8147 Courses

Duration 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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 This training is ideally suited for data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities. It will also benefit system administrators and data engineers who wish to harness Elastic Stack's functionalities for efficient system logging, monitoring, and robust data visualization. With a focus on practical application, this course is perfect for those aspiring to solve complex data challenges in real-time environments across diverse industry verticals. 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 explore: New features and updates introduced in Elastic Stack 7.0 Fundamentals of Elastic Stack including Elasticsearch, Logstash, and Kibana Useful tips for using Elastic Cloud and deploying Elastic Stack in production environments How to install and configure an Elasticsearch architecture How to solve the full-text search problem with Elasticsearch Powerful analytics capabilities through aggregations using Elasticsearch How to build a data pipeline to transfer data from a variety of sources into Elasticsearch for analysis How to create interactive dashboards for effective storytelling with your data using Kibana How to secure, monitor and use Elastic Stack's alerting and reporting capabilities The Elastic Stack is a powerful combination of tools for techniques such as distributed search, analytics, logging, and visualization of data. Elastic Stack 7.0 encompasses new features and capabilities that will enable you to find unique insights into analytics using these techniques. Geared for experienced data analysts, IT professionals, and software developers who seek to augment their data processing and analytics capabilities, Working with Elasticsearch will explore how to use Elastic Stack and Elasticsearch efficiently to build powerful real-time data processing applications. Throughout the two-day hands-on course, you?ll explore the power of this robust toolset that enables advanced distributed search, analytics, logging, and visualization of data, enabled by new features in Elastic Stack 7.0. You?ll delve into the core functionalities of Elastic Stack, understanding the role of each component in constructing potent real-time data processing applications. You?ll gain proficiency in Elasticsearch for distributed searching and analytics, Logstash for logging, and Kibana for compelling data visualization. You?ll also explore the art of crafting custom plugins using Kibana and Beats, and familiarize yourself with Elastic X-Pack, a vital extension for effective security and monitoring. The course also covers essentials like Elasticsearch architecture, solving full-text search problems, data pipeline building, and creating interactive Kibana dashboards. Learn how to deploy Elastic Stack in production environments and explore the powerful analytics capabilities offered through Elasticsearch aggregations. The course will also touch upon securing, monitoring, and utilizing Elastic Stack's alerting and reporting capabilities. Hands-on labs, captivating demonstrations, and interactive group activities enrich your learning journey throughout the course. Introducing Elastic Stack What is Elasticsearch, and why use it? Exploring the components of the Elastic Stack Use cases of Elastic Stack Downloading and installing Getting Started with Elasticsearch Using the Kibana Console UI Core concepts of Elasticsearch CRUD operations Creating indexes and taking control of mapping REST API overview Searching - What is Relevant The basics of text analysis Searching from structured data Searching from the full text Writing compound queries Modeling relationships Analytics with Elasticsearch The basics of aggregations Preparing data for analysis Metric aggregations Bucket aggregations Pipeline aggregations Substantial Lab and Case Study Analyzing Log Data Log analysis challenges Using Logstash The Logstash architecture Overview of Logstash plugins Ingest node Visualizing Data with Kibana Downloading and installing Kibana Preparing data Kibana UI Timelion Using plugins

Duration 5 Days 30 CPD hours This course is intended for The Red Hat Enterprise Linux Diagnostics and Troubleshooting course is aimed at senior system administrators who wish to learn more about troubleshooting. Overview As a result of attending this course, students should be able to analyze the operational state of computer systems and identify potential issues. When problems appear, students will be able to successfully resolve the issue, returning it to a secure and stable working condition. Students should be able to demonstrate the following skills: Diagnostic and analysis procedures Preventive maintenance planning and implementation System recovery using proven tools and techniques This course enriches your skills by providing the tools and techniques that you need to successfully diagnose, and fix, a variety of potential issues. You will work through hands-on problems in various subsystems to diagnose and fix common issues.You will learn develop the skills to apply the scientific method to a structured form of troubleshooting. You will then apply this approach in troubleshooting various types of problems, including boot issues, hardware issues, storage issues, RPM issues, network issues, third-party application issues, security issues, and kernel issues. At the end of the course, you will be empowered to complete various comprehensive review labs to test your skills.This course covers the same material as RH342, but includes the Red Hat Certified Specialist in Linux Diagnostics and Troubleshooting exam (EX342) Introduction to troubleshooting Describe a generalized strategy for troubleshooting. Take proactive steps to prevent small issues Prevent small issues from becoming large problems by employing proactive system administration techniques. Troubleshoot boot issues Identify and resolve issues that can affect a system's ability to boot. Identify hardware issues Identify hardware problems that can affect a system?s ability to operate. Troubleshoot storage issues Identify and fix issues related to storage. Troubleshoot RPM issues Identify and fix problems in, and using, the package management subsystem. Troubleshoot network issues Identify and resolve network connectivity issues. Troubleshoot application issues Debug application issues. Deal with security issues Identify and fix issues related to security subsystems. Troubleshoot kernel issues Identify kernel issues and assist Red Hat Support in resolving kernel issues. Red Hat Enterprise Linux Diagnostics and Troubleshooting comprehensive review Practice and demonstrate knowledge and skills learned in Red Hat Enterprise Linux Diagnostics and Troubleshooting. Additional course details: Nexus Humans Red Hat Linux Diagnostics and Troubleshooting with Exam (RH343) 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 Red Hat Linux Diagnostics and Troubleshooting with Exam (RH343) 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: 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 architectures. Module 1: 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 Module 2: 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 Module 3: 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 Module 4: 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 Module 5: 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 Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes on AWS 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 Building Data Lakes on AWS 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 Python-experienced attendees who wish to be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling using Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains with the help of step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool. Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Geared for data team members with incoming Python scripting experience, Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding lessons, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. Students will leave the course armed with the skills required to use pandas to ensure the veracity of their data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Introduction to Data Analysis Fundamentals of data analysis Statistical foundations Setting up a virtual environment Working with Pandas DataFrames Pandas data structures Bringing data into a pandas DataFrame Inspecting a DataFrame object Grabbing subsets of the data Adding and removing data Data Wrangling with Pandas What is data wrangling? Collecting temperature data Cleaning up the data Restructuring the data Handling duplicate, missing, or invalid data Aggregating Pandas DataFrames Database-style operations on DataFrames DataFrame operations Aggregations with pandas and numpy Time series Visualizing Data with Pandas and Matplotlib An introduction to matplotlib Plotting with pandas The pandas.plotting subpackage Plotting with Seaborn and Customization Techniques Utilizing seaborn for advanced plotting Formatting Customizing visualizations Financial Analysis - Bitcoin and the Stock Market Building a Python package Data extraction with pandas Exploratory data analysis Technical analysis of financial instruments Modeling performance Rule-Based Anomaly Detection Simulating login attempts Exploratory data analysis Rule-based anomaly detection Getting Started with Machine Learning in Python Learning the lingo Exploratory data analysis Preprocessing data Clustering Regression Classification Making Better Predictions - Optimizing Models Hyperparameter tuning with grid search Feature engineering Ensemble methods Inspecting classification prediction confidence Addressing class imbalance Regularization Machine Learning Anomaly Detection Exploring the data Unsupervised methods Supervised methods Online learning The Road Ahead Data resources Practicing working with data Python practice

Duration 2 Days 12 CPD hours This course is intended for Data Modelers Participants will learn the full scope of the metadata modeling process, from initial project creation, to publishing a dynamic cube, and enabling end users to easily author reports and analyze data. Introduction to IBM Cognos Dynamic Cubes Define and differentiate Dynamic Cubes Dynamic Cubes characteristics Examine Dynamic Cube requirements Examine Dynamic Cube components Examine high level architecture IBM Cognos Dynamic Query Review Dimensional Data Structures Dynamic Cubes caching Create & Design a Dynamic Cube Explore the IBM Cognos Cube Designer Review the cube development process Examine the Automatic Cube Generation Manual development overview Create dimensions Model the cube Best practice for effective modeling Deploy & Configure a Dynamic Cube Deploy a cube Explore the Estimate Hardware Requirements Identify cube management tasks Examine Query Service administration Explore Dynamic Cube properties Schedule cube actions Use the DCAdmin comment line tool Advanced Dynamic Cube Modelling Examine advanced modeling concepts Explore modeling caveats Calculated measures and members Model Relative Time Explore the Current Period property Define period aggregation rules for measures Advanced Features of Cube Designer Examine multilingual support Examine ragged hierarchies and padding members Define Parent-Child Dimensions Refresh Metadata Import Framework Manager packages Filter measures and dimensions Optimize Performance with Aggregates Identify aggregates and aggregate tables In-memory aggregates Use Aggregate Advisor to identify aggregates User defined in-memory aggregates Optimize In-Memory Aggregates automatically Aggregate Advisor recommendations Monitor Dynamic Cube performance Model aggregates (automatically vs manually) Use Slicers to define aggregation partitions Define Security Overview of Dynamic Cube security Identify security filters The Security process - Three steps Examine security scope Identify scope rules Identify roles Capabilities and access permissions Cube security deep dive Model a Virtual Cube Explore virtual cubes Create the virtual cube Explore virtual cube objects Examine virtual measures and calculated members Currency conversion using virtual cubes Security on virtual cubes Introduction to IBM Cognos Analytics Define IBM Cognos Analytics Redefined Business Intelligence Self-service Navigate to content in IBM Cognos Analytics Interact with the user interface Model data with IBM Cognos Analytics IBM Cognos Analytics components Create reports Perform self-service with analysis and Dashboards IBM Cognos Analytics architecture (high level) IBM Cognos Analytics security Package / data source relationship Create Data modules Upload files Additional course details: Nexus Humans B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) 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 B6063 IBM Cognos Cube Designer - Design Dynamic Cubes (v11.0) 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 The audience includes System administrators, IT administrators, Maintenance personnel, Procurement personnel, Inventory personnel, Asset managers and work managers. The audience also includes consultants that are looking to gain an understanding of Maximo Asset Management 7.6.x. Overview After completing this course, you should be able to perform the following tasks: Describe the Asset Management Lifecycle in Maximo Query and Retrieve Data Describe options available for various applications Describe and use the different Maximo applications and functions as they relate to business processes Enter core data elements and data structures This course helps clients to make informed business decisions with the design and planning of their implementation. Consultants gain a foundation on which to build their product knowledge and skill set. The foundation also helps in working with clients to obtain optimal value from the product. It provides the fundamental concepts and setup considerations of the various business areas that Maximo supports and, as a bonus section, the new Work Center functionality. This course consists of lectures, demonstrations, and labs that cover applications, processes and interrelationships within Maximo. IBM Maximo Asset Management Overview This unit provides a high-level overview of IBM Maximo Asset Management framework architecture and its key element and components. You learn about strategic asset management with IBM Maximo Asset Management to manage assets through their life cycle. You also discuss implementation considerations and system configuration as they pertain to integrations, reporting, and business processes. Item Planning and Setup This unit focuses on the planning and setup of inventory item and asset configurations. You use the applications to set up inventory items and asset configurations in Maximo. Assets This unit focuses on the creation of assets and how they are used in Maximo Asset Management. You will learn the applications that can create assets, associate assets to people and locations and learn how to move assets between locations. Work Management This unit focuses on learning to use Maximo for work management with the generation and processing of work orders to completion. This unit will include the new Work Centers that came available in Maximo 7.6.0.5. Work Centers are made available for Business Analyst, Supervisors and Workers complete the work order tasks. We will also look at the new Inspection Form tool. Purchasing This unit focuses on learning to use Maximo for procurement, starting with generating purchase requests, then processing them, and completing them. Developer and System Administration Each lesson in this unit focuses on different aspects of entering records into the database. This unit is also an overview of the different applications and setup options that are available with IBM Maximo Asset Management. The focus is on using applications that are primarily used for building (setting up) the database. Additional course details: Nexus Humans U5TR572 - IBM Maximo Asset Management Fundamentals v7.6.x 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 U5TR572 - IBM Maximo Asset Management Fundamentals v7.6.x course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for experienced integration specialists and senior-level developers with experience in application development, messaging middleware applications, and transport protocols such as HTTP and FTP. Overview Describe the features and uses of IBM App Connect Enterprise Develop, deploy, and test message flow applications Generate message flow applications from predefined patterns Use the IBM App Connect Enterprise Toolkit problem determination aids to diagnose and solve development and runtime errors Describe the function and appropriate use of IBM App Connect Enterprise processing nodes Write basic Extended Structured Query Language and Java programs to transform data Use the IBM Graphical Data Mapping editor to transform data Define, use, and test simple XML and Data Format Description Language (DFDL) data models Describe supported transport protocols and how to call them in message flows IBM App Connect Enterprise provides connectivity and universal data transformation in heterogeneous IT environments. It enables businesses of any size to eliminate point-to-point connections and batch processing, regardless of operating system, protocol, and data format. This course teaches you how to use IBM App Connect Enterprise to develop, deploy, and support message flow applications. These applications use various messaging topologies to transport messages between service requesters and service providers, and allow the messages to be routed, transformed, and enriched during processing. In this course, you learn how to construct applications to transport and transform data. The course explores how to control the flow of data by using various processing nodes, and how to use databases and maps to transform and enrich data during processing. You also learn how to construct data models by using the Data Format Description Language (DFDL) Course Outline Introduction to IBM App Connect Enterprise Application development fundamentals Exercise: Importing and testing a message flow Creating message flow applications Exercise: Creating a message flow application Connecting to IBM MQ Exercise: Connecting to IBM MQ Controlling the flow of messages Exercise: Adding flow control to a message flow application Modeling the data Exercise: Creating a DFDL model Processing file data Exercise: Processing file data Using problem determination tools and help resources Exercise: Using problem determination tools Exercise: Implementing explicit error handling Mapping messages with the Graphical Data Mapping editor Referencing a database in a message flow application Exercise: Referencing a database in a map Using Compute nodes to transform messages Exercise: Transforming data by using the Compute and JavaCompute nodes Processing JMS, HTTP, and web service messages Preparing for production Exercise: Creating a runtime-aware message flow Additional course details: Nexus Humans WM668G IBM App Connect Enterprise V11 Application Development 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 WM668G IBM App Connect Enterprise V11 Application Development 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 System integrators Overview By the end of the course, you should be able to meet the following objectives: Introduce troubleshooting principles and procedures Practice Linux commands that aid in the troubleshooting process Use command-line interfaces, log files, and the vSphere Client to diagnose and resolve problems in the vSphere environment Explain the purpose of key vSphere log files Monitor and optimize compute, network, and storage performance on ESXi hosts Monitor and optimize vCenter Server performance Identify networking problems based on reported symptoms, validate and troubleshoot the reported problem, identify the root cause and implement the appropriate resolution Analyze storage failure scenarios using a logical troubleshooting methodology, identify the root cause, and apply the appropriate resolution to resolve the problem Troubleshoot vSphere cluster failure scenarios and analyze possible causes Diagnose common VMware vSphere High Availability problems and provide solutions Identify and validate VMware ESXi™ host and VMware vCenter Server problems, analyze failure scenarios, and select the correct resolution Troubleshoot virtual machine problems, including migration problems, snapshot problems, and connection problems Troubleshoot performance problems with vSphere components This five-day, accelerated, hands-on training course is a blend of the VMware vSphere: Optimize and Scale and VMware vSphere: Troubleshooting courses. This Fast Track course includes topics from each of these advanced courses to equip experienced VMware administrators with the knowledge and skills to effectively optimize and troubleshoot vSphere at an expert level. Course Introduction Introductions and course logistics Course objectives Introduction to Troubleshooting Define the scope of troubleshooting Use a structured approach to solve configuration and operational problems Apply a troubleshooting methodology to logically diagnose faults and improve troubleshooting efficiency Troubleshooting Tools Use command-line tools (such as Linux commands, vSphere CLI, ESXCLI) to identify and troubleshoot vSphere problems Identify important vSphere log files and interpret the log file contents Network Optimization Explain performance features of network adapters Explain the performance features of vSphere networking Use esxtop to monitor key network performance metrics Troubleshooting Virtual Networking Analyze and resolve standard switch and distributed switch problems Analyze virtual machine connectivity problems and fix them Examine common management network connectivity problems and restore configurations Storage Optimization Describe storage queue types and other factors that affect storage performance Use esxtop to monitor key storage performance metrics Troubleshooting Storage Troubleshoot and resolve storage (iSCSI, NFS, and VMware vSphere© VMFS) connectivity and configuration problems Analyze and resolve common VM snapshot problems Identify multipathing-related problems, including common causes of permanent device loss (PDL) and all paths down (APD) events and resolve these problems 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, and host-swapping techniques for memory reclamation when memory is overcommitted Use esxtop to monitor key memory performance metrics Troubleshooting vSphere Clusters Identify and recover from problems related to vSphere HA Analyze and resolve VMware vSphere© vMotion© configuration and operational problems Analyze and resolve common VMware vSphere© Distributed Resource Scheduler? problems Troubleshooting Virtual Machines Identify possible causes and resolve virtual machine power-on problems Troubleshoot virtual machine connection state problems Resolve problems seen during VMware Tools? installations vCenter Server Performance Optimization Describe the factors that influence vCenter Server performance Use VMware vCenter© Server Appliance? tools to monitor resource use Troubleshooting vCenter Server and ESXi Analyze and fix problems with vCenter Server services Analyze and fix vCenter Server database problems Examine ESXi host and vCenter Server failure scenarios and resolve the problems

Duration 2 Days 12 CPD hours This course is intended for This is an intermediate and beyond level SQL course geared for experienced end users, data scientists, business analysts, application developers and database administrators. Students should have recently attended a basic SQL class or have equivalent experience. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment led by our expert practitioner, attendees will learn advanced skills needed to: Advanced Query Techniques Manipulating Table Data Using SQL's Data Manipulation Language (DML) User-Defined Functions Stored Procedures Triggers A company?s success hinges on responsible, accurate database management. Organizations rely on highly available data to complete all sorts of tasks, from creating marketing reports and invoicing customers to setting financial goals. Data professionals like analysts, developers and architects are tasked with creating, optimizing, managing and analyzing data from databases ? with little room for error. When databases aren?t built or maintained correctly, it?s easy to mishandle or lose valuable data. Our SQL Programming and Database Training Series provides students with the skills they require to develop, analyze and maintain data and in correctly structured, modern and secure databases. Next Level SQL explores how to identify and use advanced querying techniques to manipulate and index tables. All hands-on work in this course is ANSI SQL compliant and should work with most SQL databases such as Oracle, SQL Server, MySQL, MS Access, Informix, Sybase, or any other ANSI SQL compliant database. Advanced Query Techniques Join inner outer (Left, Right, Full) Subqueries Simple Correlated Using the Exists Operator Tips for Developing Complex Queries Performing Set Operations Aggregating Results Using Group by Creating Temporary Tables Manipulating Table Data Using SQL's Data Manipulation Language (DML) Inserting Data into Tables Updating Existing Data Deleting Records Truncating Tables Implementing Data Integrity with Transactions Beginning Explicit Transactions Committing Transactions Rolling Back Transactions User-Defined Functions Definition and Benefits of Use CREATE FUNCTION Syntax RETURN Clause and the RETURNS Statement Scalar vs. Table Functions Comparison with Stored Procedures Returning Scalar Values and Tables ALTER and DROP FUNCTION Stored Procedures Definition and Benefits of Use CREATE PROCEDURE Syntax Variables and Parameters Control of Program Flow ALTER and DROP PROCEDURE Implementation Differences Triggers Definition and Benefits of Use Alternatives (e.g., Constraints) CREATE TRIGGER Syntax Trigger Types 'Inserted' (or 'NEW') and 'Deleted' (or 'OLD') Tables Event Handling and Trigger Execution ALTER and DROP TRIGGER Additional course details: Nexus Humans Advanced SQL Programming (TTSQL005) 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 Advanced SQL Programming (TTSQL005) 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.