Kurs i Romedal

Understand the purpose and key concepts of the Service Desk practice, including how it serves as the central point of contact between the service provider and the users, facilitating effective communication. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content mobil-optimised practical exercises     Exam: 20 questions Multiple Choice 30 minutes Closed book Minimum required score to pass: (65%)   [-]

Spesialseminar 2: «Generasjonsskifter - utfordringer og løsninger»    Dette spesialseminaret er åpent og kostnadsfritt for alle som er med i styrenettverksgrupper arrangert av Styreforeningen. For andre deltakere som er medlemmer av Styreforeningen koster seminaret kr. 4.990,-.For deltakere som ikke er med i styrenettverksgrupper og som ikke er medlemmer av Styreforeningen er prisen kr. 5.990,-   Seminaret tar for seg en rekke krevende faser og situasjoner som kan oppstå i forbindelse med styrearbeidet. Vi ser blant annet på:   hva undersøkeler som er gjort omkring generasjonsskifteprosesser forteller oss uventet aksjonær bortgang og ulike utfordringer det kan medføre fremtidsfullmakt i forbindelse med aksjeeierskap og aksjeovertakels viktigheten av vedtektsbestemmelser eventuelt aksjonæravtaler ved generasjonsskifter, og utfordringene som kan oppstå dersom situasjonen ikke er hensynttat i formalia styrets rolle i generasjonsskifteprosesser forberedelsene og tilretteleggingen i generasjonsskifteprosesser sjekkliste ved generasjonsskifteprosesser [-]

Modulen dekker bruk og effekt av Lean og agile arbeidsmåter, og hvordan dette kan utnyttes til fordel for organisasjonen. Kurset vil gi kandidatene en praktisk og strategisk metode for å planlegge og levere kontinuerlig forbedring med nødvendig smidighet.  E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 12 moduler. Les mer om ITIL® 4 på AXELOS sine websider Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert.   [-]

AZ-1008: Administer Active Directory Domain Services [-]

I Matematikk R2 lærer du om integralregning, rekker, vektorer og trigonometri. Matematikk R2 er for deg som trenger realfagmatematikk for videre studier eller for å forbedre karakteren din. Faget er obligatorisk om du skal studere til ingeniør eller arkitekt. Matematikk R2 gir 1 realfagpoeng. Du må ha matematikk R1 for å ta dette faget. Gjennomføring NettstudierDu bestemmer hvor og når du vil lære. Her får du varierte leksjoner i form av tekster, video, quiz, podcast, veiledning og oppgaver. Du har alltid kontakt med din personlige lærer hos K2. Målet er å gjøre deg best mulig forberedt til eksamen. Eksamen Eksamen i Matematikk R2 er skriftlig. Som privatist må du selv melde deg opp til eksamen. Oppmeldingsfristene er normalt 15. september og 1. februar. Husk at betaling av eksamensavgiften skjer ved oppmelding. Veien videre Om du har generell studiekompetanse (GENS) og velger å ta fagene fysikk 1 og matematikk R1 og R2 som privatist, da oppfyller du opptakskravene til flere studier, inkludert ingeniørstudier. Se praktisk info for frister og opptak til universitet og høyskole. Gratis veiledning Er du usikker på hva som skal til for å få studiekompetanse, ta gjerne kontakt med oss for gratis veiledning. Vi har veiledere med mange års erfaring som står klare til å hjelpe deg!Ønsker du mer informasjon om kurset velg "Send meg info"-knappen under. Vil du chatte med oss, så klikk på ikonet nederst i høyre hjørne. Gratis veiledning Vi har veiledere med mange års erfaring som står klare til å hjelpe deg! Er du usikker på hva som skal til for å få studiekompetanse, ta gjerne kontakt med oss for gratis veiledning.  Ønsker du mer informasjon om kurset velg "Send meg info"-knappen under. Vil du chatte med oss, klikk på ikonet nederst i høyre hjørne. Lånekassestøtte Utdanningen er godkjent i lånekassen. Du søker direkte via lanekassen.no. Alt du må vite om lån og stipend fra Lånekassen som deltaker hos K2 utdanning Støtteordning Er du organisert i en fagforening, kan du i de fleste fagforeningene søke støtte til utdanning. Dersom du er organisert bør du sjekke med din fagforening om muligheter for støtte, frister og hvordan du søker. Forkunnskaper Du må ha fullført grunnskole eller tilsvarende opplæring. Minoritetsspråklige bør ha minimum B1-nivå i norsk muntlig og skriftlig. Dersom du har behov for å lære mere norsk før du starter på utdanning har vi norskkurs på forskjellig nivå (A1-B2). Språkkursene er digitale med personlig oppfølging fra lærer. Se alle norskkurs K2 tilbyr. Krav til utstyr Som deltaker hos K2 må du ha tilgang til pc på eksamen. I tillegg trenger du PC-versjonen av Office eller tilsvarende programmer. Se hva du har tilgang til av nettbaserte ressurser på eksamen. Praktisk info Du finner svar på ofte stilte spørsmål på nettsiden vår under praktisk info.     [-]

Check Point Certified Security Expert (CCSE) – R81.20 [-]

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     [-]

Kursene er inkludert lunsj! Kurset tar for seg: Praktisk bruk, kontroll og kursing av stropp/redskap Sikkerhetsbestemmelser for bruk av løfteinnretninger Signal og tegn Forskrifter: - Best nr.703 §10-3 og §10-2 Forskrift om utførelse av arbeid  Anhuking Kurset har en varighet på 24 undervisningstimer á 45 min, hvorav: 16 timer teori om løfteredskap 8 timer om sikkerhet, ansvar og kontroll Om kurset: (Dette kurset kommer også inn under de ulike krankursene.) Undervisningen er tilpasset de som har lese- og skrivevansker.     Kurspris eksl. modul 1.1 kr 3.850,-. Kurspris inkl. modul 1.1 kr. 4.950,-. Prisene er inkludert lunsj! Hvis man ikke har modul 1.1, er man nødt å ta den. Kurset vil bli avholdt i våre lokaler i spelhaugen 15, Bergen Vi arrangerer gjerne bedriftsintern opplæring. Nettbasert kurs holder vi også.   [-]

IJOS: Introduction to the Junos Operating System [-]

Cloud Operations on AWS [-]

Kvalitetsledelse (Quality Manager)  Dette kurset er for deg som vil ha mer dybdekunnskap, og/eller sertifisere deg som kvalitetsleder. Kurset tar for seg ISO 9001 da den ligger som en grunnsten for kvalitetsfaget, men vi skal også innom andre områder som er viktig for å få styringssystemer til å fungere i praksis. Pensumet til kurset er satt av den Europeiske foreningen EOQ, slik at alle som velger å sertifisere seg i Europa har samme minimums-kompetanse. Vi kommer til å følge et case gjennom kurset slik at du får mulighet til å trene på noen av teamene vi er innom. Gjennom hele kurset legges det opp til å gjøre innholdet enkelt og praktisk. Kvalitetsledelse handler om sunn fornuft satt i system.   Kursleder er Siri Mathiesen, se gjerne egen omtale om Siri på KRN Academy, www.qrn.no Vi som underviser hos KRN er personer med et sterkt engajsement for det vi underviser i, og det merkes når du er på kurs. Det gjør innholdet levende og interessant. Du kan gjerne få referanser fra oss.  [-]

Content:• Basic knowledge of the Working Environment Act• Roles and responsibilities• Duties of employer, employees and safety representatives• Internal control regulations• Systematic health, environment and safety work• Requirements regarding the working enviroment• Working environment cooperation• Risk assessment The Web-course includes answering questions and to execute an Risk Assessment  Calculated total time approx 40 Hours The course-access will be sendt by email when the course is signed up and paid for. Customer list: Bravida, Multiconsult and Moen Marin. [-]

  Med Content Marketing, på norsk, innholdsmarkedsføring vil du skape digitalt innhold som har en verdi for brukerne.  Det kan forklares ved at du ikke kun skal fortelle hvor billige møbler du selger, og at de må kjøpes nå, men at du gir tips og gode råd for hvordan stua kan møbleres eller hvordan møblene behandles osv. Du skal kunne lære bort, underholde og informere innenfor ditt fagområde, og dette skal igjen støtte opp om virksomhetens mål, enten det er ren informasjon eller salg av produkter og tjenester. Kursinnhold, Content Marketing: Hvordan skape verdifull informasjon, og ikke uinteressant reklame Lær å lage godt og relevant innhold til nyhetsbrev Hva er godt innhold på nettsidene Hvordan få kunder til å dele i sosiale medier Få bedre organisk synlighet på Google https://igm.no/content-marketing/   [-]

Through a combination of presentations, demos, and hands-on labs, participants learn how to use GCP services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications. Objectives This course teaches participants the following skills: Use best practices for application development Choose the appropriate data storage option for application data Implement federated identity management Develop loosely coupled application components or microservices Integrate application components and data sources Debug, trace, and monitor applications Perform repeatable deployments with containers and deployment services Choose the appropriate application runtime environment; use Google Container Engine as a runtime environment and later switch to a no-ops solution with Google App Engine Flex All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Best Practices for Application Development -Code and environment management-Design and development of secure, scalable, reliable, loosely coupled application components and microservices-Continuous integration and delivery-Re-architecting applications for the cloud Module 2: Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK -How to set up and use Google Cloud Client Libraries, Google Cloud SDK, and Google Firebase SDK-Lab: Set up Google Client Libraries, Google Cloud SDK, and Firebase SDK on a Linux instance and set up application credentials Module 3: Overview of Data Storage Options -Overview of options to store application data-Use cases for Google Cloud Storage, Google Cloud Datastore, Cloud Bigtable, Google Cloud SQL, and Cloud Spanner Module 4: Best Practices for Using Cloud Datastore -Best practices related to the following:-Queries-Built-in and composite indexes-Inserting and deleting data (batch operations)-Transactions-Error handling-Bulk-loading data into Cloud Datastore by using Google Cloud Dataflow-Lab: Store application data in Cloud Datastore Module 5: Performing Operations on Buckets and Objects -Operations that can be performed on buckets and objects-Consistency model-Error handling Module 6: Best Practices for Using Cloud Storage -Naming buckets for static websites and other uses-Naming objects (from an access distribution perspective)-Performance considerations-Setting up and debugging a CORS configuration on a bucket-Lab: Store files in Cloud Storage Module 7: Handling Authentication and Authorization -Cloud Identity and Access Management (IAM) roles and service accounts-User authentication by using Firebase Authentication-User authentication and authorization by using Cloud Identity-Aware Proxy-Lab: Authenticate users by using Firebase Authentication Module 8: Using Google Cloud Pub/Sub to Integrate Components of Your Application -Topics, publishers, and subscribers-Pull and push subscriptions-Use cases for Cloud Pub/Sub-Lab: Develop a backend service to process messages in a message queue Module 9: Adding Intelligence to Your Application -Overview of pre-trained machine learning APIs such as Cloud Vision API and Cloud Natural Language Processing API Module 10: Using Cloud Functions for Event-Driven Processing -Key concepts such as triggers, background functions, HTTP functions-Use cases-Developing and deploying functions-Logging, error reporting, and monitoring Module 11: Managing APIs with Google Cloud Endpoints -Open API deployment configuration-Lab: Deploy an API for your application Module 12: Deploying an Application by Using Google Cloud Build, Google Cloud Container Registry, and Google Cloud Deployment Manager -Creating and storing container images-Repeatable deployments with deployment configuration and templates-Lab: Use Deployment Manager to deploy a web application into Google App Engine flexible environment test and production environments Module 13: Execution Environments for Your Application -Considerations for choosing an execution environment for your application or service:-Google Compute Engine-Kubernetes Engine-App Engine flexible environment-Cloud Functions-Cloud Dataflow-Lab: Deploying your application on App Engine flexible environment Module 14: Debugging, Monitoring, and Tuning Performance by Using Google Stackdriver -Stackdriver Debugger-Stackdriver Error Reporting-Lab: Debugging an application error by using Stackdriver Debugger and Error Reporting-Stackdriver Logging-Key concepts related to Stackdriver Trace and Stackdriver Monitoring.-Lab: Use Stackdriver Monitoring and Stackdriver Trace to trace a request across services, observe, and optimize performance [-]

Etter 3 dager med kurs vil du bli løftet opp på et helt nytt nivå. Du vil kunne kvalitetssikre ditt arbeid og bruke mindre tid på å løse dine arbeidsoppgaver. Du vil garantert merke stor forskjell når du er tilbake på jobb! Kurset passer for deg som har basisferdighetene på plass men som ønsker å lære mer. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. Endre innstillingene i Excel for å få brukergrensesnittet du ønsker Kvalitetssikre regnearkene dine og unngå feil input gjennom validering Beskytt regneark mot å bli ødelagt ved feil bruk og feil lagring Betinget formatering gjør det enkelt å følge med sentrale verdier i regnearket. Bruk flere arbeidsbøker samtidig og utvid mulighetene dine Sortering og filtrering gjør arbeidet med lister og tabeller enkelt og effektivt. Bruk av funksjoner for å dra ut ønsket data fra en celle eller område Pivottabeller og pivotdiagram kan brukes for å trekke ut og vise data på en oversiktlig måte. Verktøy for analyse av data gjør deg i stand til å løse avanserte hva skjer hvis-spørsmål. Legg inn knapper/kontroller for å gjøre det enda lettere å bruke regnearkene dine Deling av arbeidsbøker gjør det lett å samarbeide med andre kollegaer. Innspilling av makroer sikrer konsekvent og korrekt databehandling Lag makroer ved å skrive programkoden selv (VBA) I tillegg får du en rekke tips og triks du kan bruke i din arbeidsdag. Alt du lærer får du repetert gjennom aktiv oppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert! Kursholderne har mer enn 20 års Excel erfaring som de gjerne deler med deg! «Fikk veldig mye ny kunnskap på relativt kort tid. Har blitt mye mer bevisst på hva Excel kan brukes til og det er mye mer enn jeg først trodde. Veldig god kursleder» Maria Amundsen, Jernbaneverkets Fellestjenester Kursinnhold Slik kan du kvalitetssikre regnearkmodellene dine - Effektiv og nyttig validering sikrer mot feil input- Beskytt regneark og bok mot å bli ødelagt ved feil bruk Lær deg å bygge gode og effektive formler med - Riktig bruk av cellereferanser- Navning av celler- Nyttige tekstformler- Smarte, innebyggede funksjoner- Å lage egne funksjoner for mer kompliserte formler som du ofte anvender Lær deg de smarte triksene du trenger til å arbeide med flere ark - Enkel kopiering av ark- Formler som summerer data fra flere ark- Hvordan du kan spare tid ved å arbeide på flere ark samtidig Slik bruker du flere Excel-bøker samtidig og utvider mulighetene dine - Riktig bruk av cellereferanser til annen bok, lær om fallgrubene og hvordan du unngår dem- Lær hvordan du setter opp og bruker hyperkoblinger til å hoppe mellom deler av prosjektet ditt- Lær om hvordan du lager dynamiske koblinger mellom Excel og andre programmer Smart bruk av Excel-maler gjør deg mer effektiv - Lær å lage, bruke og endre maler Når du vil koble Excel til bedriftens database-system - Forstå grunnprinsippene for en database- Lær hvordan du automatisk trekker data ut fra databasen og får dem skrevet inn i regnearket Slik analyserer du store datamengder på en effektiv og enkel måte - Lær deg riktig og god bruk av verktøyet Pivot- Lag sammendrag av dataene dine akkurat slik du ønsker- Lag pivot-tabeller basert direkte på bedriftens database Lær deg de nyttige og gode verktøyene for behandling av lister i Excel - Bruk av det nye, flotte verktøyet ’Tabell’- Forskjellige måter å sortere lister på- Hvordan du bruker filter for å plukke ut poster fra en liste- Hvordan du kan sette inn mellomsummer i listene dine Slik kan du forbedre brukervennligheten av regnearkene dine - Sett opp smarte kontroller som gjør det lettere for ukyndige brukere å anvende regneark-applikasjonene din- Lær deg å bruke validering til innskriving av lange tekster i celler Ta det store skrittet: lær deg effektiv og riktig makroprogrammering - Bruk av makroer kan gjøre dine Excel-applikasjoner raskere, enklere å bruke og sikrere- Makroinnspilleren hjelper deg til å lage flotte, nyttige og effektive makroer uten at du trenger å kunne programmering- Gå videre: lær deg også å forstå hemmeligheten ved programmering slik at du kan skrive programkoden selv. [-]