2896 ARC courses delivered Online

Duration 3 Days 18 CPD hours This course is intended for Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform Overview This course teaches students the following skills: Derive insights from data using the analysis and visualization tools on Google Cloud Platform Interactively query datasets using Google BigQuery Load, clean, and transform data at scale Visualize data using Google Data Studio and other third-party platforms Distinguish between exploratory and explanatory analytics and when to use each approach Explore new datasets and uncover hidden insights quickly and effectively Optimizing data models and queries for price and performance Want to know how to query and process petabytes of data in seconds? Curious about data analysis that scales automatically as your data grows? Welcome to the Data Insights course! This four-course accelerated online specialization teaches course participants how to derive insights through data analysis and visualization using the Google Cloud Platform. The courses feature interactive scenarios and hands-on labs where participants explore, mine, load, visualize, and extract insights from diverse Google BigQuery datasets. The courses also cover data loading, querying, schema modeling, optimizing performance, query pricing, and data visualization. This specialization is intended for the following participants: Data Analysts, Business Analysts, Business Intelligence professionals Cloud Data Engineers who will be partnering with Data Analysts to build scalable data solutions on Google Cloud Platform To get the most out of this specialization, we recommend participants have some proficiency with ANSI SQL. Introduction to Data on the Google Cloud Platform Highlight Analytics Challenges Faced by Data Analysts Compare Big Data On-Premises vs on the Cloud Learn from Real-World Use Cases of Companies Transformed through Analytics on the Cloud Navigate Google Cloud Platform Project Basics Lab: Getting started with Google Cloud Platform Big Data Tools Overview Walkthrough Data Analyst Tasks, Challenges, and Introduce Google Cloud Platform Data Tools Demo: Analyze 10 Billion Records with Google BigQuery Explore 9 Fundamental Google BigQuery Features Compare GCP Tools for Analysts, Data Scientists, and Data Engineers Lab: Exploring Datasets with Google BigQuery Exploring your Data with SQL Compare Common Data Exploration Techniques Learn How to Code High Quality Standard SQL Explore Google BigQuery Public Datasets Visualization Preview: Google Data Studio Lab: Troubleshoot Common SQL Errors Google BigQuery Pricing Walkthrough of a BigQuery Job Calculate BigQuery Pricing: Storage, Querying, and Streaming Costs Optimize Queries for Cost Lab: Calculate Google BigQuery Pricing Cleaning and Transforming your Data Examine the 5 Principles of Dataset Integrity Characterize Dataset Shape and Skew Clean and Transform Data using SQL Clean and Transform Data using a new UI: Introducing Cloud Dataprep Lab: Explore and Shape Data with Cloud Dataprep Storing and Exporting Data Compare Permanent vs Temporary Tables Save and Export Query Results Performance Preview: Query Cache Lab: Creating new Permanent Tables Ingesting New Datasets into Google BigQuery Query from External Data Sources Avoid Data Ingesting Pitfalls Ingest New Data into Permanent Tables Discuss Streaming Inserts Lab: Ingesting and Querying New Datasets Data Visualization Overview of Data Visualization Principles Exploratory vs Explanatory Analysis Approaches Demo: Google Data Studio UI Connect Google Data Studio to Google BigQuery Lab: Exploring a Dataset in Google Data Studio Joining and Merging Datasets Merge Historical Data Tables with UNION Introduce Table Wildcards for Easy Merges Review Data Schemas: Linking Data Across Multiple Tables Walkthrough JOIN Examples and Pitfalls Lab: Join and Union Data from Multiple Tables Advanced Functions and Clauses Review SQL Case Statements Introduce Analytical Window Functions Safeguard Data with One-Way Field Encryption Discuss Effective Sub-query and CTE design Compare SQL and Javascript UDFs Lab: Deriving Insights with Advanced SQL Functions Schema Design and Nested Data Structures Compare Google BigQuery vs Traditional RDBMS Data Architecture Normalization vs Denormalization: Performance Tradeoffs Schema Review: The Good, The Bad, and The Ugly Arrays and Nested Data in Google BigQuery Lab: Querying Nested and Repeated Data More Visualization with Google Data Studio Create Case Statements and Calculated Fields Avoid Performance Pitfalls with Cache considerations Share Dashboards and Discuss Data Access considerations Optimizing for Performance Avoid Google BigQuery Performance Pitfalls Prevent Hotspots in your Data Diagnose Performance Issues with the Query Explanation map Lab: Optimizing and Troubleshooting Query Performance Advanced Insights Introducing Cloud Datalab Cloud Datalab Notebooks and Cells Benefits of Cloud Datalab Data Access Compare IAM and BigQuery Dataset Roles Avoid Access Pitfalls Review Members, Roles, Organizations, Account Administration, and Service Accounts

Duration 3 Days 18 CPD hours This course is intended for Experienced system administrators and system integrators responsible for designing and implementing vSphere with Kubernetes Overview By the end of the course, you should be able to meet the following objectives: Describe vSphere with Kubernetes and use cases in on-premises environments Deploy vSphere with Kubernetes Describe the VMware NSX networking requirements for vSphere with Kubernetes. Create and manage vSphere with Kubernetes namespaces Deploy and run container applications on vSphere with Kubernetes Deploy and configure VMware Harbor Describe the VMware Tanzu™ Kubernetes Grid™ service Deploy a Tanzu Kubernetes Grid cluster Deploy and run container applications on a Tanzu Kubernetes Grid cluster Describe the vSphere with Kubernetes lifecycle Use logs and CLI commands to monitor and troubleshoot vSphere with Kubernetes During this 3-day course, you focus on deploying and managing VMware vSphere© with Kubernetes. You learn about how vSphere with Kubernetes can be used to orchestrate the delivery of Kubernetes clusters and containerized applications in a VMware vSphere© environment. Course Introduction Introductions and course logistics Course objectives Introduction to Containers and Kubernetes Describe Virtual Machines and Containers Describe Container Hosts Describe Container Engines Describe Dockerfile Describe Container Images Describe Image Registry Describe the purpose and functionality of Kubernetes Describe Manifest YAML files Explain Pod YAML files Explain ReplicaSets Explain Services Explain Deployments Introduction to vSphere with Kubernetes Describe the purpose and functionality of vSphere with Kubernetes Explain the integration with VMware Tanzu? Mission Control? Describe the capabilities of vSphere with Kubernetes Describe the components of vSphere with Kubernetes Contrast vSphere with Kubernetes to traditional Kubernetes Describe the requirements for vSphere with Kubernetes Prepare a vSphere cluster for vSphere with Kubernetes Describe the NSX components required for vSphere with Kubernetes Describe the network topology of vSphere with Kubernetes Explain the networking requirements of vSphere with Kubernetes Compare NSX networking objects with Kubernetes networking objects vSphere with Kubernetes Core Services Explain the architecture of the vSphere with Kubernetes Core Services Describe the Container Service Describe the Volume Service Describe the Network Service Describe the Registry Service Describe the use cases of vSphere with Kubernetes Enable vSphere with Kubernetes Deploy VMware Harbor Registry vSphere with Kubernetes Namespaces Describe a vSphere with Kubernetes namespace Contrast a vSphere with Kubernetes namespace to a traditional Kubernetes namespace Describe Resource Quotas Explain Authentication and Authorization to vSphere with Kubernetes Explain the use cases of namespaces Create a namespace Describe kubectl Use kubectl to interact with vSphere with Kubernetes Describe using kubectl pod deployment Explain scaling a pod deployment Explain managing pod lifecycle Explain deleting pods Use kubectl to deploy a pod Use kubectl to scale a pod Use kubectl to switch between namespaces VMware Tanzu Kubernetes Grid service Explain Tanzu Kubernetes Grid service Describe the use cases for Tanzu Kubernetes Grid clusters Describe the integration with Tanzu Mission Control Explain the lifecycle of Tanzu Kubernetes Grid clusters Deploy Tanzu Kubernetes Grid cluster Deploy pods to a Tanzu Kubernetes Grid cluster Monitoring and Troubleshooting Describe the monitoring tools for vSphere with Kubernetes Describe the troubleshooting tools for vSphere with Kubernetes Explain cluster, node, and namespace health Explain usage and capacity monitoring Describe vCenter Server events Describe vSphere with Kubernetes events Gather support information vSphere with Kubernetes Lifecycle Describe the vSphere with Kubernetes lifecycle Describe the Tanzu Kubernetes Grid lifecycle Describe scaling a vSphere with Kubernetes cluster Update vSphere with Kubernetes Update Tanzu Kubernetes Grid clusters Remove vSphere with Kubernetes Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware vSphere with Tanzu: Deploy and Manage [V7] 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 vSphere with Tanzu: Deploy and Manage [V7] course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 3 Days 18 CPD hours This course is intended for Server administrators Network engineers Systems engineers Consulting systems engineers Technical solutions architects Network administrators Storage administrators Network managers Cisco integrators and partners Overview After taking this course, you should be able to: Describe and implement Fibre Channel, zoning, and N-Port Virtualization (NPV) features on Cisco UCS Describe and implement Fibre Channel over Ethernet (FCoE) on Cisco UCS Describe Cisco UCS policies for service profiles Describe Cisco Adapter Fabric Extender (FEX) and Single Root I/O Virtualization Describe and implement Role-Based Access Control (RBAC) on Cisco UCS Describe and implement external authentication providers on Cisco UCS Manager Describe and implement key management on Cisco UCS Manager Describe Cisco UCS Director Describe and implement Cisco Intersight Describe the scripting options for Cisco UCS Manager Describe and implement monitoring on Cisco UCS Manager The Configuring Cisco Unified Computing System (DCCUCS) v1.0 shows you how to deploy, secure, operate, and maintain Cisco Unified Computing System? (Cisco UCS©) B-series blade servers, Cisco UCS C-Series, and S-Series rack servers for use in data centers. You will learn how to implement management and orchestration software for Cisco UCS. You will gain hands-on practice: configuring key features of Cisco UCS, Cisco UCS Director, and Cisco UCS Manager; implementing UCS management software including Cisco UCS Manager and Cisco Intersight?; and more. Implementing Cisco UCS Storage Area Network (SAN) SAN Introduction Cisco UCS Fabric Interconnect Fibre Channels modes Named VSANs Cisco UCS Fibre Channel and FCoE Storage Connectivity Describing Cisco UCS Policies for Service Profiles Storage Policies and Profiles Basic Input Output System (BIOS) Policies Boot Policy Intelligent Platform Management Interface (IPMI) Policies Scrub Policies Maintenance Policies Describing Cisco Adapter FEX and Single Root I/O Virtualization Cisco FEX Overview Cisco Adapter FEX Single Root I/O Virtualization Implementing RBAC on Cisco UCS RBAC in Cisco UCS Users, Roles, and Privileges Functions of Organizations and Locales Effective Rights of a User Implementing External Authentication Providers Options for External Authentication Providers Implementing Key Management on Cisco UCS Manager Public Key Infrastructure Implementing Cisco UCS Director Cisco UCS Director Overview Policies, Virtual Data Centers, and Catalogs Cisco UCS Director Virtualization Support Managing Compute with Cisco UCS Director Cisco UCS Manager Orchestration Self-Service Portal Reporting and Monitoring in Cisco UCS Director Implementing Cisco Intersight Cisco UCS Director Overview Important Features of Cisco Intersight Describing the Scripting Options for Cisco UCS Manager Cisco UCS Manager XML API Cisco UCS Management Information Tree Managed Object Browser Cisco UCS PowerTool Cisco UCS Python Software Development Kit (SDK) Implementing Key Management on Cisco UCS Manager Public Key Infrastructure Implementing Cisco Intersight Cisco Intersight Overview Important Features of Cisco Intersight Describing the Scripting Options for Cisco UCS Manager Cisco UCS Manager XML API Cisco UCS Management Information Tree Managed Object Browser Cisco UCS Manager PowerTool Cisco UCS Python SDK Implementing Monitoring on Cisco UCS Manager Logging Sources in Cisco UCS Manager Port Monitoring Capabilities of Cisco UCS Manager Simple Network Management Protocol (SNMP) Security Ramifications Cisco UCS Manager Call Home Feature Lab outline Configure Pod-Specific Device Aliases Configure Zoning Configure VSANs in Cisco UCS Manager Configure Unified Ports on Cisco UCS Fabric Interconnects Install and Boot VMware Elastic Sky X Integrated (ESXi) on Cisco UCS from the FCoE Logical Unit Number (LUN) via FCoE Configure RBAC Configure Cisco UCS Manager to Authenticate Users via Open Lightweight Directory Access Protocol (OpenLDAP) Configure a Trusted Point and Key Ring in Cisco UCS Manager Configure Cisco UCS with Cisco Intersight Configure Cisco UCS Manager Using Scripting Implement Syslog and Call Home Additional course details: Nexus Humans Cisco Configuring Cisco Unified Computing System v1.0 (DCCUCS) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Cisco Configuring Cisco Unified Computing System v1.0 (DCCUCS) 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 Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 2 Days 12 CPD hours This course is intended for This introduction to Spring development course requires that incoming students possess solid Java programming skills and practical hands-on Java experience. This class is geared for experienced Java developers who are new to Spring, who wish to understand how and when to use Spring in Java and JEE applications. Overview Working in a hands-on learning environment, led by our expert practitioner, students will: Explain the issues associated with complex frameworks such as JEE and how Spring addresses those issues Understand the relationships between Spring and JEE, AOP, IOC and JDBC. Write applications that take advantage of the Spring container and the declarative nature of assembling simple components into applications. Understand how to configure the Spring Boot framework Understand and work on integrating persistence into a Spring application Explain Spring's support for transactions and caching Work with Spring Boot to facilitate Spring setup and configuration Apply Aspect Oriented Programming (AOP) to Spring applications Become familiar with the conditionally loading of bean definitions and Application Contexts Understand how to leverage the power of Spring Boot Use Spring Boot to create and work with JPA repositories Introduction to Spring Boot | Spring Boot Quick Start is a hands-on Spring training course geared for experienced Java developers who need to understand what the Spring Boot is in terms of today's systems and architectures, and how to use Spring in conjunction with other technologies and frameworks. This leading-edge course provides added coverage of Spring's Aspect-Oriented Programming and the use of Spring Boot. Students will gain hands-on experience working with Spring, using Maven for project and dependancy management, and, optionally, a test-driven approach (using JUnit) to the labs in the course. The Spring framework is an application framework that provides a lightweight container that supports the creation of simple-to-complex components in a non-invasive fashion. Spring's flexibility and transparency is congruent and supportive of incremental development and testing. The framework's structure supports the layering of functionality such as persistence, transactions, view-oriented frameworks, and enterprise systems and capabilities. This course targets Spring Boot 2 , which includes full support for Java SE 11 and Java EE 8. Spring supports the use of lambda expressions and method references in many of its APIs. The Spring Framework Understand the value of Spring Explore Dependency Injection (DI) and Inversion of Control (IoC) Introduce different ways of configuring collaborators Spring as an Object Factory Initializing the Spring IoC Container Configuring Spring Managed Beans Introduce Java-based configuration The @Configuration and @Bean annotations Define bean dependencies Bootstrapping Java Config Context Injection in Configuration classes Using context Profiles Conditionally loading beans and configurations Bean Life-Cycle Methods Defining Bean dependencies Introduce Spring annotations for defining dependencies Explore the @Autowired annotation Stereotype Annotations Qualifying injection points Lifecycle annotations Using properties in Java based configuration The @Value annotation Using the Candidate Components Index Introduction to Spring Boot Introduce the basics of Spring Boot Explain auto-configuration Introduce the Spring Initializr application Bootstrapping a Spring Boot application Working with Spring Boot Provide an overview of Spring Boot Introduce starter dependencies Introduce auto-configuration @Enable... annotations Conditional configuration Spring Boot Externalized Configuration Bootstrapping Spring Boot Introduction to Aspect Oriented Programming Aspect Oriented Programming Cross Cutting Concerns Spring AOP Spring AOP in a Nutshell @AspectJ support Spring AOP advice types AspectJ pointcut designators Spring Boot Actuator Understand Spring Boot Actuators Work with predefined Actuator endpoints Enabling Actuator endpoints Securing the Actuator Developing in Spring Boot Introduce Spring Boot Devtools Enable the ConditionEvaluationReport Debugging Spring Boot applications Thymeleaf Provide a quick overview of Thymeleaf Introduce Thymeleaf templates Create and run a Spring Thymeleaf MVC application Additional course details: Nexus Humans Spring Boot Quick Start | Core Spring, Spring AOP, Spring Boot 2.0 and More (TT3322) 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 Spring Boot Quick Start | Core Spring, Spring AOP, Spring Boot 2.0 and More (TT3322) 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 The primary audience for this course is as follows: Channel partners and reseller engineers installing, configuring, and maintaining scalable and resilient deployments of Cisco Meeting Server Channel partners and reseller engineers providing presales support for scalable and resilient Cisco Meeting Server deployments Customer engineers supporting scalable and resilient Cisco Meeting Server deployments Overview Upon completion of this course, the learner should be able to meet the following objectives: Cisco Meeting Server API commands Scalable and resilient deployments Customization Recording In this 3-day course Cisco Meeting Server Advanced (COLLAB350), you will learn advanced techniques in installing, troubleshooting, and maintaining a single server and single server split deployment of Cisco Meeting Servers. The recently purchased Acano collaboration hardware and software includes video and audio-bridging technology that allows customers to connect video systems from multiple vendors across both cloud and hybrid environments. Cisco has incorporated Acano collaboration technologies into the new Meeting Server tool. Module 1: Reviewing Cisco Meeting Server Intermediate Component parts of a Cisco Meeting Solution Configuration steps for a Cisco Meeting Solution Module 2: APIs Purpose of APIs Benefits of APIs Types of APIs Function of the GET, POST, PUT, and DELETE commands Module 3: Configuring Software with an API How a user can interact with software using an API Download and install Chrome Postman Other API software Download the Cisco Meeting Server API guide Module 4: Configuring Spaces with the Cisco Meeting Server API Cisco Meeting Server API structure Use Chrome Postman for information on calls and spaces with the GET command Use Chrome Postman to post a new call space with the POST command Use Chrome Postman to place changes in a space, including adding a member, with the PUT command Use Chrome Postman to delete a space with the DELETE command Module 5: Customization Configuration Create and modify a user profile and assign users Create and modify a dual-tone multifrequency (DTMF) profile and apply to a user profile Modify the interactive voice response (IVR), color scheme, and background Module 6: Planning a Resilient and Scalable Cisco Meeting Server Deployment Resilient server solutions Scalable server solutions Geographically dispersed configurations and GeoDNS Domain Name System (DNS) records required for a resilient and scalable solution Certificate requirements for a resilient and scalable solution Module 7: Configuring a Database Cluster Relationship between cluster master and cluster slaves in a database cluster Certificate requirements for a secure database cluster Configure the certificates for a database cluster Configure a database cluster Module 8: Configuring a Call Bridge Cluster Relationship between the Call Bridge and the Database How cross-cluster spaces behave Configure certificates for Call Bridge clustering Storage of configuration when entering with the API and the individual web interfaces for Lightweight Directory Access Protocol (LDAP) and Call Bridge clustering Configure a Call Bridge Cluster Connect XMPP server to multiple Call Bridges Module 9: Configuring Load Balancers and Trunks Relationship between the XMPP server, Trunk and Load Balancer Configure certificates for multiple trunks and load balancers Configure multiple Trunks to multiple Load Balancers Module 10: Configuring Multiple Web Bridges Relationship between multiple Web Bridges and Call Bridges Internal and external DNS records support for both internal and external Web Bridges Configure certificates for multiple Web Bridges Configure multiple Web bridges Connect multiple Call Bridges to multiple Web Bridges Module 11: Configuring Multiple TURN Servers Relationship between multiple TURN servers and multiple Call Bridges Configure certificates for multiple TURN servers Configure multiple TURN servers Connect multiple Call Bridges to multiple TURN servers Module 12: Configuring Multiple Recorders Features of the recording capability Software, hardware, and licensing requirements for recording DNS records Configure the Recorder Relationship between multiple Recorders and multiple Call Bridges Configure a Call Bridge to use a Recorder Configure certificates for multiple Recorders servers Configure multiple Recorder servers Connect multiple Call Bridges to multiple Recorder servers Module 13: Integrating with a Resilient and Scalable Cisco Meeting Server Deployment Integration with Cisco TelePresence Video Communication Server (VCS) or Cisco Unified Communications Manager and multiple Call Bridges Integration with Cisco Expressway technology and multiple Call Bridges Integration with Microsoft Skype for Business and multiple Call Bridges Integration with Cisco TelePresence Management Suite and multiple Call Bridges Module 14: Deploying an H.323 Gateway Cisco preferred architecture for H.323 and Session Initiation Protocol (SIP) interoperability Functionality of the Cisco Meeting Server H.323 gateway Configuration the Cisco Meeting Server H.323 gateway Module 15: Multitenancy Options Purpose of the multitenancy capabilities Options for multitenancy capabilities on the Cisco Meeting Server Module 16: Customization Options Options available to customize Cisco Meeting Apps License keys required for customization Web Server requirements for customization Options available to customize recorded messaging Customization options available for invitation text

Duration 5 Days 30 CPD hours This course is intended for This course is intended for anyone responsible for conf iguring, maintaining, and troubleshooting Symantec Data Loss Prevention. Additionally, this course is intended for technical users responsible for creating and maintaining Symantec Data Loss Prevention policies and the incident response structure. Overview At the completion of the course, you will be able to: Enforce server, detection servers, and DLP Agents as well as reporting, workflow, incident response management, policy management and detection, response management, user and role administration, directory integration, and filtering. This course is designed to provide you with the fundamental know ledge to configure and administer the Symantec Data Loss Prevention Enforce platform. Introduction to Symantec Data Loss Prevention Symantec Data Loss Prevention overview Symantec Data Loss Prevention architecture Navigation and Reporting Navigating the user interface Reporting and analysis Report navigation, preferences, and features Report filters Report commands Incident snapshot Incident Data Access Hands-on labs: Become familiar with navigation and tools in the user interface. Create, filter, summarize, customize, and distribute reports. Create users, roles, and attributes. Incident Remediation and Workflow Incident remediation and w orkf low Managing users and attributes Custom attribute lookup User Risk Summary Hands-on labs: Remediate incidents and configure a user?s reporting preferences Policy Management Policy overview Creating policy groups Using policy templates Building policies Policy development best practices Hands-on labs: Use policy templates and policy builder to configure and apply new policies Response Rule Management Response rule overview Configuring Automated Response rules Configuring Smart Response rules Response rule best practices Hands-On Labs: Create and use Automated and Smart Response rules Described Content Matching DCM detection methods Hands-on labs: Create policies that include DCM and then use those policies to capture incidents Exact Data Matching and Directory Group Matching Exact data matching (EDM) Advanced EDM Directory group matching (DGM) Hands-on labs: Create policies that include EDM and DGM, and then use those policies to capture incident Indexed Document Matching Indexed document matching (IDM) Hands-on labs: Create policies that include IDM rules and then use those policies to capture incidents Vector Machine Learning Vector Machine Learning (VML) Hands-on labs: Create a VML profile, import document sets, and create a VML policy Network Monitor Review of Network Monitor Protocols Traffic filtering Network Monitor best practices Hands-On Labs: Apply IP and L7 filters Network Prevent Network Prevent overview Introduction to Network Prevent (Email) Introduction to Network Prevent (Web) Hands-On Labs: Configure Network Prevent (E-mail) response rules, incorporate them into policies, and use the policies to capture incidents Mobile Email Monitor and Mobile Prevent Introduction to Mobile Email Monitor Mobile Prevent overview Configuration VPN configuration Policy and Response Rule Creation Reporting and Remediation Troubleshooting Network Discover and Network Protect Network Discover and Network Protect overview Configuring Discover targets Configuring Box cloud targets Protecting data Auto-discovery of servers and shares Running and managing scans Reports and remediation Network Discover and Network Protect best practices Hands-on labs: Create and run a filesystem target using various response rules, including quarantining Endpoint Prevent Endpoint Prevent overview Detection capabilities at the Endpoint Configuring Endpoint Prevent Creating Endpoint response rules Viewing Endpoint Prevent incidents Endpoint Prevent best practices Managing DLP Agents Hands-on labs: Create Agent Groups and Endpoint response rules, monitor and block Endpoint actions, view Endpoint incidents, and use the Enforce console to manage DLP Agents Endpoint Discover Endpoint Discover overview Creating and running Endpoint Discover targets Using Endpoint Discover reports and reporting features Hands-on labs: Create Endpoint Discover targets, run Endpoint Discover targets, and view Endpoint Discover incidents Enterprise Enablement Preparing for risk reduction Risk reduction DLP Maturity model System Administration Server administration Language support Incident Delete Credential management Troubleshooting Diagnostic tools Troubleshooting scenario Getting support Hands-on labs: Interpret event reports and traffic reports, configure alerts, and use the Log Collection and Configuration tool Additional course details: Nexus Humans Symantec Data Loss Prevention 14.0 - Administration 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 Symantec Data Loss Prevention 14.0 - Administration 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 will deploy and manage networks based on HPE's ArubaOS-CX switches. Overview After you successfully complete this course, expect to be able to: Use NetEdit to manage switch configurations Use the Network Analytics Engine (NAE) to implement scripting solutions to provide for proactive network management and monitoring Compare and contrast VSX, VSF, and backplane stacking Explain how VSX handles a split-brain scenario Implement and manage a VSX fabric Define ACLs and identify the criteria by which ACLs select traffic Configure ACLs on AOS-CX switches to select given traffic Apply static ACLs to interfaces to meet the needs of a particular scenario Examine an ACL configuration and determine the action taken on specific packets Deploy AOS-Switches in single-area and multi-area OSPF systems Use area definitions and summaries to create efficient and scalable multiple area designs Advertise routes to external networks in a variety of OSPF environments Promote fast, effective convergence during a variety of failover situations Use virtual links as required to establish non-direct connections to the backbone Implement OSFP authentication Establish and monitor BGP sessions between your routers and ISP routers Advertise an IP block to multiple ISP routers Configure a BGP router to advertise a default route in OSPF Use Internet Group Management Protocol (IGMP) to optimize forwarding of multicast traffic within VLANs Describe the differences between IGMP and IGMP snooping Distinguish between PIM-DM and PIM-SM Implement PIM-DM and PIM-SM to route multicast traffic Implement Virtual Routing Forwarding (VRF) policies to contain and segregate routing information Create route maps to control routing policies Understand the use of user roles to control user access on AOS-CX switches Implement local user roles on AOS-CX switches and downloadable user roles using a ClearPass solution Implement 802.1X on AOS-CX switch ports Integrate AOS-CX switches with an Aruba ClearPass solution, which might apply dynamic role settings Implement RADIUS-based MAC Authentication (MAC-Auth) on AOS-CX switch ports Configure captive portal authentication on AOS-CX switches to integrate them with an Aruba ClearPass solution Combine multiple forms of authentication on a switch port that supports one or more simultaneous users Configure dynamic segmentation on AOS-CX switches This course teaches you the advanced skills necessary to implement and operate enterprise level Aruba campus switching solutions. You will build on the skills you learned at the Associate level to configure and manage modern, open standards-based networking solutions using Aruba's OS-CX routing and switching technologies. In this course, participants learn about ArubaOS-CX switch technologies including: securing port access with Aruba's dynamic segmentation, redundancy technologies such as Multiple Spanning Tree Protocol (MSTP), link aggregation techniques including Link Aggregation Protocol (LACP) and switch virtualization with Aruba?s Virtual Switching Extension (VSX) and Aruba's Virtual Switching Framework (VSF). This course is approximately 50% lecture and 50% hands-on lab exercises. Introduction to Aruba Switching Switches overview Architectures NetEdit Overview Centralized configuration Switch groups/templates AOS-CX mobile App Network Analytics Engine (NAE) Overview Configuration Core NAE feature lab sflow, local mirror, remote mirror VSX VSF vs. VSX: access and Agg/core design Stacking review VSF and uni/multi packet forwarding Stack fragments / split brain VSX Overview: roles, control, data, management planes VSX components (ISL, Keepalive, VSX LAG, Active Gateway, Active-Forwarding, Link Delay) Split Brain scenario Upstream Connectively Options (ROP single VRF, SVIs with multiple VRF, VSX Lag SVIs with multiple VRFs) Upstream/Downstream unicast traffic flow (South-North and North-South) VSX Configuration: VSX and Active Gateway VSX firmware updates ACLs Overview: types, components MAC ACL, Standard ACL, Extended ACL, Classifier-based Policies Configuration: wildcard bits, logging, pacl, vacl, racl Advanced OSPF Review basic OSPF Multi area: setup and aggregation Area-Types Stub, Totally Stub, NSSA, Totally NSSA External routes OSPF tuning: costs, bfd, gr, auth, vrrp, virt link BGP Overview: i/e bgp, as numbers Best path selection Configuration: route announcement Route filtering to prevent transit as IGMP Overview Querier Snooping Unknown multicasts Multicast Routing: PIM Overview PIM DM 802.1X Authentication Overview: roles, requirements, coa, accounting Dynamic port configuration: avp, acl, qos, VLAN Port-based vs. user-based: examples Radius service tracking, critical VLAN MAC Authentication Overview: Use cases Radius-based MAC Auth Dynamic Segmentation Leverage dynamic segmentation features Configure tunneled-node on AOS-CX switches Describe when and how to configure PAPI enhanced security, high availability, and fallback switching for tunneled-node Quality of Service Overview VoQ (Virtual Output Queue) QOS: queueing, QOS marks, dot1p, dscp Trust levels QOS configuration: port, VLAN, policies Interaction with user roles Queue configuration Rate limiters LLDP-MED Additional Routing Technologies VRF - Management VRF PBR MDNS PIM SM Capitve Portal Authentication Overview of guest solutions Built-in web auth ClearPass redirect with CPPM

Duration 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.