Lists SQL Database External Radius Server Guest Guest Account creation Web Login pages Guest Service configuration Self-registration pages Configuring NADS for Guest Guest Manager Deep Dive Web Login Deep Dive Sponsor Approval MAC Caching Onboard Intro to Onboard Basic Onboard Setup Onboard Deepdive Single SSID Onboarding Dual SSID Onboarding Profiling Intro to Profiling Endpoint Analysis Deep Dive Posture Intro to Posture Posture Deployment Options OnGuard Agent Health Collection OnGuard workflow 802.1x with Posture using Persistent/dissolvable agent OnGuard web Login Monitoring and Updates Operation and Admin Users Operations Admin Users Clustering and Redundancy Clustering Redundancy LAB Licensing ClearPass Licensing Base License Applications ClearPass Exchange Intro Examples General HTTP Palo Alto Firewall Configuration Case Study Objectives Discussion Advanced Labs Overview Wired Port Authentication 802.1X for access layer switch ports Profiling on Wired Network Configuration of Dynamic Segmentation Aruba Downloadable User Roles Downloadable User Role Enforcement in ClearPass Aruba Controller/Gateway configuration Aruba Switch configuration Troubleshooting

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Architecture and course review Post course knowledge check Architecture review Course review