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Duration 2 Days 12 CPD hours This course is intended for IBM SPSS Statistics users who want to familiarize themselves with the statistical capabilities of IBM SPSS StatisticsBase. Anyone who wants to refresh their knowledge and statistical experience. Overview Introduction to statistical analysis Describing individual variables Testing hypotheses Testing hypotheses on individual variables Testing on the relationship between categorical variables Testing on the difference between two group means Testing on differences between more than two group means Testing on the relationship between scale variables Predicting a scale variable: Regression Introduction to Bayesian statistics Overview of multivariate procedures This course provides an application-oriented introduction to the statistical component of IBM SPSS Statistics. Students will review several statistical techniques and discuss situations in which they would use each technique, how to set up the analysis, and how to interpret the results. This includes a broad range of techniques for exploring and summarizing data, as well as investigating and testing relationships. Students will gain an understanding of when and why to use these various techniques and how to apply them with confidence, interpret their output, and graphically display the results. Introduction to statistical analysis Identify the steps in the research process Identify measurement levels Describing individual variables Chart individual variables Summarize individual variables Identify the normal distributionIdentify standardized scores Testing hypotheses Principles of statistical testing One-sided versus two-sided testingType I, type II errors and power Testing hypotheses on individual variables Identify population parameters and sample statistics Examine the distribution of the sample mean Test a hypothesis on the population mean Construct confidence intervals Tests on a single variable Testing on the relationship between categorical variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Identify differences between the groups Measure the strength of the association Testing on the difference between two group meansChart the relationship Describe the relationship Test the hypothesis of two equal group means Assumptions Testing on differences between more than two group means Chart the relationship Describe the relationship Test the hypothesis of all group means being equal Assumptions Identify differences between the group means Testing on the relationship between scale variables Chart the relationship Describe the relationship Test the hypothesis of independence Assumptions Treatment of missing values Predicting a scale variable: Regression Explain linear regression Identify unstandardized and standardized coefficients Assess the fit Examine residuals Include 0-1 independent variables Include categorical independent variables Introduction to Bayesian statistics Bayesian statistics and classical test theory The Bayesian approach Evaluate a null hypothesis Overview of Bayesian procedures in IBM SPSS Statistics Overview of multivariate procedures Overview of supervised models Overview of models to create natural groupings

Duration 1 Days 6 CPD hours This course is intended for This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines. Completed either AWS Technical Essentials or Architecting on AWS Completed Building Data Lakes on AWS Overview In this course, you will learn to: Compare the features and benefits of data warehouses, data lakes, and modern data architectures Design and implement a data warehouse analytics solution Identify and apply appropriate techniques, including compression, to optimize data storage Select and deploy appropriate options to ingest, transform, and store data Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights Secure data at rest and in transit Monitor analytics workloads to identify and remediate problems Apply cost management best practices In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse service. The course focuses on the data collection, ingestion, cataloging, storage, and processing components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to support both analytics and machine learning workloads. You will also learn to apply security, performance, and cost management best practices to the operation of Amazon Redshift. Module A: Overview of Data Analytics and the Data Pipeline Data analytics use cases Using the data pipeline for analytics Module 1: Using Amazon Redshift in the Data Analytics Pipeline Why Amazon Redshift for data warehousing? Overview of Amazon Redshift Module 2: Introduction to Amazon Redshift Amazon Redshift architecture Interactive Demo 1: Touring the Amazon Redshift console Amazon Redshift features Practice Lab 1: Load and query data in an Amazon Redshift cluster Module 3: Ingestion and Storage Ingestion Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API Data distribution and storage Interactive Demo 3: Analyzing semi-structured data using the SUPER data type Querying data in Amazon Redshift Practice Lab 2: Data analytics using Amazon Redshift Spectrum Module 4: Processing and Optimizing Data Data transformation Advanced querying Practice Lab 3: Data transformation and querying in Amazon Redshift Resource management Interactive Demo 4: Applying mixed workload management on Amazon Redshift Automation and optimization Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster Module 5: Security and Monitoring of Amazon Redshift Clusters Securing the Amazon Redshift cluster Monitoring and troubleshooting Amazon Redshift clusters Module 6: Designing Data Warehouse Analytics Solutions Data warehouse use case review Activity: Designing a data warehouse analytics workflow Module B: Developing Modern Data Architectures on AWS Modern data architectures

Duration 5 Days 30 CPD hours This course is intended for This course is for professionals who need knowledge about implementing various Service Provider core technologies and advanced routing technologies. Network administrators System engineers Project managers Network designers Overview After taking this course, you should be able to: Describe the main characteristics of routing protocols that are used in Service provider environments Implement advanced features of multiarea Open Shortest Path First (OSPFv2) running in Service Provider networks Implement advanced features of multilevel Intermediate System to Intermediate System (ISIS) running in Service Provider networks Configure route redistribution Configure Border Gateway Protocol (BGP) in order to successfully connect the Service Provider network to the customer or upstream Service Provider Configure BGP scalability in Service Provider networks Implement BGP security options Implement advanced features in order to improve convergence in BGP networks Troubleshoot OSPF, ISIS, and BGP Implement and verify MPLS Implement and troubleshoot MPLS traffic engineering Implement and verify segment routing technology within an interior gateway protocol Describe how traffic engineering is used in segment routing networks Implement IPv6 tunneling mechanisms Describe and compare core multicast concepts Implement and verifying the PIM-SM protocol Implement enhanced Protocol-Independent Multicast - Sparse Mode (PIM-SM) features Implement Multicast Source Discovery Protocol (MSDP) in the interdomain environment Implement mechanisms for dynamic Rendezvous Point (RP) distribution The Implementing Cisco Service Provider Advanced Routing Solutions (SPRI) 5-day course teaches you theories and practices to integrate advanced routing technologies including routing protocols, multicast routing, policy language, Multiprotocol Label Switching (MPLS), and segment routing, expanding your knowledge and skills in service provider core networks. This course prepares you for the 300-510 Implementing Cisco© Service Provider Advanced Routing Solutions (SPRI) exam. The course qualifies for 40 Cisco Continuing Education credits (CE) towards recertification. This course will help you: Gain the high-demand skills to maintain and operate advanced technologies related to Service Provider core networks Increase your knowledge and skills for implementing Service Provider core advanced technologies though hands-on application and practical instruction Prepare to take the 300-510 SPRI exam Course Outline Implementing and Verifying Open Shortest Path First Multiarea Networks Implementing and Verifying Intermediate System to Intermediate System Multilevel Networks Introducing Routing Protocol Tools, Route Maps, and Routing Policy Language Implementing Route Redistribution Influencing Border Gateway Protocol Route Selection Scaling BGP in Service Provider Networks Securing BGP in Service Provider Networks Improving BGP Convergence and Implementing Advanced Operations Troubleshooting Routing Protocols Implementing and Verifying MPLS Implementing Cisco MPLS Traffic Engineering Implementing Segment Routing Describing Segment Routing Traffic Engineering (SR TE) Deploying IPv6 Tunneling Mechanisms Implementing IP Multicast Concepts and Technologies Implementing PIM-SM Protocol Implementing PIM-SM Enhancements Implementing Interdomain IP Multicast Implementing Distributed Rendezvous Point Solution in Multicast Network

SAP Training Online | Sap Training in London, UK What is SAP? SAP is one of the largest ERP(Enterprise Resource Planning) software in the world. It provides end to end solution for Financials, Manufacturing, Logistics, Distributions etc. SAP applications, built around their latest R/3 system, provide the capability to manage financial, asset, and cost accounting, production operations and materials, personnel, plants, and archived documents. The R/3 system runs on a number of platforms including Windows and MAC and uses the client/server model. SAP Career Potential You can become a SAP consultant. SAP consultants analyze, design, and configure new computer software and systems in accordance with their employers' or clients' specifications, as well as write programs such as forms, specifications, and interfaces. They also test new interfaces to ensure that system workflows are optimized and interact with end-users to make changes as requested and obtain feedback. As a SAP consultant, you could expect to earn £80,000-£150,000 per year. Which SAP Module Osborne Training offers training on various sap modules such as  Financial and Controlling Sales and Distribution Material Management CRM SRM HANA And many more... Study Options Instructor-Led Live SAP Online Training Students attending training through Online LIVE Training have a real-time, Live Instructor-Led student experience through the world-class Virtual Learning Campus. Online LIVE Training provides an engaging live classroom environment that allows students to easily interact with instructors and fellow students virtually. Classroom-Based Live SAP Training in London Osborne Training offers evening sessions for classroom-based training, where an experienced Tutor/Consultant goes through the whole SAP Training course from the London campus. Only selected modules are offered from the London campus. Free Certification from Osborne Training on completion. You may attempt for SAP certification exams online to get certificate directly from SAP. Syllabus varies depending on the module take. Please send a query to receive full syllabus information.

Duration 5 Days 30 CPD hours This course is intended for Entry- to mid-level network engineers Network administrators Network support technicians Help desk technicians Overview After taking this training, you should be able to: Illustrate the hierarchical network design model and architecture using the access, distribution, and core layers Compare and contrast the various hardware and software switching mechanisms and operation while defining the Ternary Content Addressable Memory (TCAM) and Content Addressable Memory (CAM) along with process switching, fast switching, and Cisco Express Forwarding concepts Troubleshoot Layer 2 connectivity using VLANs and trunking Implement redundant switched networks using Spanning Tree Protocol Troubleshoot link aggregation using Etherchannel Describe the features, metrics, and path selection concepts of Enhanced Interior Gateway Routing Protocol (EIGRP) Implement and optimize Open Shortest Path First (OSPF)v2 and OSPFv3, including adjacencies, packet types and areas, summarization, and route filtering for IPv4 and IPv6 Implement External Border Gateway Protocol (EBGP) interdomain routing, path selection, and single and dual-homed networking Implement network redundancy using protocols such as Hot Standby Routing Protocol (HSRP) and Virtual Router Redundancy Protocol (VRRP) Implement internet connectivity within Enterprise using static and dynamic Network Address Translation (NAT) Describe the virtualization technology of servers, switches, and the various network devices and components Implement overlay technologies such as Virtual Routing and Forwarding (VRF), Generic Routing Encapsulation (GRE), VPN, and Location Identifier Separation Protocol (LISP) Describe the components and concepts of wireless networking, including Radio Frequency (RF) and antenna characteristics, and define the specific wireless standards Describe the various wireless deployment models available, including autonomous Access Point (AP) deployments and cloud-based designs within the centralized Cisco Wireless LAN Controller (WLC) architecture Describe wireless roaming and location services The Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR) v1.3 training gives you the knowledge and skills needed to install, configure, operate, and troubleshoot an enterprise network and introduces you to overlay network design by using SD-Access and SD-WAN solutions. You?ll also learn to understand and implement security principles and automation and programmability within an enterprise network. Course Outline Examining Cisco Enterprise Network Architecture Exploring Cisco Switching Paths Implementing Campus LAN Connectivity Building Redundant Switched Topology Implementing Layer 2 Port Aggregation Understanding EIGRP Implementing OSPF Optimizing OSPF Exploring EBGP Implementing Network Redundancy Implementing NAT Introducing Virtualization Protocols and Techniques Understanding Virtual Private Networks and Interfaces Understanding Wireless Principles Examining Wireless Deployment Options Understanding Wireless Roaming and Location Services Examining Wireless AP Operation Implementing Wireless Client Authentication Troubleshooting Wireless Client Connectivity Implementing Network Services Using Network Analysis Tools Implementing Infrastructure Security Implementing Secure Access Control Discovering the Basics of Python Programming Discovering Network Programmability Protocols Implementing Layer 2 Port Aggregation Discovering Multicast Protocols Understanding QoS Exploring Enterprise Network Security Architecture Exploring Automation and Assurance Using Cisco DNA Center Examining the Cisco SD-Access Solution Understanding the Working Principles of the Cisco SD-WAN Solution

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

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Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 5 Days 30 CPD hours This course is intended for This course will help you: Configure, troubleshoot, and manage enterprise wired and wireless networks Implement security principles within an enterprise network Overview Upon completing this course, students will be able to meet these objectives: Illustrate the hierarchical network design model and architecture using the access, distribution, and core layers. Compare and contrast the various hardware and software switching mechanisms and operation, while defining the TCAM and CAM, along with process switching, fast switching, and Cisco Express Forwarding concepts. Troubleshoot layer 2 connectivity using VLANs, trunking. Implementation of redundant switched networks using spanning tree protocol. Troubleshooting link aggregation using Etherchannel. Describe the features, metrics, and path selection concepts of EIGRP. Implementation and optimization of OSPFv2 and OSPFv3, including adjacencies, packet types, and areas, summarization and route filtering for IPv4 and IPv6. Implementing EBGP interdomain routing, path selection and single and dual-homed networking. Implementing network redundacy using protocols like HSRP and VRRP. Implementing internet connectivity within Enterprise using static and dynamic NAT. Describe the virtualization technology of servers, switches, and the various network devices and components. Implementing overlay technologies like VRF, GRE, VPN and LISP. Describe the components and concepts of wireless networking including RF, antenna characteristics, and define the specific wireless standards. Describe the various wireless deployment models available, include autonomous AP deployments and cloud-based designs within the centralized Cisco WLC architecture. Describe wireless roaming and location services. Describe how APs communicate with WLCs to obtain software, configurations, and centralized management. Configure and verify EAP, WebAuth, and PSK wireless client authentication on a WLC. Troubleshoot wireless client connectivity issues using various tools available. Troubleshooting Enterprise networks using services like NTP, SNMP , Cisco IOS IP SLAs, NetFlow and Cisco IOS Embedded Event Manager. Explain the use of available network analysis and troubleshooting tools, which include show and debug commands, as well as best practices in troubleshooting The Implementing and Operating Cisco Enterprise Network Core Technologies (ENCOR) v1.2 course provides the knowledge and skills needed to configure, troubleshoot, and manage enterprise wired and wireless networks. You?ll learn to implement security principles within an enterprise network and how to overlay network design by using solutions such as SD-Access and SD-WAN Course Outline Examining Cisco Enterprise Network Architecture Understanding Cisco Switching Paths Implementing Campus Lan Connectivity Building Redundant Switched Topology Implementing Layer 2 Port Aggregation Understanding EIGRP Implementing OSPF Optimizing OSPF Exploring EBGP Implementing Network Redundancy Implementing NAT Introducing Virtualization Protocols And Techniques Understanding Virtual Private Networks And Interfaces Understanding Wireless Principles Examining Wireless Deployment Options Understanding Wireless Roaming And Location Services Examining Wireless AP Operation Understanding Wireless Client Authentication Troubleshooting Wireless Client Connectivity Introducing Multicast Protocols Introducing QoS Implementing Network Services Using Network Analysis Tools Implementing Infrastructure Security Implementing Secure Access Control Understanding Enterprise Network Security Architecture Exploring Automation and Assurance Using Cisco DNA Center Examining the Cisco SD-Access Solution Understanding the Working Principles of the Cisco SD-WAN Solution Understanding the Basics of Python Programming Introducing Network Programmability Protocols Introducing APIs in Cisco DNA Center and vManage

Duration 3 Days 18 CPD hours This course is intended for This course is intended for: Database architects Database administrators Database developers Data analysts and scientists Overview This course is designed to teach you how to: Discuss the core concepts of data warehousing, and the intersection between data warehousing and big data solutions Launch an Amazon Redshift cluster and use the components, features, and functionality to implement a data warehouse in the cloud Use other AWS data and analytic services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3, to contribute to the data warehousing solution Architect the data warehouse Identify performance issues, optimize queries, and tune the database for better performance Use Amazon Redshift Spectrum to analyze data directly from an Amazon S3 bucket Use Amazon QuickSight to perform data analysis and visualization tasks against the data warehouse Data Warehousing on AWS introduces you to concepts, strategies, and best practices for designing a cloud-based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. This course demonstrates how to collect, store, and prepare data for the data warehouse by using other AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data Module 1: Introduction to Data Warehousing Relational databases Data warehousing concepts The intersection of data warehousing and big data Overview of data management in AWS Hands-on lab 1: Introduction to Amazon Redshift Module 2: Introduction to Amazon Redshift Conceptual overview Real-world use cases Hands-on lab 2: Launching an Amazon Redshift cluster Module 3: Launching clusters Building the cluster Connecting to the cluster Controlling access Database security Load data Hands-on lab 3: Optimizing database schemas Module 4: Designing the database schema Schemas and data types Columnar compression Data distribution styles Data sorting methods Module 5: Identifying data sources Data sources overview Amazon S3 Amazon DynamoDB Amazon EMR Amazon Kinesis Data Firehose AWS Lambda Database Loader for Amazon Redshift Hands-on lab 4: Loading real-time data into an Amazon Redshift database Module 6: Loading data Preparing Data Loading data using COPY Data Warehousing on AWS AWS Classroom Training Concurrent write operations Troubleshooting load issues Hands-on lab 5: Loading data with the COPY command Module 7: Writing queries and tuning for performance Amazon Redshift SQL User-Defined Functions (UDFs) Factors that affect query performance The EXPLAIN command and query plans Workload Management (WLM) Hands-on lab 6: Configuring workload management Module 8: Amazon Redshift Spectrum Amazon Redshift Spectrum Configuring data for Amazon Redshift Spectrum Amazon Redshift Spectrum Queries Hands-on lab 7: Using Amazon Redshift Spectrum Module 9: Maintaining clusters Audit logging Performance monitoring Events and notifications Lab 8: Auditing and monitoring clusters Resizing clusters Backing up and restoring clusters Resource tagging and limits and constraints Hands-on lab 9: Backing up, restoring and resizing clusters Module 10: Analyzing and visualizing data Power of visualizations Building dashboards Amazon QuickSight editions and feature