Oversikt over relevante kurs innen IT-kurs i Aurskog Høland

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 - 40 spørsmål skal besvares, og du består med 70% riktige svar (dvs. 28 av 40). Deltakerne har 1 time og 30 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.  Nødvendige forkunnskaper: Bestått ITIL Foundation sertifisering Gjennomført godkjent kurs/e-læring [-]

Throughout four years of teaching my High-Performance Java Persistence course, I came to realize that there is so much Java developers can learn about the latest SQL features introduced by Oracle, SQL Server, PostgreSQL, or MySQL.This training spans over the course of 2 days and covers the Top 4 relational database systems: Oracle, SQL Server, PostgreSQL, and MySQL.From execution plans to the best way to paginate data, this training explains lesser-known techniques such as LATERAL JOIN, CROSS APPLY, as well as Derived Tables, Common Table Expressions, recursive queries, and the amazing Window Functions, PIVOT, or UPSERT statements.Last but not least, we are going to learn that, not only modern databases support JSON column types, but you can combine JSON structures with the traditional relational ones, therefore getting the best of both worlds.All examples are inspired by real-life scenarios, and they come in a GitHub repository for which attendees have exclusive and unlimited time access.At the end of these two days of training, the attendees will be better prepared to solve various data-intensive tasks using all these awesome SQL features that have been over the past 20 years.Agenda  Day 1Introduction - 1h 30m    - Beyond SQL:92    - SQL Parsing    - SQL Operation Order    - TOP-N queries    - OFFSET pagination    - Keyset PaginationSubqueries - 1h 15m    - EXISTS and NOT EXISTS    - IN and NOT IN    - ANY and ALL    - INSERT with subqueries    - Aggregation with subqueries   Joins - 1h 15m    - CROSS JOIN    - INNER and LEFT/RIGHT OUTER JOIN    - FULL OUTER JOIN    - NATURAL JOIN    - LATERAL JOIN and CROSS APPLYDay 2Window Functions - 1h 30m    - Analytics queries and window frame processing    - ROW_NUMBER, RANK, and DENSE_RANK    - FIRST_VALUE, LAST_VALUE, LEAD and LAG    - CUME_DIST and PERCENT_RANK    - PERCENTILE_DISC and PERCENTILE_CONTDerived Tables, CTE, Hierarchical Queries - 1h 30m    - Derived Tables    - CTE (Common Table Expressions)    - Recursive CTE    - Hierarchical queries   PIVOT, UNPIVOT, FILTER, and CASE - 1h    - CASE Expressions    - PostgreSQL FILTER Expressions    - PIVOT    - UNPIVOTDay 3UPSERT and MERGE - 30m- MERGE statements- UPSERT statements   JSON processing - 1h 30m    - Schemaless data structures and JSON    - JSON queries    - EAV Model   Transactions and Concurrency Control - 2h    - ACID, 2PL, MVCC    - Isolation Levels and anomalies    - Pessimistic and optimistic locking    - SKIP_LOCKED, NOWAIT [-]

MS-100: Microsoft 365 Identity and Services [-]

Elasticsearch [-]

Kursinnhold: De ulike begrepene som blir brukt i Google Analytics Segmentering av brukere i statistikken Hvordan lese relevant statistikk Hva du kan bruke tallene til videre i din markedsføring Hvordan nettsidene dine fungerer og hvor konverteringene kommer fra [-]

Vil du lære å tegne illustrasjoner og logoer til bruk i alle medier? Illustrator tegner vektorgrafikk som kan forstørres ubegrenset, uten å tape kvalitet og kan derfor brukes overalt. Adobe Illustrator er verktøyet for illustratører og grafiske designere, men også et program for deg som vil lage litt enklere illustrasjoner til internett, Power Point og Word. På kurset lærer du å ta utgangspunkt i enkle basisformer og kombinere dem til kompliserte figurer, slik at det blir det lett for alle å tegne. Hvorfor ta dette kurset: Du får en grundig innføring i programmet Du vil lære konkrete tegne- og designoppgaver Du vil lære å redigere/endre Illustrator-filer du mottar Du vil lære å lage illustrasjoner og logoer Du vil lære å lage grafikk for bruk på internett, lesebrett eller mobil Du vil lære effektive arbeidsmetoder Du får kontroll på tegninger med mange elementer og lag Du vil lære om fargebruk og klargjøring av filer for trykk og nett Dette lærer du: Arbeidsmiljøet i programmet Tegning med tegneverktøyene og ved å kombinere enkle grunnformer Redigering og transformering av objekter Innsetting av tekst og bilder Tekstbearbeiding Lage bannerannonser Bruk av farger og forløpninger Lag og gjennomsiktighet [-]

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 [-]

Introduksjon til VBA   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. [-]

TOGAF® EA Course Combined [-]

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

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. [-]

Med Power Automate kan du automatisere forretningsprosesser og handlinger på tvers av organisasjonen, med lite eller ingen koding. Ta farvel med kjedelige, repetitive oppgaver og effektiviser hverdagen. Ikke minst er Power Automate ofte en del av Microsoft 365 lisensen du kanskje allerede har. Power Automate er Microsoft sin løsning for automatisering av prosesser, og er en tjeneste som lar deg utvikle flyter på tvers av en rekke applikasjoner og tjenester med lite eller ingen koding. Du kan selvfølgelig få tjenestene i Microsoft 365 til å snakke sammen slik du vil, men det finnes også flere hundre koblinger til andre eksterne tjenester. I tillegg har du naturligvis mulighet til å benytte generelle tilkoblinger, for å hente data fra egne APIer, databaser og tjenester. Power Automate gir muligheter til brukere på tvers av organisasjonen som tidligere i stor grad har vært forbeholdt utviklere.  I løpet av kurset vil deltagere få en hands-on opplevelse av hva Power Automate er, hva det kan brukes til, og hvordan en kan jobbe med det. Kursholderen vil gjøre deltakerne godt kjent med terminologien, demonstrere løsninger og utfordre med øvelser.  Dette er et introduksjonskurs, så det er naturligvis mye vi ikke vil rekke å gå gjennom. Kursleder vil peke deltagerne til gode kilder for videre læring. Det er også mulig å be om bedriftsinterne kurs på videregående nivå, der man kan spesifisere ønsket fokus og spesifikke behov. Disse kan også kjøres som workshops.   TA MED EGEN PC   Kursinnhold Power Automate - det store bildet Ulike flyttyper Bli kjent med arbeidsflaten Datakilder og koblinger Beste praksis for navngivning, utvikling, dokumentering m.m. Bruksområder og viktige begrensninger   [-]

Fleksible kurs for fremtidenNy 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.   AutoCAD og LT Basis I Her er et utvalg av temaene du vil lære på kurset: AutoCADs brukergrensesnitt Den strategiske bruken av programmet fra start til slutt i tegningsprosessen Utføre og modifisere en tegning Bruk av tegneobjekter Bruk av redigeringsverktøy Laghåndtering, bruk av tekst og målsetting Håndtere layout og plotte den ferdige tegningen Etter kurset vil du ha god kjennskap til AutoCADs brukerflate og hvordan du skal bruke programmet gjennom hele tegneprosessen. Du vil kunne utføre og modifisere en tegning når det gjelder bruk av tegneobjekter, bruk av redigeringsverktøy, laghåndtering, tekstbruk og målsetning samt opprettelse og bruk av blokker. Du vil også kunne håndtere layout og plotte den ferdige tegningen. Dette er et populært kurs, meld deg på nå!   Tilpassete kurs for bedrifterVi 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 [-]

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

Excel Videregående [-]