Oversikt over relevante kurs innen IT-kurs i Indre Østfold

Understand the purpose and key concepts of the Service Level Management Practice, elucidating its significance in defining, negotiating, and managing service levels to meet customer expectations. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Service Configuration Management, including its role in maintaining accurate and reliable information about configuration items (CIs) within the IT infrastructure. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content Mobile-optimised Practical exercises   Exam: 20 questions Multiple Choice 30 Minutes Closed book Pass Mark: 65% [-]

Understand the purpose and key concepts of the Continual Improvement Practice, elucidating its significance in fostering a culture of ongoing improvement and innovation within the organisation. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the key concepts of Relationship Management, Supplier Management, Service Level Management, Continual Improvement, and Information Security Management, elucidating their significance in fostering collaboration, ensuring service quality, driving continual improvement, and maintaining information security. This eLearning is: Interactive Self-paced   Device-friendly   12 hours content   Mobile-optimised   Practical exercises   Exam: 60 questions Multiple choise 90 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of Deployment Management, highlighting its importance in managing the deployment of new or changed services into the live environment. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours of content   Mobile-optimised   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

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

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

ITIL® Specialist - High Velocity IT [-]

AZ-1001: Deploy and manage containers using Azure Kubernetes Service [-]

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. [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Grunnleggende kunnskaper i HTML, CSS tilsvarende emnet IINI1002 Webutvikling 1. Kunnskaper om grunnleggende programmering og helst litt Javascript er en fordel. Innleveringer: Større eller mindre øvinger tilsvarende 8 øvinger, hvor 6 må være godkjent før endelig karakter settes. Personlig veileder: ja Vurderingsform: Prosjektoppgave som vurderes til bestått/ikke bestått. Karakteren i faget settes på grunnlag av en individuell 4-timers nettbasert hjemmeeksamen. Klageadgang i dette faget gjelder hver enkelt vurderingsdel. Ansvarlig: Atle Nes Eksamensdato: 09.12.13 / 12.05.14         Læremål: Etter å ha gjennomført emnet skal studenten ha følgende samlede læringsutbytte: KUNNSKAPER:Kandidaten:- forstår problemstillinger knyttet til bruk av ikke-standardisert teknologi- har kjennskap til nyttige rammeverk for HTML5 og fallback-løsninger- har kjennskap til problemstillinger knyttet til bruk av ulike medieformater FERDIGHETER:Kandidaten:- kan ta i bruk nye semantiske elementer fra HTML5- kan ta i bruk ny funksjonalitet fra CSS3 og JavaScript på nettstedet- kan ta i bruk nye skjemaelementer og -attributter fra HTML5- kan tegne på et canvas-element med JavaScript- kan legge til multimedia ved hjelp av video- og audio-elementet- kan lage nettsider som tilpasser seg mobile enheter og utnytter egenskaper hos disse- kan bruke lokal lagring til å lagre og hente fram data- kan bruke XMLHttpRequest2 til kommunikasjon med webtjeneren- kan lage en større HTML5-basert webløsning GENERELL KOMPETANSE:Kandidaten:- får et overblikk over ny webteknologi som er i ferd med å bli standardisert Innhold:Introduksjon til HTML5, grunnleggende syntaks og struktur, nye semantiske elementer, dynamiske websider med JavaScript og CSS3, nye skjemaelementer (forms), HTML5 canvas til grafikk og tegning, HTML5 video og audio, mobile enheter og device access, lokal lagring av applikasjoner og data, dataoverføring med Web SocketsLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag HTML5 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

  Jobber du med store datamengder? Vil du få kontroll over dataene dine? Har du problemer med utskrift fra Excel? Her vil du få kjennskap til en rekke gode metoder for å jobbe med lister.  Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Prinsipper for å arbeide med lister  Få med deg råd og regler som gjelder for et godt grunnlag   Effektiv merking og navigasjon   Flere måter å sortere grunnlaget på  Sortering etter verdier Sortering etter cellefarge, skriftfarge og celleikon Sortering etter egendefinert liste   Delsammendrag  Lag enkle rapporter ved å bruke delsammendrag verktøyet Kopiere delsammendrag   Filtrering  Se hvordan du finner relevante data i et stort grunnlag Filtrering etter farge og ikon   Fryse første rad og første kolonne     Skjule / vise rader og kolonner     Utskriftinnstillinger  Tilpass utskrift til en side Gjenta rader eller kolonner ved utskrift av flere sider Tilpass utskriftområdet     [-]

Start din reise som frontend-utvikler med kurset "HTML: Komplett" ledet av Espen Faugstad hos Utdannet.no. Dette kurset er første trinn i læringsstien «Frontend-utvikler», designet for å gi deg alle nødvendige ferdigheter for å bygge raske og moderne nettsider. Vi antar ingen tidligere kunnskap om HTML, CSS eller JavaScript, og du vil lære alt fra grunnleggende til avanserte teknikker. Kurset begynner med HTML, hjertet av webutvikling, og vil ta deg gjennom grunnleggende syntaks, tekstformatering, oppretting av lenker, håndtering av multimedia, og mye mer. Du vil også lære å strukturere nettsider effektivt, jobbe med tabeller og skjemaer, og til slutt lage et prosjekt som demonstrerer dine nye ferdigheter. Etter å ha fullført dette kurset, vil du være godt rustet til å fortsette med CSS og JavaScript for å skape vakre, responsive og lynraske nettsider som fungerer utmerket på alle enheter.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Internett Kapittel 3: Syntaks Kapittel 4: Tekst Kapittel 5: Link Kapittel 6: Multimedia Kapittel 7: Tabell Kapittel 8: Skjema Kapittel 9: Struktur Kapittel 10: Prosjekt Kapittel 11: Avslutning   Varighet: 5 timer og 55 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. [-]

ITIL® Strategist - Direct, Plan and Improve [-]

TOGAF® EA Practitioner Bridge Course [-]