Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Bærum

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher slik at du kan ta sertifiseringstesten for eksempel hjemme eller på jobb. Denne vil være gyldig i ett år. Tid for sertifiseringstest avtales som beskrevet i e-post med voucher. Eksamen overvåkes av en web-basert eksamensvakt.   Eksamen er på engelsk. Eksamensformen er multiple choice - 50 spørsmål skal besvares, og du består ved 50% korrekte svar (dvs 25 av 50 spørsmål). Deltakerne har 40 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.   Nødvendige forkunnskaper: Ingen [-]

The course combines lecture with case studies to demonstrate basic architect design principles. Successful students have experience and knowledge in IT operations, including networking, virtualization, identity, security, business continuity, disaster recovery, data platforms, and governance. Students also have experience designing and architecting solutions. COURSE OBJECTIVES Skills gained Design a governance solution. Design a compute solution. Design an application architecture. COURSE CONTENT Module 1: Design compute and application solutions In this module you will learn about governance, compute, and application architectures. Lessons of Module 1 Design for governance Design for compute solutions Design for application architectures Lab : Case studies of Module 1 After completing this module, students will be able to: Design a governance solution. Design a compute solution. Design an application architecture. Module 2: Design storage solutions In this module, you will learn about non-relational storage, relational storage, and data integration solutions. Lessons of Module 2 Design a non-relational storage solution. Design a relational storage solution. Design a data integration solution. Lab : Case studies of Module 2 After completing this module, students will be able to: Design non-relational storage solutions. Design relational storage solutions. Design a data integration solution. Module 3: Design networking and access solutions In this module you will learn about authentication and authorization, identity and access for applications, and networking solutions. Lessons of Module 3 Design authentication and authorization solutions Design networking solutions Lab : Case studies of Module 3 After completing this module, students will be able to: Design authentication and authorization solutions. Design network solutions. Module 4: Design business continuity solutions Lessons of Module 4 Design for backup and disaster recovery Design monitoring solutions Design for migrations Lab : Case studies of Module 4 After completing this module, students will be able to: Design backup and disaster recovery. Design monitoring solutions. Design for migrations. [-]

COURSE OVERVIEW Students will begin by understanding the core compute and storage technologies that are used to build an analytical solution. They will then explore how to design an analytical serving layers and focus on data engineering considerations for working with source files. The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. TARGET AUDIENCE The primary audience for this course is data professionals, data architects, and business intelligence professionals who want to learn about data engineering and building analytical solutions using data platform technologies that exist on Microsoft Azure. The secondary audience for this course data analysts and data scientists who work with analytical solutions built on Microsoft Azure. COURSE OBJECTIVES   Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics COURSE CONTENT Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Introduction to Azure Synapse Analytics Describe Azure Databricks Introduction to Azure Data Lake storage Describe Delta Lake architecture Work with data streams by using Azure Stream Analytics Lab 1: Explore compute and storage options for data engineering workloads Combine streaming and batch processing with a single pipeline Organize the data lake into levels of file transformation Index data lake storage for query and workload acceleration After completing module 1, students will be able to: Describe Azure Synapse Analytics Describe Azure Databricks Describe Azure Data Lake storage Describe Delta Lake architecture Describe Azure Stream Analytics Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Design a multidimensional schema to optimize analytical workloads Code-free transformation at scale with Azure Data Factory Populate slowly changing dimensions in Azure Synapse Analytics pipelines Lab 2: Designing and Implementing the Serving Layer Design a star schema for analytical workloads Populate slowly changing dimensions with Azure Data Factory and mapping data flows After completing module 2, students will be able to: Design a star schema for analytical workloads Populate a slowly changing dimensions with Azure Data Factory and mapping data flows Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Lab 3: Data engineering considerations Managing files in an Azure data lake Securing files stored in an Azure data lake After completing module 3, students will be able to: Design a Modern Data Warehouse using Azure Synapse Analytics Secure a data warehouse in Azure Synapse Analytics Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Explore Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Lab 4: Run interactive queries using serverless SQL pools Query Parquet data with serverless SQL pools Create external tables for Parquet and CSV files Create views with serverless SQL pools Secure access to data in a data lake when using serverless SQL pools Configure data lake security using Role-Based Access Control (RBAC) and Access Control List After completing module 4, students will be able to: Understand Azure Synapse serverless SQL pools capabilities Query data in the lake using Azure Synapse serverless SQL pools Create metadata objects in Azure Synapse serverless SQL pools Secure data and manage users in Azure Synapse serverless SQL pools Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Understand big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Lab 5: Explore, transform, and load data into the Data Warehouse using Apache Spark Perform Data Exploration in Synapse Studio Ingest data with Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Spark pools in Azure Synapse Analytics Integrate SQL and Spark pools in Azure Synapse Analytics After completing module 5, students will be able to: Describe big data engineering with Apache Spark in Azure Synapse Analytics Ingest data with Apache Spark notebooks in Azure Synapse Analytics Transform data with DataFrames in Apache Spark Pools in Azure Synapse Analytics Integrate SQL and Apache Spark pools in Azure Synapse Analytics Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Lab 6: Data Exploration and Transformation in Azure Databricks Use DataFrames in Azure Databricks to explore and filter data Cache a DataFrame for faster subsequent queries Remove duplicate data Manipulate date/time values Remove and rename DataFrame columns Aggregate data stored in a DataFrame After completing module 6, students will be able to: Describe Azure Databricks Read and write data in Azure Databricks Work with DataFrames in Azure Databricks Work with DataFrames advanced methods in Azure Databricks Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Lab 7: Ingest and load Data into the Data Warehouse Perform petabyte-scale ingestion with Azure Synapse Pipelines Import data with PolyBase and COPY using T-SQL Use data loading best practices in Azure Synapse Analytics After completing module 7, students will be able to: Use data loading best practices in Azure Synapse Analytics Petabyte-scale ingestion with Azure Data Factory Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Data integration with Azure Data Factory or Azure Synapse Pipelines Code-free transformation at scale with Azure Data Factory or Azure Synapse Pipelines Lab 8: Transform Data with Azure Data Factory or Azure Synapse Pipelines Execute code-free transformations at scale with Azure Synapse Pipelines Create data pipeline to import poorly formatted CSV files Create Mapping Data Flows After completing module 8, students will be able to: Perform data integration with Azure Data Factory Perform code-free transformation at scale with Azure Data Factory Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Orchestrate data movement and transformation in Azure Data Factory Lab 9: Orchestrate data movement and transformation in Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines After completing module 9, students will be able to: Orchestrate data movement and transformation in Azure Synapse Pipelines Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Lab 10: Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Understand developer features of Azure Synapse Analytics Optimize data warehouse query performance in Azure Synapse Analytics Improve query performance After completing module 10, students will be able to: Optimize data warehouse query performance in Azure Synapse Analytics Understand data warehouse developer features of Azure Synapse Analytics Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Analyze and optimize data warehouse storage in Azure Synapse Analytics Lab 11: Analyze and Optimize Data Warehouse Storage Check for skewed data and space usage Understand column store storage details Study the impact of materialized views Explore rules for minimally logged operations After completing module 11, students will be able to: Analyze and optimize data warehouse storage in Azure Synapse Analytics Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark pools Query Azure Cosmos DB with serverless SQL pools Lab 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Synapse Analytics Query Azure Cosmos DB with serverless SQL pool for Azure Synapse Analytics After completing module 12, students will be able to: Design hybrid transactional and analytical processing using Azure Synapse Analytics Configure Azure Synapse Link with Azure Cosmos DB Query Azure Cosmos DB with Apache Spark for Azure Synapse Analytics Query Azure Cosmos DB with SQL serverless for Azure Synapse Analytics Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Lab 13: End-to-end security with Azure Synapse Analytics Secure Azure Synapse Analytics supporting infrastructure Secure the Azure Synapse Analytics workspace and managed services Secure Azure Synapse Analytics workspace data After completing module 13, students will be able to: Secure a data warehouse in Azure Synapse Analytics Configure and manage secrets in Azure Key Vault Implement compliance controls for sensitive data Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Lab 14: Real-time Stream Processing with Stream Analytics Use Stream Analytics to process real-time data from Event Hubs Use Stream Analytics windowing functions to build aggregates and output to Synapse Analytics Scale the Azure Stream Analytics job to increase throughput through partitioning Repartition the stream input to optimize parallelization After completing module 14, students will be able to: Enable reliable messaging for Big Data applications using Azure Event Hubs Work with data streams by using Azure Stream Analytics Ingest data streams with Azure Stream Analytics Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Process streaming data with Azure Databricks structured streaming Lab 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks Explore key features and uses of Structured Streaming Stream data from a file and write it out to a distributed file system Use sliding windows to aggregate over chunks of data rather than all data Apply watermarking to remove stale data Connect to Event Hubs read and write streams After completing module 15, students will be able to: Process streaming data with Azure Databricks structured streaming Module 16: Build reports using Power BI integration with Azure Synpase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Create reports with Power BI using its integration with Azure Synapse Analytics Lab 16: Build reports using Power BI integration with Azure Synpase Analytics Integrate an Azure Synapse workspace and Power BI Optimize integration with Power BI Improve query performance with materialized views and result-set caching Visualize data with SQL serverless and create a Power BI report After completing module 16, students will be able to: Create reports with Power BI using its integration with Azure Synapse Analytics Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. Use the integrated machine learning process in Azure Synapse Analytics Lab 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics Create an Azure Machine Learning linked service Trigger an Auto ML experiment using data from a Spark table Enrich data using trained models Serve prediction results using Power BI After completing module 17, students will be able to: Use the integrated machine learning process in Azure Synapse Analytics     [-]

Due to the Coronavirus the course instructor is not able to come to Oslo. As an alternative we offer this course as a Blended Virtual Course.   Blended Virtual CourseThe course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams, you will receive a link and can access the course using the web browser.    Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture    The courseThis course provides essential skills for all mobile application testers. A mobile application is a software application designed to run on mobile devices such as smartphones and tablet computers. Mobile applications are becoming part of our every day lives and many organisations are developing applications to run on mobile devices to complement their desktop applications. This course will give the mobile application tester the knowledge skills needed to test mobile applications and to understand the differences and similarities in testing mobile applications to conventional applications. This course is highly practical - providing the participant with different mobile applications to test using a variety of techniques and tactics in order to find those mobile application bugs.The pre-requisite in receiving this qualification is that you have have attained the ISTQB Foundation.   The examThe Foundation Level Mobile Application Testing exam is comprised of 40 multiple-choice questions, with a pass mark grade of 65% to be completed within 60 minutes. Participants that take the exam not in their spoken language, will receive additional 25% time, for a total of 75 minutes. [-]

Dette er et spesialkurs som fokuserer på analyse av store datasett ved hjelp av Pivottabell og Power Pivot, samt formelbasert analyse. Målet er å få frem styrker og svakheter ved de forskjellige metodene, og å se litt på hvilke forutsetninger som påvirker valg av løsning. For å ha utbytte av dette kurser forutsettes at man er vant bruker av Excel. Pivot og Power Pivot blir gjennomgått fra begynnelsen, så man trenger ikke være kjent med disse verktøyene fra før. Betingede formler kan være ganske krevende, så det er en fordel å være litt trygg på formelskriving. I en kurssituasjon blir selvsagt kurset tilpasset deltagernes nivå og forkunnskaper. I kurset gjennomgås bl.a.: Kontroll/gjennomgang av en del sentral funksjonalitet – bl.a. absolutte, relative og blandede referanser. Sammendrag av data fra flere ark i samme eller flere arbeidsbøker, bl.a. gjennomgående summering og tabulering v.hj.a. INDIREKTE-funksjonen. Betingende sammendrag v.hj.a. matriseformler og funksjoner Modifisere datasett med FINN.RAD, FINN.KOLONNE, matriseformler og andre teknikker Pivottabell, hvor vi bl.a. ser på: Sette sammen data fra forskjellige grunnlag før pivotering Vise dataserie på forskjellige måter (sum, gjennomsnitt, prosentfordelt, etc.) Hvordan foreta beregninger rett i pivottabellen, f.ex. inntekter – kostnader = resultat Pivottabell hvor datagrunnlaget er oppdelt i flere forskjellige Pivottabell rett mot en spørring i en database Power Pivot Forskjeller (og likheter) med «vanlig» Pivottabell Når forlater vi den vanlige pivottabellen til fordel for Power Pivot? Fordeler og ulemper med Pivot og Power Pivot. Lage Power Pivot-tabell med data fra flere forskjellige datasett. [-]

Etter gjennomført kurs skal kursdeltagerne bla. kunne bruke Inventor til å:• Lage modeller• Generere tegninger ut i fra modell• Lage sammenstillinger• Utføre de vanligste tegne- og editeringsfunksjoner• Målsette og påføre tekstKursinnhold:• Grunnleggende begrep og arbeidsmetoder • Parametrisk part-modellering• Arbeide med skisser• Features• Arbeide med sammenstillinger• 2D-layout - oppsett og innstillinger• Generere 2D-tegninger fra modell• Målsetting og innsetting av stykklister [-]

Lær å lage nettsider med WordPress Dette kurset passer for deg som skal designe eller bruke nettsider laget med WordPress og som trenger å forstå hvordan det virker. Du lærer også hvordan du forandrer og tilpasser designmaler, bruker utvidelser, knytter nettsiden mot sosiale medier, og hvordan du søkemotoroptimaliserer nettsidene slik at du blir funnet av brukerene. WordPress er en publiseringsløsning som gjør det enkelt å lage profesjonelle nettsider og det har den fordelen at kunden kan oppdatere og legge inn eget innhold. Dette er verdens største publiseringsverktøy og det finnes utallige designmaler som gjør det mulig for deg å endre utseende på nettsiden uten å endre innholdet. Dette lærer du: Bli kjent med løsningen Opprette nettsider på eget domene Du lærer å lage nettsider ved hjelp av ferdige maler Endre designmaler Publisere nettsider og innlegg Legge inn bilder Gjøre nettsiden din søkbar Bruk av plugins og widgets slik som f. eks: deling i sosiale medier, slideshow, kalenderfunksjon og mye mer Enkel bilde­redigering i WordPress https://igm.no/wordpress-kurs/ [-]

  Webinaret passer for deg som har de grunnleggende ferdighetene i Excel, men som vil lære mer om verktøyene i programmet. På kurset vil vi vise deg hvordan du kan jobbe mer effektivt ved å ta i bruk flere verktøy som ligger i systemet. Målet er at du etter endt kurs skal kunne jobbe raskere og mer effektivt når du produserer regneark. Temaer på webinaret: Datatyper i Excel Autofyll og serier Formatering og betinget formatering Formler med absolutte, relative og blandede referanser Bruk av navn i formler Noen praktiske funksjoner Lister og tabeller med sortering og filtrering Bygge diagram raskt og enkelt   Pris: 1600 kroner (Ansatte og studenter ved UiS har egne betingelser)   [-]

MB-220: Dynamics 365 Customer Insights - Journeys [-]

Blended Virtual Course The course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. You do not have to install Microsoft Teams , you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture  This 3-day course is aimed at anyone wishing to attain the ISTQB Advanced Test Automation Engineer qualification. This qualification builds upon the Foundation syllabus and provides essential skills for all those involved in test automation and who want to develop further their expertise in one or more specific areas. Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt A Test Automation Engineer is one who has broad knowledge of testing in general, and an in-depth understanding in the special area of test automation. An in-depth understanding is defined as having sufficient knowledge of test automation theory and practice to be able to influence the direction that an organization and/or project takes when designing, developing and maintaining test automation solutions for functional tests. The modules offered at the Advanced Level Specialist cover a wide range of testing topics.   The course is highly practical addressing the following areas: Introduction and objectives for Test Automation This section provides an introduction to test automation explaining the objectives, advantages, disadvantages and limitations of test automation as well as technical success factors of a test automation project. Preparing for Test Automation Understanding the type of system is vital for determining the most appropriate automation solution and also how we can design systems and testing for more effective automation. This section also looks at how we can evaluate for the most appropriate tools. The generic Test Automation architecture A test automation engineer has the role of designing, developing, implementing, and maintaining test automation solutions. As each solution is developed, similar tasks need to be done, similar questions need to be answered, and similar issues need to be addressed and prioritized. These reoccurring concepts, steps, and approaches in automating testing become the basis of the generic test automation architecture, and this will be discussed in detail during this section Deployment risks and contingencies This section looks at the various risks associated with the deployment of test tools and how to avoid test automation failure. Test Automation reporting and metrics Providing information to stakeholders for them to make informed decisions about the quality of the software is a vital part of testing and this section looks at the various metrics that can be used to monitor test automation and what information should be supplied to the stakeholder and how it should be presented. Transitioning manual testing to an automated environment This section looks at the various criteria to apply to determine the suitability for automation and understanding the factors for transitioning from manual to automation testing Verifying the Test Automation solution To have justified confidence in the information we supply to the stakeholders regarding test automation we must have justified confidence in the test environment and test automation solution supporting the information Continuous improvement This section looks ahead and how we can improve the automation solution making it more effective and efficient The Exam The ISTQB Advanced Test Automation Engineer exam is a 1 hour 30 minute, 40 question multiple-choice exam totaling 75 points. The pass mark is 65% (49 out of 75). It is a pre-requisite that attendees hold the ISTQB Foundation Level certificate. [-]

AZ-900: Microsoft Azure Fundamentals [-]

  Jobber du med store datamengder? Vil du få kontroll over dataene dine? Har du problemer med utskrift fra Excel? Her vil du få kjennskap til en rekke gode metoder for å jobbe med lister.  Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Prinsipper for å arbeide med lister  Få med deg råd og regler som gjelder for et godt grunnlag   Effektiv merking og navigasjon   Flere måter å sortere grunnlaget på  Sortering etter verdier Sortering etter cellefarge, skriftfarge og celleikon Sortering etter egendefinert liste   Delsammendrag  Lag enkle rapporter ved å bruke delsammendrag verktøyet Kopiere delsammendrag   Filtrering  Se hvordan du finner relevante data i et stort grunnlag Filtrering etter farge og ikon   Fryse første rad og første kolonne     Skjule / vise rader og kolonner     Utskriftinnstillinger  Tilpass utskrift til en side Gjenta rader eller kolonner ved utskrift av flere sider Tilpass utskriftområdet     [-]

Ønsker du kontroll på ressursbruken din? Planlegg med ressurser og få en oversikt over hvor mange ressurser du trenger til enhver tid. Du kan også få en veldig god oversikt over økonomien i prosjektet.  Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause.  Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Hvilke typer ressurser har man tilgang på i Project Arbeidsressurser. Hvordan definere og bruke disse. Forskjell mellom generiske og personlige ressurser Materiellkostnader, hvordan benytte seg av dette i Project Hvordan sette opp kostnader   Ressursallokering i prosjektet Legge til, fjerne og endre ressurser Forskjellen mellom innsatsdreven og ikke innsatsdreven aktivitet Håndtere overallokeringer - hva skjer og hvordan få ressursplanlegging på plass   3 gode grunner til å delta 1. Få en oversikt over ressursbruk 2. Planlegg for bedre ressursbruk 3. Du får kontroll på utgiftene i prosjektet ditt   [-]

COURSE OVERVIEW You’ll learn how to implement and secure a wireless network infrastructure and use Cisco Identity Service Engine (ISE), Cisco Prime Infrastructure (PI), and Cisco Connect Mobile Experience to monitor and troubleshoot network issues.   The course provides hands-on labs to reinforce concepts including deploying Cisco Prime Infrastructure Release 3.5, Cisco Catalyst 9800 Wireless Controller Release IOS XE Gibraltar 16.10, Cisco Digital Network Architecture (DNA) Center Release 1.2.8, Cisco CMX Release 10.5, Cisco MSE Release 8.0 features and Cisco Identity Services Engine (ISE) Release 2.4.   This course also helps you prepare to take the Implementing Cisco Enterprise Wireless Networks (300-430 ENWLSI) exam, which is part of the new CCNP Enterprise certification. Passing the exam will also provide you with the Cisco Certified Specialist - Enterprise Wireless Implementation certification.   TARGET AUDIENCE Individuals needing to understand how to implement, secure and troubleshoot a Cisco Enterprise Wireless Network.   COURSE OBJECTIVES After completing this course you should be able to: Implement network settings to provide a secure wireless network infrastructure Troubleshoot security issues as it relates to the wireless network infrastructure Implement a secure wireless client and troubleshoot wireless client connectivity issues Implement and troubleshoot QoS in wireless networks Implement and troubleshoot advanced capabilities in wireless network services   COURSE CONTENT Securing and Troubleshooting the Wireless Network Infrastructure Implement Secure Access to the WLCs and Access Points Configure the Network for Access Point 802.1X Authentication Use Cisco DNA Center for Controller and AP Auto Install Implement Cisco Prime Infrastructure Define Network Troubleshooting Techniques Troubleshoot Access Point Join Issues Monitor the Wireless Network Implementing and Troubleshooting Secure Client Connectivity Configure the Cisco WLC for Wireless Client 802.1x Authentication Configure the Wireless Client for 802.1X Authentication Configure a Wireless LAN for FlexConnect Implement Guest Services in the Wireless Network Configure the Cisco WLC for Centralized Web Authentication Configure Central Web Authentication on Cisco ISE Implement BYOD Implement Location-Aware Guest Services Troubleshoot Client Connectivity Describe Issues that Affect Client Performance Monitor Wireless Clients Implementing and Troubleshooting QoS in Wireless Networks Implement QoS in the Wireless Network Configure the Cisco WLC to Support Voice Traffic Optimize Wireless Utilization on the Cisco WLC Implement Cisco AVC in the Wireless Network Implement Multicast Services Implement mDNS Service Implement Cisco Media Stream Troubleshoot QoS Issues in the Wireless Network Troublehoot mDNS Issues Troubleshoot Media Stream Issues Implementing and Troubleshooting Advanced Wireless Network Services Implement Base Location Services on Cisco Prime Infrastructure Implement Hyperlocation in the Wireless Network Implement Detect and Locate Services on Cisco CMX Implement Analytics on Cisco CMX Implement Presence Services on Cisco CMX Monitor and Locate Rogue Devices with Cisco Prime Infrastructure and Cisco CMX Monitor and Detect Wireless Clients with Cisco CMX and Cisco DNA Center Run Analytics on Wireless Clients Troubleshoot Location Accuracy with Cisco Hyperlocation Monitor and Manage RF Interferers on the Cisco WLC Monitor and Manager RF Interferers on Cisco Prime Infrastructure and Cisco CMX Labs Lab Familiarization (Base Learning Lab) Configure Secure Management Access for WLCs and APs Add Network Devices and External Resources to Cisco Prime Infrastructure Capture a Successful AP Authentication Implement AAA Services for Central Mode WLANs Implement AAA Services for FlexConnect Mode WLANs Configure Guest Services in the Wireless Network Configure BYOD in the Wireless Network Capture a Successful Client Authentications Configure QoS in the Wireless Network for Voice and Video Services Configure Cisco AVC in the Wireless Network Capture Successful QoS Traffic Marking in the Wireless Network Configure Detect and Locate Services on the Cisco CMX Identify Wireless Clients and Security Threats [-]

Bli en ekspert i verdens ledende programvare for digital bildebehandling og grafisk design med kurset "Photoshop: Komplett". Ledet av sertifisert Photoshop-ekspert Espen Faugstad hos Utdannet.no, er dette kurset perfekt for alle som ønsker å utforske og mestre Adobe Photoshop, et verktøy sentralt i nesten alle kreative prosjekter. Dette omfattende kurset tar deg gjennom alle aspekter av Photoshop, fra grunnleggende til avanserte teknikker. Du vil lære alt fra å åpne og håndtere dokumenter, jobbe med lag, utføre markeringer og beskjæringer, til avansert retusjering og redigering. Kurset dekker også bruk av justeringer, masker, effekter, blend modes og filtre. Med praktiske prosjekter og eksempler vil du utvikle ferdigheter som gjør deg i stand til å løse komplekse og kreative utfordringer, og ved kursets slutt vil du ha oppnådd en dyptgående forståelse og kompetanse i Photoshop. Dette kurset vil utruste deg med kunnskapen og ferdighetene som trengs for å utnytte Photoshop i full skala, enten for personlig bruk eller i en profesjonell sammenheng.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Åpne Kapittel 3: Dokument Kapittel 4: Image Kapittel 5: Layers Kapittel 6: Markere Kapittel 7: Beskjære Kapittel 8: Retusjere Kapittel 9: Verktøy Kapittel 10: Adjustments Kapittel 11: Masker Kapittel 12: Effekter Kapittel 13: Blend Modes Kapittel 14: Filter Kapittel 15: Prosjekter Kapittel 16: Eksportere Kapittel 17: Avslutning   Varighet: 8 timer og 59 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]