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

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 [-]

Synes du det er uoversiktlig å samarbeide om dokumenter med andre? Vi lærer deg de viktigste funksjonene og metodene for vellykket dokument-samhandling og ferdigstilling av dokumenter. 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:   Samarbeidsfunksjoner Spor endringer for å se hvem som har gjort hva Bruk av merknader Sammenligne dokumenter Passordbeskytte dokumenter   Filer i SharePoint, OneDrive for Business og OneDrive Samtidigredigering Versjonering   Før publisering Fjerne skjulte data Fjerne personlig informasjon   3 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Gratis support [-]

JOBB SMART OG EFFEKTIVT! E-læringskurset gir deg en opplevelsesrik og praktisk opplæring i e-postprogrammet Outlook 2016. En kombinasjon av videoer, teori, oppskrifter, oppgaver og tester gjør det enkelt å lære seg de nye funksjonene og verktøyene. E-læringskurset inneholder 46 opplæringsvideoer. E-læringskurset er tilpasset Office 365. * Office 365 gir deg alltid den nyeste versjonen av Outlook. * Navigasjonsfeltet gjør det enkelt å bytte mellom visningene i Outlook 2016. * Egen modus som er optimalisert for berøring. * Microsoft-kontoen kobler enheten til OneDrive, slik at du alltid har tilgang til filene dine. * Enklere søk etter kommandoer, handlinger og hjelp. * Mulighet for automatisk komprimering av store bildevedlegg. * Den første linjen i meldingsteksten vises som standard i meldingslisten og gir god oversikt. * Enklere filtrering av uleste meldinger. * Et integrert utskriftsmiljø med både utskriftsinnstillinger og forhåndsvisning. * Svarknapper gjør det enkelt å svare på eller videresende meldinger direkte fra leseruten. * Enklere sortering og gruppering av meldinger. * Hurtigtrinn kan brukes for å utføre flere handlinger samtidig. * Automatiske svar håndterer meldinger mens du er borte fra kontoret. * Møteinvitasjoner kan opprettes direkte basert på en melding. *Møteinvitasjoner viser et bilde av kalenderen, slik at du kan sjekke om du har ledig tid. * Personkortet i Outlook 2016 inneholder alle viktige detaljer om en kontakt samlet på ett sted. INNHOLDSFORTEGNELSE FØR DU STARTER Hva er Office 365? Elektronisk post Programvinduet Berøringsmodus Visninger Microsoft-konto Hjelp SENDING AV MELDINGER Nettetikette Sending av meldinger Meldingsformater Vedlegg Signatur Viktighet Svarknapper Leverings- og lesebekreftelse BEHANDLING AV MELDINGER Sending og mottak av meldinger Meldingslisten Bildenedlasting Forhåndsvisning og utskrift av meldinger Flagg for oppfølging Svar og videresending Sortering av meldinger Kolonner Direktesøk Organisering av meldinger Hurtigtrinn Søppelpost Automatiske svar KALENDER Kalenderen Avtaler Hendelser Møter Møtesvar Forhåndsvisning og utskrift av kalenderen Værdata PERSONER Kontakter Kontaktgrupper Forhåndsvisning og utskrift av kontakter OPPGAVER OG NOTATER Oppgaver Notater [-]

E-læringskurset gir deg en opplevelsesrik og praktisk opplæring i tekstbehandlingsprogrammet Word 2016. En kombinasjon av videoer, teori, oppskrifter, oppgaver og tester gjør det enkelt å lære seg de nye funksjonene og verktøyene. En komplett e-læring med både grunnleggende og videregående emner. E-læringskurset inneholder 79 opplæringsvideoer.E-læringskurset er tilpasset Office 365.Testene i e-læringskurset måler kunnskap før, under og etter opplæringen. Når ettertesten er bestått får du tilgang til et kursbevis i PDF-format som enkelt kan lagres eller skrives ut.Jobb smart og effektivt!- Office 365 gir deg alltid den nyeste versjonen av Word.- Maler er tilgjengelig ved oppstart.- Enklere åpning og lagring av dokumenter.- Microsoft-kontoen kobler enheten til OneDrive, slik at du alltid har tilgang til filene dine.- Egen modus som er optimalisert for berøring.- Enklere søk etter kommandoer, handlinger og hjelp.- Et integrert utskriftsmiljø med både utskriftsinnstillinger og forhåndsvisning.- Du kan konvertere et PDF-dokument til et redigerbart Word-dokument.- Med formatering av tegn og avsnitt setter du ditt personlige preg på dine dokumenter.- Stiler effektiviserer formateringsarbeidet og gjør formateringen konsekvent.- Med verktøy for sideformatering kan du tilpasse papirretning, marger og topp- og bunntekst.- Bruk av tema gir en konsekvent layout på alle Office-dokumenter.- Stave- og grammatikkontrollen luker ut de fleste stavefeilene i dokumentet.- Synonymordboka gjør det enklere å variere og forbedre språket.- Tabeller kan brukes for å presentere informasjon på en oversiktlig måte.- Med tabellstiler går det raskt å formatere tabeller med et profesjonelt utseende.- Frisk opp dokumentet med illustrasjoner som bilder, figurer, WordArt og SmartArt-grafikk.- Enklere å finne og sette inn bilder fra både datamaskinen, OneDrive og en kilde på Internett.- Figurene kan brukes til å tegne linjer, rektangler, piler, stjerner og flytskjema.- Objekter tilpasses ved å endre plassering, størrelse, rotering, rekkefølge etc.- WordArt kan brukes for å lage spesielle teksteffekter i et dokument.- SmartArt-grafikk kan brukes til å lage flotte illustrasjoner.- Diagram egner seg godt for å gi et visuelt, lettfattelig inntrykk av tallverdier.- Maler brukes for å sikre at dokumenter av samme type får en ensartet formatering.- Innholdskontroller kan settes inn for å effektivisere bruken av maler.- Med utskriftsfletting kan du masseprodusere brev, e-postmeldinger, konvolutter og etiketter.Innhold:- Før du starter- Redigering- Formatering- Sideformatering- Språkverktøy- Tabeller- Illustrasjoner- Maler og skjema- Fletting [-]

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   [-]

På webinaret vil vi vise deg hvordan du kan lage nettbaserte spørreundersøkelse, tester og påmeldingsskjemaer ved hjelp av Microsoft Forms. Microsoft Forms er en enkel og elegant app i Microsoft 365 familien for opprettelse av undersøkelser og tester. Du kan lage skjema med flere språk i samme skjema. Du kan ha forgreninger til ulike svarretninger alt etter hva som velges som svar. Det er mange ulike spørsmålsalternativer å velge mellom. Svarene kan være anonyme om ønskelig. Du kan også sette inn undersøkelser (poll) i et Teams-møte eller som en del av en presentasjon i PowerPoint. Resultatene behandler og analyserer du enkelt i Excel. Hva er Forms | Forskjell undersøkelser og tester | Personlige skjema vs gruppeskjema | Opprette skjema | Spørsmålstyper | Forgreninger | Innstillinger | Flere språk i samme skjema | Simulere skjema | Delingsmåter (samle inn svar) | Samarbeide om samme skjema eller duplisere skjema (gi kopi til andre) | Resultater og analyser | Forms og Teams | Forms og PowerPoint Pris: 1690 kroner [-]

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

. This course includes identity content for Azure AD, enterprise application registration, conditional access, identity governance, and other identity tools.   TARGET AUDIENCE This course is for the Identity and Access Administrators who are planning to take the associated certification exam, or who are performing identity and access administration tasks in their day-to-day job. This course would also be helpful to an administrator or engineer that wants to specialize in providing identity solutions and access management systems for Azure-based solutions; playing an integral role in protecting an organization. COURSE OBJECTIVES Implement an identity management solution Implement an authentication and access management solutions Implement access management for apps Plan and implement an identity governancy strategy COURSE CONTENT Module 1: Implement an identity management solution Learn to create and manage your initial Azure Active Directory (Azure AD) implementation and configure the users, groups, and external identities you will use to run your solution. Lessons M1 Implement Initial configuration of Azure AD Create, configure, and manage identities Implement and manage external identities Implement and manage hybrid identity Lab 1a: Manage user roles Lab 1b: Setting tenant-wide properties Lab 1c: Assign licenses to users Lab 1d: Restore or remove deleted users Lab 1e: Add groups in Azure AD Lab 1f: Change group license assignments Lab 1g: Change user license assignments Lab 1h: Configure external collaboration Lab 1i: Add guest users to the directory Lab 1j: Explore dynamic groups After completing module 1, students will be able to: Deploy an initail Azure AD with custom settings Manage both internal and external identities Implement a hybrid identity solution Module 2: Implement an authentication and access management solution Implement and administer your access management using Azure AD. Use MFA, conditional access, and identity protection to manager your identity solution. Lessons M2 Secure Azure AD user with MFA Manage user authentication Plan, implement, and administer conditional access Manage Azure AD identity protection Lab 2a: Enable Azure AD MFA Lab 2b: Configure and deploy self-service password reset (SSPR) Lab 2c: Work with security defaults Lab 2d: Implement conditional access policies, roles, and assignments Lab 2e: Configure authentication session controls Lab 2f: Manage Azure AD smart lockout values Lab 2g: Enable sign-in risk policy Lab 2h: Configure Azure AD MFA authentication registration policy After completing module 2, students will be able to: Configure and manage user authentication including MFA Control access to resources using conditional access Use Azure AD Identity Protection to protect your organization Module 3: Implement access management for Apps Explore how applications can and should be added to your identity and access solution with application registration in Azure AD. Lessons M3 Plan and design the integration of enterprise for SSO Implement and monitor the integration of enterprise apps for SSO Implement app registration Lab 3a: Implement access management for apps Lab 3b: Create a custom role to management app registration Lab 3c: Register an application Lab 3d: Grant tenant-wide admin consent to an application Lab 3e: Add app roles to applications and recieve tokens After completing module 3, students will be able to: Register a new application to your Azure AD Plan and implement SSO for enterprise application Monitor and maintain enterprise applications Module 4: Plan and implement an identity governancy strategy Design and implement identity governance for your identity solution using entitlement, access reviews, privileged access, and monitoring your Azure Active Directory (Azure AD). Lessons M4 Plan and implement entitlement management Plan, implement, and manage access reviews Plan and implement privileged access Monitor and maintain Azure AD Lab 4a: Creat and manage a resource catalog with Azure AD entitlement Lab 4b: Add terms of use acceptance report Lab 4c: Manage the lifecycle of external users with Azure AD identity governance Lab 4d: Create access reviews for groups and apps Lab 4e: Configure PIM for Azure AD roles Lab 4f: Assign Azure AD role in PIM Lab 4g: Assign Azure resource roles in PIM Lab 4h: Connect data from Azure AD to Azure Sentinel After completing module 4, students will be able to: Mange and maintain Azure AD from creation to solution Use access reviews to maintain your Azure AD Grant access to users with entitlement management [-]

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     [-]

. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The course also covers monitoring, troubleshooting, and optimizing Azure solutions.   TARGET AUDIENCE Students in this course are interested in Azure development or in passing the Microsoft Azure Developer Associate certification exam.   COURSE CONTENT Module 1: Creating Azure App Service Web Apps Students will learn how to build a web application on the Azure App Service platform. They will learn how the platform functions and how to create, configure, scale, secure, and deploy to the App Service platform. Azure App Service core concepts Creating an Azure App Service Web App Configuring and Monitoring App Service apps Scaling App Service apps Azure App Service staging environments Module 2: Implement Azure functions This module covers creating Functions apps, and how to integrate triggers and inputs/outputs in to the app. Azure Functions overview Developing Azure Functions Implement Durable Functions Module 3: Develop solutions that use blob storage Students will learn how Azure Blob storage works, how to manage data through the hot/cold/archive blob storage lifecycle, and how to use the Azure Blob storage client library to manage data and metadata. Azure Blob storage core concepts Managing the Azure Blob storage lifecycle Working with Azure Blob storage Module 4: Develop solutions that use Cosmos DB storage Students will learn how Cosmos DB is structured and how data consistency is managed. Students will also learn how to create Cosmos DB accounts and create databases, containers, and items by using a mix of the Azure Portal and the .NET SDK. Azure Cosmos DB overview Azure Cosmos DB data structure Working with Azure Cosmos DB resources and data Module 5: Implement IaaS solutions This module instructs students on how to use create VMs and container images to use in their solutions. It covers creating VMs, using ARM templates to automate resource deployment, create and manage Docker images, publishing an image to the Azure Container Registry, and running a container in Azure Container Instances. Provisioning VMs in Azure Create and deploy ARM templates Create container images for solutions Publish a container image to Azure Container Registry Create and run container images in Azure Container Instances Module 6: Implement user authentication and authorization Students will learn how to leverage the Microsoft Identity Platform v2.0 to manage authentication and access to resources. Students will also learn how to use the Microsoft Authentication Library and Microsoft Graph to authenticate a user and retrieve information stored in Azure, and how and when to use Shared Access Signatures. Microsoft Identity Platform v2.0 Authentication using the Microsoft Authentication Library Using Microsoft Graph Authorizing data operations in Azure Storage Module 7: Implement secure cloud solutions This module covers how to secure the information (keys, secrets, certificates) an application uses to access resources. It also covers securing application configuration information. Manage keys, secrets, and certificates by using the KeyVault API Implement Managed Identities for Azure resources Secure app configuration data by using Azure App Configuration Module 8: Implement API Management Students will learn how to publish APIs, create policies to manage information shared through the API, and to manage access to their APIs by using the Azure API Management service. API Management overview Defining policies for APIs Securing your APIs Module 9: Develop App Service Logic Apps This module teaches students how to use Azure Logic Apps to schedule, automate, and orchestrate tasks, business processes, workflows, and services across enterprises or organizations. Azure Logic Apps overview Creating custom connectors for Logic Apps Module 10: Develop event-based solutions Students will learn how to build applications with event-based architectures. Implement solutions that use Azure Event Grid Implement solutions that use Azure Event Hubs Implement solutions that use Azure Notification Hubs Module 11: Develop message-based solutions Students will learn how to build applications with message-based architectures. Implement solutions that use Azure Service Bus Implement solutions that use Azure Queue Storage queues Module 12: Monitor and optimize Azure solutions This module teaches students how to instrument their code for telemetry and how to analyze and troubleshoot their apps. Overview of monitoring in Azure Instrument an app for monitoring Analyzing and troubleshooting apps Implement code that handles transient faults Module 13: Integrate caching and content delivery within solutions Students will learn how to use different caching services to improve the performance of their apps. Develop for Azure Cache for Redis Develop for storage on CDNs [-]

Tekst er ikke alltid best egnet til å kommunisere ditt budskap. Dette webinaret viser deg hvordan du enkelt og effektivt benytter figurer, smart art modeller, diagrammer, bilder og video. Du får en rekke tips som vil bidra til at du sparer mye tid.  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:   Innsetting av ulike objekter Figurer og SmartArt Bilder Video - ha kontroll på avspilling   Bruk av diagrammer Koblinger til Excel Håndtere koblinger   Håndtering av objekter Justere og fordele Fordeler og ulemper ved gruppering   3 gode grunner til å delta 1. Lær og justere og fordele objekter effektivt 2. Lag figurmodeller raskt og enkelt 3. Ha kontroll på koblede objekter   [-]

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. After completing this course, students will be able to: 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 prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.Recommended prerequisites:M-DP900 - Microsoft Azure Data FundamentalsM-AZ900 - Microsoft Azure Fundamentals Agenda 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. 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. 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. 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). 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Module 16: Build reports using Power BI integration with Azure Synapase 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. 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. [-]

Behersk kunsten å skape overbevisende presentasjoner med "PowerPoint: Komplett", et omfattende kurs ledet av Espen Faugstad hos Utdannet.no. Microsoft PowerPoint er en vital del av moderne kommunikasjon, brukt utstrakt i både næringslivet og utdanning. Dette kurset er designet for å gi deg fullstendig mestring av PowerPoint 2019, uansett om du er nybegynner eller ønsker å forbedre dine eksisterende ferdigheter. Kurset starter med grunnleggende, som å kjøpe og installere PowerPoint, og lærer deg å navigere i brukergrensesnittet. Du vil lære å lage presentasjoner som kombinerer tekst, bilder og illustrasjoner. Videre lærer kurset deg hvordan du kan lage din egen PowerPoint-mal, og bruker overganger og animasjoner for å gi presentasjonen din liv. Du vil også bli veiledet gjennom prosessen med å ferdigstille og holde en effektiv presentasjon. Ved kursets slutt vil du være i stand til å skape profesjonelle presentasjoner som engasjerer og informerer ditt publikum. Du vil ha en dyp forståelse av alle aspekter av PowerPoint, fra å lage innhold til å dele og presentere det på en effektiv måte.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Ny presentasjon Kapittel 3: Tekst Kapittel 4: Bilder Kapittel 5: Illustrasjoner Kapittel 6: Media Kapittel 7: Lysbildemal Kapittel 8: Overganger og animasjoner Kapittel 9: Ferdigstill presentasjon Kapittel 10: Start presentasjon Kapittel 11: Del presentasjon Kapittel 12: Avslutning   Varighet: 3 timer og 29 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. [-]

UTDANNINGSMÅLKursdeltakerne skal etter endt kurs kunne bruke AutoCAD til å: Opprette nye tegninger Gjøre forandringer i eksisterende tegninger Bruke og forstå de vanligste tegne- og editeringskommandoer Målsette og påføre tekst i en tegning Bruke og forstå lagoppbyggingen i AutoCAD Symbol- og blokkhåndtering Enkel layout/plotting Lagre tegninger KURSINNHOLD: Introduksjon av AutoCAD 2-dimensjonal tegning Zoom og skjermbehandlingsteknikk Redigeringsfunksjoner Målsettingsfunksjoner Laghåndtering Symbol- og blokkhåndtering Hente symboler og parametere (målsettings- og tekststiler, osv.) fra eksisterende tegninger Layout/plotting av tegninger Lagrings- og avslutningsrutiner [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Windows server 2008/2012 - god kjennskap om Windows server Innleveringer: Øvinger: 8 av må være godkjent. Personlig veileder: ja Vurderingsform: Eksamen blir arrangert som 2 dagers hjemmeeksamen (start kl 09.00 og innlevering kl 15.00 dagen etter). Hver student får tildelt et virtuelt område. Det skal også leveres en skriftelig begrunnelse for de valg som er foretatt. Hjemmeeksamen, individuell, 2 dager, 0 Ansvarlig: Stein Meisingseth Eksamensdato: 10.12.13 / 13.05.14         Læremål: KUNNSKAPER:Kandidaten:- har innsikt i drift av nettverk basert på Windows Server, programvaredistribusjon og kjenner til hvilke verktøy som kan brukes for administrasjon av virtuelle maskiner og nettverk- kan forklare systemer som kan benyttes til overvåkning og vedlikehold FERDIGHETER:Kandidaten kan:- installere og konfigurere System Center Configuration Manager 2012- automatisere manuelle operasjoner- sikre, oppdatere og overvåke IT-systemer GENERELL KOMPETANSE:Kandidaten har:- perspektiv og kompetanse i å velge riktige og tilpassete driftsløsninger- kompetanse i å formidle driftsterminologi, både muntlig og skriftlig Innhold:- Automatisering og sikring ved hjelp av System Center Cooperation Manager 2012 (SCCM 2012) - Applikasjonsutrulling - Operativ System utrulling - Klient tilstands-monitorering - Programvare oppdateringer - Sikkerhetsbeskyttelse vha Endpoint ProtectionLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Microsoft System Center i overvåkning og drift 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]