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Duration 5 Days 30 CPD hours This course is intended for This advanced course is appropriate for System Administrators, Technical Support Personnel, and Business Partners responsible for implementing LPARs on IBM Power Systems with AIX servers. Overview Discuss the advantages or value of PowerVM edition's featuresDefine micro-partitioning and shared processor LPARsDiscuss the benefits of simultaneous multithreadingDiscuss and configure the Integrated Virtual Ethernet (IVE)Install and configure the Virtual I/O ServerConfigure virtual network devices, such as virtual Ethernet and shared Ethernet adaptersConfigure virtual SCSI and virtual Fibre Channel storage adaptersConfigure virtual SCSI target devices on a virtual SCSI adapterDefine file-backed storage pools and file-backed virtual optical devicesIdentify single points of failure in virtualized environmentsConfigure multiple VIO servers for high availabilityConfigure advanced virtual networking optionsConfigure the shared Ethernet adapter failover featureConfigure advanced virtual SCSI optionsConfigure MPIO in a VIO server's client partitionManage the service events, configure call home, add, exchange FRUs, and discuss FSP failoverPerform PowerVM (VIOS) Maintenance This course explains the new features and benefits of virtualization including processor virtualization, Virtual I/O Server, and virtual devices, such as virtual Ethernet, virtual SCSI, and virtual Fibre Channel adapters. Course Outline Unit 1: Introduction to partitioning Exercise 1: Power Systems documentation overview Unit 2: HMC V8 enhancements Exercise 2: HMC enhanced interface Unit 3: Processor virtualization Exercise 3: Processor virtualization configuration Unit 4: Virtual Ethernet Exercise 4: Virtual Ethernet adapter configuration Unit 5: Virtual I/O Server and Shared Ethernet Adapter Exercise 5: Virtual I/O Server configuration Unit 6: Virtual SCSI devices Exercise 6: Client partition configuration Unit 7: Virtual network configuration with dual VIOS Exercise 7: SEA failover setup Unit 8: Virtual SCSI configurations with dual VIOS Exercise 8: Dual VIO server configuration with MPIO in the client partition Unit 9: Virtual Fibre Channel devices Exercise 9: Virtual Fibre Channel adapter configuration Unit 10: HMC Service Management Exercise 10: Manage service events Unit 11: PowerVM advanced systems maintenance Exercise 11: PowerVM system maintenance Exercise 12: (Optional) File-backed virtual SCSI devices Additional course details: Nexus Humans AN30 IBM Power Systems for AIX - Virtualization I - Implementing Virtualization 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 AN30 IBM Power Systems for AIX - Virtualization I - Implementing Virtualization 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 intended for both novice and experienced programmers. Some experience in C++, C# or Java is helpful. Overview After completing this course, you will be able to: Write and test JavaScript code in modern browsers. Understand and work with the browser's Document Object Model (DOM). Work DOM objects and write code to respond to events. Add jQuery to their projects and use jQuery to manipulate the DOM and to make Ajax calls. Understand Ajax and Web Services, including HTTP Verbs and asynchronous Ajax calls. Work with XML and JSON data formats. Understand and use RESTful Web Services including ODATA queries. Work with Anonymous Functions, Self-Invoking Functions and Closures. Understand the role of the Window and Navigator objects. Describe and understand variables, 'variable hoisting', and JavaScript objects. This course is an in depth hands-on study of JavaScript. The course includes detailed hands-on labs and Q&A labs. The labs include multiple projects that update an existing web site. This course is similar to the retired Course 20480: Programming in HTML5 with JavaScript and CSS3. Module 1: Basic Scripting JavaScript Basics JavaScript or ECMAScript? Which Version of JavaScript am I Using? Shims, Shivs, and Polyfills Writing and Testing JavaScript Editors and the F12 Tools Key Parts of a Script Module 2: The Document Object Model The DOM Accessing Objects from the DOM Responding to Events Adding Elements to the DOM Module 3: Introduction to jQuery jQuery Downloading the jQuery Library jQuery Versions Linking to the Library Selecting Elements using jQuery Working with the Data Returned by jQuery Setting CSS Properties Running a Function Against the jQuery Return Set Module 4: Ajax and Web Services Web Services HTTP Verbs Data Formats ? XML and JSON Ajax RESTful Web Services OData OData Queries OData Updates Module 5: Diving in Deeper Is JavaScript Compiled? Data Types and Objects Extending Data Types Variables, Scopes and Hoisting Functions The ?this? Keyword Errors Recursion

Duration 4 Days 24 CPD hours This course is intended for This in an intermediate-level Java development course geared for students experienced with Java and Spring programming essentials. This course does not cover Java or Spring development basics. Overview Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will: Understand the ReactiveX specification Understand the basics of Reactive Programming Discuss the advantages and limitations of Observables Write a client application capable of handling Reactive events Apply operators to event streams to filter, modify and combine the objects emitted by event publishers Select the appropriate type of Event Source Use both Cold and Hot Observables Deal with backpressure problems in reactive programming Develop a reactive web application using Spring WebFlux Define application flows of a WebFlux application Use the WebClient API to work with both synchronous and streaming APIs Develop Unit and Integration tests to test WebFlux endpoints Creating a reactive REST endpoint Become familiar with the basics of WebSockets Create a WebSocket endpoint using Spring Create a WebSocket client Understand the basics of NoSQL Become familiar with the basics of MongoDB Understand how the data in MongoDB can be retrieved using a Reactive API Define Spring Data MongoDB repositories Query the MongoDB using Spring Data Define a reactive repository using MongoDB Explore the Spring Data R2DBC API to perform reactive CRUD operations against a relational database Spring Data reative allow us to implement database operations relying on Reative Programming APIs. While the Spring R2DBC initiative aims to bring reactive programming to relational databaes, several NoSQL databases already provide this possibility. After an introduction to NoSQL and the MongoDB, this courses covers the APIs available to communicate with this NoSQL database using both blocking and reactive APIs.Introdcution to Reactive Spring is a comprehensive Java training workshop geared for experienced developers who wish to explore concurrent, asynchronous and reactive programming APIs and techniques using Spring. After an introduction to reactive programming, Reactive Streams and the Project Reactor APIs, this course will show how this APIs are integrated into Spring. Spring 5 includes Spring WebFlux, providing a reactive programming model for web applications, including support for Reactive REST APIs. Spring WebSocket assists in the creation of web applications which provide a full-duplex, two-way communication between client and server. Introduction to Reactive Programming Reactive Manifesto Introduce ReactiveX ReactiveX implementations The Observer, Iterator pattern and functional programming Discuss hot and cold publishers Reactive Streams API Introduce the Reactive Streams specification Publisher and Subscribers java.util.concurrent.Flow Transformation of Messages (Processor) Controlling messages Tutorial: Setup Eclipse for Using Maven Introduction Introduce the Reactor Building blocks Flux and Mono Creating observables Subscribing to a stream Testing Event Sources (introduction) Testing reactive implementations StepVerifier : test sequence of emitted items Defining expectations TestPublisher: produce test data to test downstream operators Reactive Operators Introduce Operators Show the use of marble diagrams Explain some commonly used operators Callback operators Schedulers (Multithreading) Thread usage of subscriber and consumer Using the subscribeOn method Introduce the Scheduler interface Using the observeOn method Backpressure Strategies for dealing with Backpressure ?reactive pull? backpressure Exception Handling Handling errors in onError Exception handling strategies Using onErrorReturn or onErrorNext operators Using the retry operators The Global Error Handler Spring Data Review Quick review of Spring Data repositories Query return types Defining Query methods Pagination and sorting R2DBC Reactive Relational Database Connectivity DatabaseClient Performing CRUD operations Reactive Query annotated methods Spring WebFlux: Introduction Annotated Controllers Functional Endpoints WebFlux configuration Creating a reactive REST endpoint Defining flows Defining the application flow Actions Defining decision Navigating flows RouterFunction View Technologies View technologies Using Thymeleaf to create the view View Configuration Spring WebClient: Introduction to WebClient Working with asynchronous and streaming APIs Making requests Handling the response Lab: WebClient WebTestClient Testing WebFlux server endpoints Testing controllers or functions Define integration tests Introduction to Spring Reactive WebSockets Be familiar with the basics of WebSockets Understand the HTTP handshake and upgrade Name some of the advantages of WebSockets Defining the WebSocket WebSocket Message Handling WebSocketSession Implementing the WebSockethandler Creating a Browser WebSocket Client WebSocket STOMP Streaming (or Simple) text-orientated messaging protocol Introduce SockJS Connecting to the STOMP endpoint Configuring the message broker STOMP destinations Reactive WebSocket Reactive WebSocket support Implement the reactive WebSocketHandler BigData Introduce Big Data Explain the need for enhanced data storage Introduction to MongoDB JavaScript Object Notation Overview Introduce Binary JSON (BSON) Starting the database Creating Collections and Documents Executing ?simple? database commands Introduce the ObjectID Searching for documents using query operators Updating and deleting documents MongoDB Compass Spring and MongoDB MongoDB Support in Spring Data MongoClient and MongoTemplate Spring Data MongoDB configuration @EnableMongoRepositories Adding documents to the database The @Document and @Field annotations Polymorphism and the _class property The Criteria object Spring Data MongoDB MongoRepository Field naming strategy Using JSON queries to find documents The @PersistenceConstructor annotation Reactive Repositories with MongoDB Using reactive repositories ReactiveMongoTemplate RxJava or Reactor Additional course details: Nexus Humans Introduction to Reactive Spring (TT3355 ) 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 Reactive Spring (TT3355 ) 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.

Overview This 1 day course focus on comprehensive review of the current state of the art in quantifying and pricing counterparty credit risk. Learn how to calculate each xVA through real-world, practical examples Understand essential metrics such as Expected Exposure (EE), Potential Future Exposure (PFE), and Expected Positive Exposure (EPE) Explore the ISDA Master Agreement, Credit Support Annexes (CSAs), and collateral management. Gain insights into hedging strategies for CVA. Gain a comprehensive understanding of other valuation adjustments such as Funding Valuation Adjustment (FVA), Capital Valuation Adjustment (KVA), and Margin Valuation Adjustment (MVA). Who the course is for Derivatives traders, structurers and salespeople xVA desks Treasury Regulatory capital and reporting Risk managers (market and credit) IT, product control and legal Quantitative researchers Portfolio managers Operations / Collateral management Consultants, software providers and other third parties Course Content To learn more about the day by day course content please click here To learn more about schedule, pricing & delivery options, book a meeting with a course specialist now

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Duration 3 Days 18 CPD hours This course is intended for This is an intermediate course for architects, system integrators, security administrators, network administrators, software engineers, technical support individuals, and IBM Business Partners who implement LPARs on IBM Power Systems. Overview Describe important concepts associated with managing POWER7 processor-based systems, such as Logical partitioning (LPAR), dynamic partitioning, virtual devices, virtual processors, virtual consoles, virtual Local Area Network (VLAN), and shared processors Describe the features of the PowerVM Editions. Use the System Planning Tool to plan an LPAR configuration Describe the functions of the HMC Configure and manage the HMC, including users and permissions, software, startup and shutdown, remote access features, network configuration, security features, HMC backup and restore options, and the HMC reload procedure Describe the rules associated with allocating resources, including dedicated processors, processing units for Micro-Partitions, memory, physical I/O for AIX and Linux partitions Configure and manage LPARs using the HMC Graphical User Interface (GUI) and HMC commands Interpret physical and AIX location codes and relate to the key hardware components Power on and power off the POWER7 system Use the HMC to back up and restore partition data In this course, students will learn the skills needed to become an effective administrator on IBM's POWER7-based systems that support Logical Partitioning (LPAR). Day 1 Introduction to partitioning Hardware system overview Hardware Management Console Day 2 Hardware Management Console (cont.) System Planning Tool HMC and managed system maintenance System power management Planning and configuring logical partitions Day 3 Planning and configuring logical partitions (cont.) Partition operations Dynamic resource allocation Exercise 9 Additional course details: Nexus Humans AN110 IBM Power Systems for AIX I - LPAR Configuration and Planning 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 AN110 IBM Power Systems for AIX I - LPAR Configuration and Planning 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 IS Security Officers IS Managers Risk Managers Auditors Information Systems Owners IS Control Assessors System Managers Government Employees Overview Upon completion, Certified Threat Intelligence Analyst students will be able to proactively collect threat data and implement strategies to limit exposure to those threats. Additionally, they will be prepared to take the C)TIA exam Everywhere you turn today, you hear about the need for threat intelligence analysis! However, in some cases, it is just a buzzword, while in other cases, threat intelligence is being touted as the remedy toward advanced persistent threats. The real question is, how do we leverage threat intelligence to reduce network vulnerabilities without wasting time and money? The answer is simple, Mile2?s Certified Threat Intelligence Analyst course. Mile2?s CTIA course will help security professionals learn how to make good use of the many sources of threat intelligence. It will aid an individual to understand what threat sources are helpful, which specific threats are targeted and which ones may need minor adjustments to monitor within your organization. Mile2?s CTIA course focuses heavily on hands-on labs, concentrating on discerning and interpreting threats and responding to them.ÿ The CTIA course focuses overall on current significant threats, threat actors, and identification procedures so that cyber-security professionals can implement the best policies and procures for their organizational security posture. Once complete, the student will be competent toward improving a company?s existing security infrastructure. Policies and methodologies learned in the CTIA will allow the student to use threat intelligence concepts to decrease overall company risk. Course Outline Threat Intelligence Basics Cyber Threats Threat Actors Case Studies Threat Identification Proactive Approach

Duration 3 Days 18 CPD hours This course is intended for Data Wrangling with Python takes a practical approach to equip beginners with the most essential data analysis tools in the shortest possible time. It contains multiple activities that use real-life business scenarios for you to practice and apply your new skills in a highly relevant context. Overview By the end of this course, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. In this course you will start with the absolute basics of Python, focusing mainly on data structures. Then you will delve into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python.This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The course will further help you grasp concepts through real-world examples and datasets. Introduction to Data Structure using Python Python for Data Wrangling Lists, Sets, Strings, Tuples, and Dictionaries Advanced Operations on Built-In Data Structure Advanced Data Structures Basic File Operations in Python Introduction to NumPy, Pandas, and Matplotlib NumPy Arrays Pandas DataFrames Statistics and Visualization with NumPy and Pandas Using NumPy and Pandas to Calculate Basic Descriptive Statistics on the DataFrame Deep Dive into Data Wrangling with Python Subsetting, Filtering, and Grouping Detecting Outliers and Handling Missing Values Concatenating, Merging, and Joining Useful Methods of Pandas Get Comfortable with a Different Kind of Data Sources Reading Data from Different Text-Based (and Non-Text-Based) Sources Introduction to BeautifulSoup4 and Web Page Parsing Learning the Hidden Secrets of Data Wrangling Advanced List Comprehension and the zip Function Data Formatting Advanced Web Scraping and Data Gathering Basics of Web Scraping and BeautifulSoup libraries Reading Data from XML RDBMS and SQL Refresher of RDBMS and SQL Using an RDBMS (MySQL/PostgreSQL/SQLite) Application in real life and Conclusion of course Applying Your Knowledge to a Real-life Data Wrangling Task An Extension to Data Wrangling

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.