Oversikt over relevante kurs innen IT-kurs i Avaldsnes

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

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

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

ITIL® 4 Foundation-kurset er en introduksjon til ITIL® 4. Kurset lar kandidater se på IT-tjenestestyring gjennom en ende-til-ende driftsmodell, som inkluderer oppretting, levering og kontinuerlig forbedring av IT-relaterte produkter og tjenester. E-læringskurset inneholder 12 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Foundation e-læring (engelsk) i 12 måneder. ITIL® Foundation online voucher til sertifiseringstest + digital ITIL Foundation bok Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. Sertifiseringen består av: 40 spørsmål Multiple Choice 60 minutter + 15 minutter til rådighet dersom du ikke har engelsk som morsmål For å bestå må du ha minimum 26 riktige (65%) Ingen hjelpemidler tillatt ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Oracle GoldenGate 19c: Fundamentals for Oracle [-]

ITIL® 4 Specialist: Monitor, Support and Fulfil [-]

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   [-]

Power BI – Profesjonelle rapporter [-]

MasterClass: Hacking and Securing Windows Infrastructure with Paula Januszkiewicz [-]

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     [-]

Kubernetes Administration (LFS458) [-]

CCNA: Implementing and Administering Cisco Solutions [-]

Power BI Desktop – DAX formler [-]

Adobe Audition er et profesjonelt lydredigeringsprogram som gir deg muligheten til å spille inn, mikse og bearbeide lyd. Dette programmet er ideelt for alle som arbeider med lyd, uavhengig om det er for video, animasjon, radio, podkast eller spill. Audition støtter et bredt spekter av lydformater, inkludert ASIO, VST og MIDI. Du kan også enkelt importere prosjekter fra Adobe Premiere Pro, noe som gjør Audition til et utmerket valg for de som ønsker førsteklasses lyd til sine videoproduksjoner. For å få tilgang til Adobe Audition må du abonnere på Adobe Creative Cloud (kr 590 per måned). I dette omfattende kurset vil Espen Faugstad veilede deg gjennom hele programmet, fra begynnelse til slutt. Du vil lære alt du trenger for å kunne bruke programmet på en effektiv måte. Kurset dekker emner som hvordan du importerer, organiserer og redigerer lydfiler. Du vil også lære å fjerne bakgrunnsstøy, fremheve stemmer, legge til lydeffekter, og mye mer. Kursinnhold: Introduksjon Lydterminologi Importering av lyd Redigering med Waveform Editor (del 1) Redigering med Waveform Editor (del 2) Multitrack Editor (del 1) Multitrack Editor (del 2) Bruk av paneler og verktøy Essential Sound-panelet Eksportering av prosjekt og samarbeid med Premiere Pro Etter fullføring av kurset, vil du ha den nødvendige kunnskapen og ferdighetene til å arbeide effektivt med Adobe Audition for å oppnå høykvalitetslyd i dine prosjekter.   Varighet: 4 timer og 5 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av de beste digitale nettkursene i landet. 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 tilgjengelige. Med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger har vi opplevd betydelig vekst de siste årene. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

PowerPoint med Presentasjonsteknikk [-]