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

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

Excelkurs Basis - elæringskurs [-]

Bruker du mye tid i Excel på å få gjort enkle arbeidsoppgaver? Kommer det til stadighet prosent og dato i celler hvor du vil ha vanlige tall? Blir en formel ødelagt når du flytter den? Er det vanskelig å lage det diagrammet du ønsker? Blir ikke utskriftene dine slik du ønsker? Dette er vanlige problemstillinger mange sliter med og som blir borte etter endt kurs! På kun 2 dager vil du mestre de vanligste formler og funksjoner du trenger i din arbeidsdag. Du lærer gode rutiner og hurtigtastene du trenger for å kunne arbeide raskt og effektivt. Du vil kunne bygge alt fra enkle til mer avanserte modeller og vil føle deg trygg på at modellen din virker og gir rett resultat. Du vil også få 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!   Kurset passer for deg med liten erfaring og som ønsker å lære Excel fra grunnen av. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. Meld deg på Excel-kurs allerede i dag og sikre deg plass!   Krav til forkunnskaper Grunnleggende kunnskaper i Windows.   Kursinnhold Redigering Merking Sletting Angre muligheter Flytting og kopiering Innsetting og sletting Formler Bruk av formler Autofyll Cellereferanser Formatering Hva er formatering? Kolonnebredde og radhøyde Tallformatering Skriftformatering Justering av celleinnhold Kantlinjer og fyllfarger Betinget formatering Funksjoner Bruk av funksjoner Summering Minst, størst, antall og gjennomsnitt Hvis-funksjonen Betinget summering Diagram Utforming av diagram Diagramtyper Flere regneark Arbeid med regneark Innsetting og sletting av regneark Flytting og kopiering av regneark Referering til andre regneark Enkle formler på tvers av ark Vindus håndtering Lister og tabeller Sortering Tabeller Filtrering Deling og frysing av vindu   [-]

Kursinnhold Gjennomgang av Excel vinduet Enkle formler Enkel formatering Klipp og lim Kopiering av formler Merking Slette data Fjerne og legge til celler, rader og kolonner Angre Flytting og kopiering Søk og erstatt Autofyll Cellereferanser Låse og gi navn til celler Hva er en funksjon? Funksjonsveiviseren Gjennomgang av de mest brukte funksjonene: Summer, antall, størst, min og gjennomsnitt. Målgruppe Deg som Har begynt i en stilling hvor en er forventet å kunne Excel Er nysgjerrig på hva Excel kan gjøre for deg i din jobb Er nybegynner eller litt øvet Sliter med å skjønne hvordan du kan jobbe mest effektivt i Excel Forkunnskaper Excel: Ingen Øvrig: Er kjent med bruk av PC Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer. Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

AI-900: Microsoft Azure AI Fundamentals [-]

Developing on AWS [-]

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     [-]

Microsoft Project 365 er et av verdens mest brukte verktøy for planlegging og oppfølging av prosjekter. Dette kurset lærer deg å få kontroll på aktiviteter, ressurser, kostnader og tidsbruk. Du lærer hvordan du kan ta ut fremdriftsplaner, lage flotte rapporter og hvordan du kan gjøre nytte av Project’s automatikk for å løse opp i problemstillinger med overforbruk av tid, ressurser og kostnader.   Kursinstruktør Geir Johan Gylseth Geir Johan Gylseth er utdannet ved Universitetet i Oslo med hovedvekt på Informatikk og har over 30 års erfaring som instruktør. Geir sin styrke ligger innenfor MS Office. Han har lang erfaring med skreddersøm av kurs, kursmanualer og oppgaver. Geir er en entusiastisk og dyktig instruktør som får meget gode evalueringer.   Kursinnhold Skriver du inn start- og sluttdato i tabell-delen og opplever at du ikke klarer å re-planlegge? Får du ikke korrekt antall timer på aktivitetene dine? Er det vanskelig å få kostnadene i Project til å stemme overens med de virkelige? Bruker du andre programmer til å lage skikkelige fremdriftsplaner? Er det vanskelig å lage forståelige rapporter? Er det vanskelig å få korrekte start-dato på aktivitetene dine? Blir hele prosjektet forskjøvet i tid når du bare gjør en liten endring?   Dette er vanlige problemstillinger mange sliter med og som blir borte etter endt kurs! På kun 2 dager vil du mestre de vanligste arbeidsoppgavene i Project. Du lærer gode rutiner og hurtigtastene du trenger for å kunne arbeide rasktog effektivt. Du vil føle deg trygg på at det er du som kontrollerer Project og ikke omvendt! Du vil også få 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! NB: Ta med egen PC    Dag 1 Dag 1 bruker vi mest tid på aktiviteter og på å sy dem sammen til en god plan. På slutten av dagen ser vi på ressurser og kostnader og hvordan de kan håndteres av Project. Bli kjent med Project Oppstart og avslutning Åpning, lagring og lukking Visninger Nytt prosjekt Kalenderalternativer Aktiviteter Manuell planlegging Registrering av aktiviteter Redigering av aktiviteter Disposisjon Kobling av aktiviteter Tidsforskyvning Tidsbetingelser Ressurser og Kostnader Ressursliste Ressurskostnader Tildeling av ressurser Innsatsdrevet planlegging Aktivitetstyper Ressurskonflikter Faste kostnader Kostnadsinformasjon   Dag 2 Dag 2 begynner vi med repetisjon av dag 1 og deretter tar vi for oss hva som skjer når prosjektet ruller og går: Baseline, oppfølging, rapportering og hvordan få prosjektet på sporet igjen. Vi ser også på tilpasning av Project til den enkeltes behov.   Repetisjonsoppgave Oppfølging Referanseplaner Den kritiske linjen Faktiske opplysninger Sammenligning Framdriftslinje Justering av prosjektplanen Presentasjon Utskrift Rapporter Visuelle rapporter Formatering Tegning Tidslinjeverktøy Hyperkoblinger Kopiere bilder av visninger Tilpasninger Tabeller Sortering og gruppering Filtrering Egendefinerte visninger Delprosjekter WBS-nummerering   Meld deg på Project-kurs allerede i dag og sikre deg plass!   Hilsen fra fornøyde deltagere: "Bra og oversiktlig kurs. Ting som har vært uklart i forhold til "MSP" blir belyst, og man forstår logikken i programmet. Man forstår med andre ord funksjonaliteten i programmet gjennom kurset".Ken Inge Bavda- ENI Norge   "Bra og forståelig gjennomgang av innholdet. Passe fart på læringen. Flink kursinstruktør".Trude Sundblad- EVRY AS   "Kurset var lærerikt og informativt. Kursleder var faglig dyktig og hadde godt humør. Jeg hadde to lærerike dager".Christopher Rustad- Opak AS   "Føler at jeg har et godt utgangspunkt for å komme i gang med å bruke programmet. godt utgangspunkt, fornøyd med kurset, det svarte til mine forventninger".Jane-Britt Berntsen- Norwex Holding AS   [-]

Dette kurset tilbys som bedriftsinternt kurs   Workshopen består av et minimum med teori og et maksimum av praktiske øvelser hvor vi lager streams av  Arrays, List, Set, Map og Files - filtrerer, mapper til nye objekter, utfører aggregeringer og konverterer tilbake til nye collections mm.   Workshopen vil dekke bl.a. Sette opp en stream, med Stream.of(), IntStream.of() og DoubleStream.of() Konvertere et Array til en stream med Arrays.stream() Konvertere en collection av typen List, Set eller Map til en stream med stream() Filtrere ut verdier med filter() Mappe til nye objekter med map() og flatMap() Sortere med sorted() og ulike typer Comparators Aggregere med reduce() og collect() Behandle hvert element med forEach() og forEachOrdered() Gruppere og telle opp forekomster i hver gruppe med collect() Konvertere tilbake til en collection med collect() Konvertere til et objekt med get() Begrense reultatet med limit() Hente enkel statistikk (min, max, average, sum) med reduce() og collect() og bl.a. summarizingInt() Bruke :: til metodereferanser Lese en fil inn i en stream med Files.lines() Behandle hvert element med forEach() og forEachOrdered() Workshopen holdes på norsk og går over 2 dager, fra 10.00-14.00, for tiden online, med dedikert lærer og Microsoft Teams som kommunikasjonsplattform.   [-]

Fleksible kurs for fremtiden Ny kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   Best practice i Part og Assemblie, tips og triks Ulike metoder på Skjellett-modellering. (Derived VS Multibody) Hvordan bruke Parametere Frame generator Flere Assemblie-features, Design View/ Level of Detail En kjapp innføring i iLogic Kurset vil også i en viss grad bli styrt ut ifra deltagernes spørsmål og ønsker   Her er et utvalg av temaene du vil lære på kurset: Intelligente konstruksjoner Sammenstillingsfunksjoner Konstruksjonsverktøyer Etter endt kurs vil du ha kunnskap og forståelse for oppbygning av definerte mekaniske konstruksjoner med Inventors avanserte designverktøy som f.eks. rammegenerator.Dessuten oppnår du en grundig kunnskap om håndtering og oppbygning av skjelettmodeller med bruk av ”Multi Bodies”.Du lærer å utnytte funksjoner som kan effektivisere håndteringen av samlinger.   Tilpassete kurs for bedrifter Vi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

AZ-2003: Deploy cloud-native apps using Azure Container Apps [-]

This course teaches IT Professionals how to manage their Azure subscriptions, secure identities, administer the infrastructure, configure virtual networking, connect Azure and on-premises sites, manage network traffic, implement storage solutions, create and scale virtual machines, implement web apps and containers, back up and share data, and monitor your solution.   TARGET AUDIENCE This course is for Azure Administrators. The Azure Administrator implements, manages, and monitors identity, governance, storage, compute, and virtual networks in a cloud environment. The Azure Administrator will provision, size, monitor, and adjust resources as appropriate. COURSE OBJECTIVES After completing this course you should be able to: Secure and manage identities with Azure Active Directory. Implement and manage users and groups. Implement and manage Azure subscriptions and accounts. Implement Azure Policy, including custom policies. Use RBAC to assign permissions. Leverage Azure Resource Manager to organize resources. Use the Azure Portal and Cloud Shell. Use Azure PowerShell and CLI. Use ARM Templates to deploy resources. Implement virtual networks and subnets. Configure public and private IP addressing. Configure network security groups. Configure Azure Firewall. Configure private and public DNS zones Configure VNet Peering. Configure VPN gateways. Choose the appropriate intersite connectivity solution. Configure network routing including custom routes and service endpoints. Configure an Azure Load Balancer. Configure and Azure Application Gateway. Choose the appropriate network traffic solution. Create Azure storage accounts. Configure blob containers. Secure Azure storage. Configure Azure files shares and file sync. Manage storage with tools such as Storage Explorer Plan for virtual machine implementations. Create virtual machines. Configure virtual machine availability, including scale sets. Use virtual machine extensions. Create an app service plan. Create a web app. Implement Azure Container Instances. Implement Azure Kubernetes Service. Backup and restore file and folders. Backup and restore virtual machines. Use Azure Monitor. Create Azure alerts. Query using Log Analytics. Use Network Watcher.   COURSE CONTENT   Module 1: Identity Azure Active Directory Users and Groups Lab : Manage Azure Active Directory Identities Module 2: Governance and Compliance Subscriptions and Accounts Azure Policy Role-based Access Control (RBAC) Lab : Manage Subscriptions and RBAC Lab : Manage Governance via Azure Policy Module 3: Azure Administration Azure Resource Manager Azure Portal and Cloud Shell Azure PowerShell and CLI ARM Templates Lab : Manage Azure resources by Using the Azure Portal Lab : Manage Azure resources by Using ARM Templates Lab : Manage Azure resources by Using Azure PowerShell Lab : Manage Azure resources by Using Azure CLI Module 4: Virtual Networking Virtual Networks IP Addressing Network Security groups Azure Firewall Azure DNS Lab : Implement Virtual Networking Module 5: Intersite Connectivity VNet Peering VPN Gateway Connections ExpressRoute and Virtual WAN Lab : Implement Intersite Connectivity Module 6: Network Traffic Management Network Routing and Endpoints Azure Load Balancer Azure Application Gateway Traffic Manager Lab : Implement Traffic Management Module 7: Azure Storage Storage Accounts Blob Storage Storage Security Azure Files and File Sync Managing Storage Lab : Manage Azure storage Module 8: Azure Virtual Machines Virtual Machine Planning Creating Virtual Machines Virtual Machine Availability Virtual Machine Extensions Lab : Manage virtual machines Module 9: Serverless Computing Azure App Service Plans Azure App Service Container Services Azure Kubernetes Service Lab : Implement Web Apps Lab : Implement Azure Container Instances Lab : Implement Azure Kubernetes Service Module 10: Data Protection File and Folder Backups Virtual Machine Backups Lab : Implement Data Protection Module 11: Monitoring Azure Monitor Azure Alerts Log Analytics Network Watcher Lab : Implement Monitoring     [-]

ENCOR: Implementing and Operating Cisco Enterprise Network Core Technologies [-]

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   [-]

Develop dynamic reports with Microsoft Power BI [-]