Kurs i programvare og applikasjoner

PowerPoint 2010 er et presentasjonsprogram som brukes når vi skal vise fram data – enten det er tekst, bilder, tall eller tegninger. Programmet kan brukes til å lage lysark som skrives ut, eller vi kan vise presentasjonen ved hjelp av PC + videokanon. På kurset vil grunnleggende funksjoner vektlegges, men vi vil og se på hvordan en bygger opp og setter sammen en presentasjon. Forkunnskaper: Du må ha kunnskaper tilsvarende PC-begynnerkurs. Brukere av Powerpoint 2007 kan og følge dette kurset. [-]

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

Innhold og nivå tilpasses ønsker og behov hos oppdragsgiver. Se beskrivelse av grunnleggende Excel, videregående Excel, Excel for controllere, Analyse med Pivottabeller og Power Pivot for eksempler på innhold. Ta kontakt så tilpasser vi innhold til ditt behov. [-]

  An Azure IoT Developer is responsible for implementing and then maintaining the cloud and edge portions of an Azure IoT solution. In addition to configuring and maintaining devices by using Azure IoT services and other Microsoft tools, the IoT Developer also sets up the physical devices and is responsible for maintaining the devices throughout the life cycle. The IoT Developer implements designs for IoT solutions, including device topology, connectivity, debugging and security. For Edge device scenarios, the IoT Developer also deploys compute/containers and configures device networking, which could include various edge gateway implementations. The IoT Developer implements designs for solutions to manage data pipelines, including monitoring and data transformation as it relates to IoT. The IoT Developer works with data engineers and other stakeholders to ensure successful business integration. IoT Developers should have a good understanding of Azure services, including data storage options, data analysis, data processing, and the Azure IoT PaaS versus SaaS options. After completing this course, students will be able to: Create, configure, and manage an Azure IoT hub. Provision devices by using IoT Hub and DPS, including provisioning at scale. Establish secure 2-way communication between devices and IoT Hub. Implement message processing by using IoT Hub routing and Azure Stream Analytics. Configure the connection to Time Series Insights and support business integration requirements. Implement IoT Edge scenarios using marketplace modules and various edge gateway patterns. Implement IoT Edge scenarios that require developing and deploying custom modules and containers. Implement device management using device twins and direct methods. Implement solution monitoring, logging, and diagnostics testing. Recognize and address security concerns and implement Azure Security Center for IoT. Build an IoT Solution by using Azure IoT Central and recongize SaaS opportunities for IoT. Course prerequisites IoT Developers should have basic programming skills in at least one Azure-supported language, including C#, Node.js, C, Python, or Java. Software development experience is a prerequisite for this course, but no specific software language is required, and the experience does not need to be at a professional level. Data Processing Experience: General understanding of data storage and data processing is a recommended but not required.  Cloud Solution Awareness: Students should have a basic understanding of PaaS, SaaS, and IaaS implementations. Microsoft Azure Fundamentals (M-AZ-900T00/M-AZ900), or equivalent skills, is recommended.  This course helps to prepare for exam AZ-220.   Agenda Module 1: Introduction to IoT and Azure IoT Services -Business Opportunities for IoT-Introduction to IoT Solution Architecture-IoT Hardware and Cloud Services Module 2: Devices and Device Communication -IoT Hub and Devices-IoT Developer Tools-Device Configuration and Communication Module 3: Device Provisioning at Scale -Device Provisioning Service Terms and Concepts-Configure and Manage the Device Provisioning Service-Device Provisioning Tasks Module 4: Message Processing and Analytics -Messages and Message Processing-Data Storage Options-Azure Stream Analytics Module 5: Insights and Business Integration -Business Integration for IoT Solutions-Data Visualization with Time Series Insights-Data Visualization with Power BI Module 6: Azure IoT Edge Deployment Process -Introduction to Azure IoT Edge-Edge Deployment Process-Edge Gateway Devices Module 7: Azure IoT Edge Modules and Containers -Develop Custom Edge Modules-Offline and Local Storage Module 8: Device Management -Introduction to IoT Device Management-Manage IoT and IoT Edge Devices-Device Management at Scale Module 9: Solution Testing, Diagnostics, and Logging -Monitoring and Logging-Troubleshooting Module 10: Azure Security Center and IoT Security Considerations -Security Fundamentals for IoT Solutions-Introduction to Azure Security Center for IoT-Enhance Protection with Azure Security Center for IoT Agents Module 11: Build an IoT Solution with IoT Central -Introduction to IoT Central-Create and Manage Device Templates-Manage Devices in Azure IoT Central [-]

Trenger du å bygge opp store og avanserte regneark? Ønsker du å lage rapporter og beregninger på store tallgrunnlag? Vil du finne ut hvordan du kan effektivisere arbeidet ditt i Excel? Ønsker du å lære de første stegene mot automatiserte rapporter? Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:   Dag 1    Generelt om regneark Om regneark og infrastruktur Bruke tastatur og hurtigtaster effektiv Absolutte referanser og definerte navn   Funksjoner Mer om funksjoner, hvis, antall.hvis, summer.hvis.sett Lær om "må ha funksjonen" Finn.rad [Vlookup] Andre funksjoner for spesielle oppgaver   Avansert formatering Spesiell formatering – dato, tekst og egendefinert Betinget formatering og cellestiler   Dag 2    Lister og tabeller Viktige regler og råd Bruk av autofilter og sortering Tabellfunksjonalitet Validering ved inntasting Beregninger av store datamengder via gode funksjoner   Pivottabell Hva er pivottabell og hvordan lage raske og enkle rapporter Utvidede muligheter i Pivot som grupperinger, vis verdier som og slicer   Dag 3   Metoder for dataimport Direkte import fra database   Innføring til makro Spille inn /registrere makro Ord/uttrykk og VBA editor   Datavask Slette tommer rader, fylle tomme celler Bruk av funksjoner for å klargjøre datagrunnlag Identifisere og håndtere avvik i grunnlag   Alternative temaer (hvis tid) Tips til diagrammer Hva hvis analyse Konsolidering   4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

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     [-]

Dette kurset, ledet av Espen Faugstad, er din omfattende guide til å mestre Adobe Illustrator på et profesjonelt nivå, uten at det kreves noen forkunnskaper. Kurset starter med grunnleggende, som å forstå programmet, dets verktøy og paneler, og lærer deg raskt å jobbe effektivt med lag, objekter, og grunnleggende geometriske figurer. Etter hvert som kurset utvikler seg, vil du dykke dypere inn i Illustrator og lære å tegne komplekse figurer med pennverktøyet, som er essensielt for å skape avansert grafikk i Illustrator. Kurset dekker også objektmanipulering, fargebruk, maling med pensler, tekst og typografi, effekter, bildehåndtering, og mye mer. Ved slutten av kurset vil du ha opparbeidet deg en solid forståelse og ferdigheter for å jobbe profesjonelt med Illustrator og vektorbasert grafikk.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Dokument Kapittel 3: Verktøy Kapittel 4: Velge og ordne objekter Kapittel 5: Linjer og figurer Kapittel 6: Tegne objekter Kapittel 7: Redigere objekter Kapittel 8: Farger Kapittel 9: Male Kapittel 10: Tekst Kapittel 11: Effekter Kapittel 12: Bilder Kapittel 13: Eksportere Kapittel 14: Avslutning   Varighet: 7 timer og 59 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

Kursinnhold In this MoP Foundation & Practitioner Course participants will learn the key aspects of the Management of Portfolios (MoP) methodology. MoP helps organizations ensure if the investments are done in the right change initiatives and implementing them correctly. This is achieved by: Prioritizing the programs and projects in terms of their contribution to the organization’s strategic objectives and overall level of risk. Managing the programs and projects consistently to ensure efficient and effective delivery. Maximizing the benefit by providing the greatest return from the investment made. Audience: Those aimed at those involved in a range of formal and informal portfolio management roles encompassing investment decision making, project and program delivery, and benefits realization. Those involved in the selection and delivery of business change initiatives. Experienced portfolio managers, managing large and complex portfolios of change initiatives. Members of portfolio offices and senior managers (e.g., financial managers, quality managers)  involved in setting strategic goals and giving direction to the organization’s portfolio of changes. Roles: Members of Management Boards Directors of Change Senior Responsible Owners (SROs) Portfolio, program, project, business change and benefits managers Business case writers Project appraisers Learning Objectives:Individuals certified at the MoP Foundation level will have demonstrated their understanding of: Define the scope and objectives of portfolio management and how it differs from program and project management. List the benefits of applying portfolio management. Explain the context it operates in. List the principles successful portfolio management is based on. List the different approaches to implement MoP. List the factors to maintain progress and how to assess the success of portfolio management. State the purpose and key content of the major portfolio documents. Define the scope of key portfolio management roles. Individuals certified at the MoP Practitioner level will have demonstrated their understanding of: Defining the business case to get senior management approval for portfolio management. Planning the implementation of portfolio management. Selecting and adapting MoP principles, practices, and techniques to suit different organizational environments. Evaluating examples of MoP information including documents and role descriptions. Analyzing the solutions adopted in relation to a given scenario. Prerequisites: There are no formal prerequisites. However, candidates are required to have passed the MoP Foundation exam in order to be certified at the Practitioner level. Course Materials:Participants receive a copy of the classroom presentation material and the handbook, which contains reference materials. It is mandatory for participants to have the official Management of Portfolios (MoP) Book. Examination:   Foundation Exam Practitioner Exam   Exam fee is NOK 2.500,- + VAT The Foundation exam is in closed book format. The exam consists of 50 multiple choice questions. A minimum score of 50% is required to pass the exam. The exam lasts 40 minutes. Additional time may be given for certain handicaps, and if the exam is not available in the candidate’s native language. Identification is required.   Exam fee is NOK 3.400,- + VAT  The practitioner exam is in open book format. The exam consists of 8 objective type questions with 10 marks available per question. 40 marks or 50% passing rate is required to pass the exam (out of 80 available). The exam lasts 180 minutes. Additional time may be given for certain handicaps and if the exam is not available in the native language. Identification is required.   Bouvet delivers the MOP® courses in collaboration with ITPreneurs   The MoP Approved Licensed Affiliate logo is a trade mark of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

DevOps Engineering on AWS [-]

NTI leverer opplæring for å forenkle og effektivisere din arbeidshverdag Årlig utdanner over 8.000 personer seg i ulike CAD- og BIM-løsninger hos NTI.Vi har mer enn 70 forskjellige kurs innen fagområdene CAD/BIM-, Industri, Prosess, Plant og Infrastruktur- og dokumenthåndtering, og i snitt har våre 100 konsulenter og instruktører mer enn 10 års erfaring med opplæring og konsulenttjenester. Hvordan få riktig oppsett av koordinater i prosjekt? Dette er et tema NTI merker stor pågang rundt til support, og henvendelsene kommer fra disipliner som byggteknikk, VVS og elektro i tillegg til arkitekt. Det er ofte arkitekten som setter opp koordinatene i Revit. Hvis utgangspunktet er feil, påvirkes dette i alle andre disipliner også. Spesielt der det er krav til at utvekslingsformatet er IFC. På dette online-kurset vil du lære: Forskjellen mellom de ulike koordinatsystemene Hva er et lokalt nullpunkt Sette opp reelle koordinater (Survey) «Best Practice» i oppsett av koordinater fra start Samhandling ved utveksling av filer og koordinater Behandle flere koordinatsystemer i samme prosjekt IFC export/import i forhold til delte koordinater Det kan gå noe tid mellom hver gang du setter opp koordinater, og det er lett å glemme prosessen. Etter gjennomført kurs, får du en «step by step» dokumentasjon, som kan benyttes som oppslagsverk senere.  Kurs på dine betingelser!Ditt firma har kanskje investert i ny CAD-programvare, oppgradert til ny versjon, oppdatert til ny programvare eller dere trenger rett og slett oppfriskning. Da er det på tide å investere i kompetanse for dine ansatte! Kontakt vår kurskoordinator Wenche, telefon 21 40 27 89 eller epost. [-]

Oracle Database: Introduction to SQL [-]

JavaScript er et av verdens mest brukte programmeringsspråk som, sammen med HTML og CSS, utgjør grunnsteinene i moderne webutvikling. Selv om språket opprinnelig ble utviklet for bruk på nettet, har det de siste årene både blitt populært som server-språk og som programmeringsspråk for enkeltstående applikasjoner og apper. I dette kurset, ledet av Lars Vidar Nordli, vil du få en grundig introduksjon til JavaScript. Målet er at du etter fullført kurs skal kunne lage dine egne interaktive nettsider. Kurset gir også en innføring i programmering generelt, og du vil lære konsepter som variabler, arrayer, funksjoner, løkker og objekter. Kurset er strukturert i følgende kapitler: Kapittel 1: Introduksjon Kapittel 2: Variabler Kapittel 3: Conditional statements Kapittel 4: Funksjoner Kapittel 5: Arrays Kapittel 6: Loops Kapittel 7: Manipulere DOM (Document Object Model) Kapittel 8: Events Kapittel 9: Objekter Kapittel 10: Rutiner Kapittel 11: Prosjekt Kapittel 12: Avslutning Etter å ha fullført kurset vil du ha en solid forståelse av JavaScript og være i stand til å bruke det til å lage interaktive nettsider og applikasjoner. Du vil også ha kjennskap til viktige programmeringskonsepter som vil være nyttige i din utviklerkarriere.   Varighet: 5 timer og 1 minutt   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

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.   Revit Architecture Basis I Her er et utvalg av temaene du vil lære på kurset: Introduksjon til BIM Modellering av 3D-bygningsmodell i flere detaljeringsgrader (informasjonsnivåer) Samarbeid med andre fagmodeller Generering av planer, snitt, fasader, detaljer og perspektiver Skjemaer og mengdeuttrekk Oppsetning til print A Anvendelse av relevante NTItools Kurset gir deg innblikk i bruken av BIM-arbeidsmetoder med Revit som hovedverktøy. Det bygges opp en full, parametrisk 3D-modell, hvor de grunnleggende funksjonene i Revit benyttes. DU vil få en bred forståelse av både prinsipper og funksjoner i Revit og skal bli i stand til å øke detaljeringen av prosjektet ytterligere.   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 [-]

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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 P&ID grunnkurs Her er et utvalg av temaene du vil lære på kurset: Opprette og håndtere P&ID-prosjekter Tegne og redigere P&ID-diagrammer Eksportere og Importere data via Excel Uttrekk av lister og rapporter Du vil lære å opprette og håndtere P&ID-prosjekter, tegne diagrammer med de verktøy og kommandoer som er designet til formålet, og bruke toolpalettenes utstyr, ventiler, fittings og instrumenter. I tillegg til å bruke valideringsverktøyet til å kvalitetssikre ditt prosjekt, eksportfunksjonen til å skape 'nøytrale' AutoCAD-kopier av dine P&ID-tegninger samt å lage forskjellige rapporter og datauttrekk for f.eks. utstyr, ventiler, instrumenter osv.   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 [-]