Oversikt over relevante kurs innen IT-kurs i Oslo

Kurset fokuserer på integrering av forskjellige verdistrømmer og aktiviteter for å lage, levere og støtte IT-aktiverte produkter og tjenester, samtidig som den dekker støtte for praksis, metoder og verktøy. Kurset gir kandidatene forståelse for tjenestekvalitet og forbedringsmetoder. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på AXELOS sine websider. Inkluderer: Tilgang til ITIL® 4 Specialist: Create, Deliver and Support e-læring (engelsk) i 12 måneder. ITIL® 4 Specialist: Create, Deliver and Support online voucher til sertifiseringstest.   ITIL®/PRINCE2®/MSP®/MoP® are registered trademarks of AXELOS Limited, used under permission of AXELOS Limited. All rights reserved. [-]

Kurset passer både for deg som skal i gang med Focus Arealplan eller som har brukt programmet litt fra før.   Hensikten med kurset er å gi deg en grunnleggende forståelse i bruken av AutoCAD-applikasjonen Focus Arealplan. Kurset er nødvendig for å komme raskt i gang med med å bruke programmet, og for å få den nødvendige forståelse for de mulighetene programmet gir. Kursinnhold: På kurset vi deltagerne lære å hente inn SOSI-filer og å utarbeide reguleringsplaner iht. Miljøverndepartementets veiledere med Focus Arealplan. Vi legger spesielt vekt på riktig arbeidsmetodikk. Kurset gjennomgår også hvordan man overfører ferdig plan til SOSI-format og kontroll av SOSI-filen med programmet 'SOSI-vis'. Vi vil også gå gjennom typiske problemstillinger hvor vi har erfart at ting kan gå galt eller ting som oppleves vanskelig hvis man benytter feil arbeidsmetodikk. [-]

ITIL® 4 Practitioner: Incident Management [-]

IP2: IP- og nettverks-workshop [-]

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 [-]

Mange av prinsippene for stor dokumenter kan med fordel også anvendes på de aller fleste dokumenter, inklusive brev og notater. Her går vi gjennom alt du trenger for å skrive bøker eller avhandlinger og også det du trenger for å skrive invitasjoner til nyttårsfest eller barnebursdag. Kursinnhold Fletting til brev, konvolutter, etiketter og e-post Bruk av stiler Bilder og bildetekster innholdsfortegnelse, stikkordliste og figurliste Spalter, marger, sidefarger, sidekantlinjer og dokumenttemaer. Deldokumenter kan samles i et hoved dokument  Topp- og bunntekster og side nummerering. Utklipp, figurer, SmartArt og diagram. Tekstbokser Tabeller: Formatering, sortering og beregninger. Maler med Felt, innholdskontroller og skjemakontroller Med makroer kan du effektivisere avanserte oppgaver som består av serie med handlinger. 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. [-]

Blended Virtual CourseThe course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams/Zoom for short bursts at the beginning of the day, then setting work for the rest of the day and then coming back at the end of the day for another on-line session for any questions before setting homework in the form of practice exams for the evening. KursinnholdDette 2-dagers kurset passer for deg som ønsker å ta en sertifisering innen Agile Testing. Kurset bygger på ISTQB Foundation syllabus og gir deg grunnleggende ferdigheter innen Agile testing. Kursdato: 14.-15. desember, eksamen 16. desember, kl. 09:00-10:15 Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt. On completion the Agile Tester will be able to: 1. Understand the fundamentals of Agile Software Development How the various agile approaches differ and understanding the concepts of the Agile ManifestoHow the tester needs to adapt in the agile process for maximum effectiveness. Apply the various aspects relating to agile, such as:o Writing and reviewing User Storieso Working in a continuous integrated environment ando Performing agile retrospectives to improve the process 2. Apply the fundamental Agile testing principles, practices and processes How testing differs when working in an agile lifecycle compared to a more traditional lifecycleHow to work in a highly collaborative and integrated environment.How independent testing can be used within an agile projectHow to report progress and the quality of the product to business stakeholdersUnderstand the role and skills of a tester within an agile team 3. Know the key testing methods, techniques and tools to use within an Agile project Understand Test Driven Development (TDD), Acceptance Driven Development (ADD), Behaviour Driven Development (BDD) and the concepts of the Test Pyramid.Perform the role of a tester within a Scrum teamo Perform test estimation and assess product quality risks within an agile projecto Interpret the information produced during an agile project to support test activitieso Write ADD test caseso Write test cases for both functional and non-functional user storieso Execute exploratory testing within an agile projectRecognise the various tools available to the tester for the various agile activities The exam The ISTQB® Agile Testing exam is a 1 hour 15 minute multiple-choice exam with the pass mark being 65%. You must hold the ISTQB® Foundation certificate in software testing in order to sit this exam.The exam is a remote proctored exam. [-]

Kursinnhold Import av .csv Import av tekstfiler (.txt) Import fra internett Transformering av data Rette opp feil Lage beregnede kolonner Regelmessig import Analyse av store datamengder   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.   [-]

Excel makro kurs for deg som ønsker å ta Excel til et nytt nivå. Er det mye rutinearbeid som tar lang tid? Trenger du noen helt spesielle funksjoner for å lette arbeidet? En makro er en program-kode som kan utføre forskjellige handlinger i Excel. En makro kan gjøre rutinemessige oppgaver, spesielle beregninger og få til operasjoner som normalt ikke går i Excel. Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:   Utforming av makroer Makroviruskontroll Registrering og kjøring av makroer Tilknytning av makro til knapp   Redigering av makroer Lesing og redigering av Visual Basic kode Feilsøking i makroer Prosjektutforskeren   Forhåndsdefinerte dialogbokser Skriving av prosedyrer Forhåndsdefinerte dialogbokser Meldingsboks og inndataboks   Funksjoner og prosedyrer Bruk av variabler Bruk av prosedyrer Brukerdefinerte funksjoner   Egendefinerte dialogbokser Utforming av skjema med felt Egenskaper Hendelsesprosedyrer og tabulatorrekkefølge   Løkker og tester Tester og betingelser Setningene IF, SELECT, DO og FOR   4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

Oppdag den intuitive og robuste verdenen av 3D-modellering med "SketchUp: Komplett", et omfattende kurs ledet av Espen Faugstad hos Utdannet.no. SketchUp, populært blant arkitekter, ingeniører, snekkere og kreative fagfolk, er et gratis verktøy som lar deg designe i både to- og tredimensjonalt perspektiv. Dette kurset er designet for alle som ønsker å lære å bruke SketchUp effektivt, uavhengig av tidligere erfaring. Kurset vil guide deg gjennom SketchUps grunnleggende, inkludert oppsett, verktøy og paneler, og hvordan du skaper to- og tredimensjonale figurer. Du vil lære å kontrollere kameraet, anvende ulike visningsstiler og manipulere objekter med en rekke verktøy. Videre dekkes tegning av figurer, måling og merking av modeller, organisering av prosjekter, samt arbeid med komponenter, materialer og teksturer. Med dette kurset vil du utvikle ferdigheter for å lage detaljerte og nøyaktige 3D-modeller og bli i stand til å presentere dine design på en overbevisende måte. Ved kursets slutt vil du ha en solid forståelse av SketchUp, noe som gjør deg i stand til å bruke programmet for en rekke prosjekter, fra enkle skisser til komplekse arkitektoniske design.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Kamera Kapittel 3: Visning Kapittel 4: Manipulere Kapittel 5: Tegne Kapittel 6: Måle og merke Kapittel 7: Organisere Kapittel 8: Komponenter Kapittel 9: Material og tekstur Kapittel 10: Presentasjon Kapittel 11: Avslutning   Varighet: 3 timer og 4 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. [-]

  Webinaret passer for deg som vil lære å ta dine notater digitalt i Microsoft OneNote. Har du oversikten over notater etter at møter er over? Føler du at de papirbaserte notatene over tid blir uoversiktlige og lite tilgjengelige. Du kan lære å jobbe raskere, smartere og bedre ved å ta i bruk OneNote for å digitalisere dine notater. I dine digitale notater i OneNote kan du inkludere tekst, bilder, lenker til filer og websider, lyd og film - og notater gjøres enkelt enten på din smarttelefon, ditt nettbrett eller din PC; alt etter hva du har tilgjengelig. Systemet vil synkronisere notatene på tvers av dine enheter. OneNote er en del av Microsoft 365 og er tilgjengelig gratis for alle. Agenda: Lær å opprette notatblokker. Du kan ha så mange notatblokker du vil, og disse organiseres i inndelinger og sider. Søk og navigering mellom dine notater. Flytting og kopiering av notater mellom inndelinger og notatblokker. Synkronisering av notatblokker mellom dine enheter (PC, mobil, nettbrett). Deling og samarbeid med andre. OneNote kan også bygges inn i Microsoft Teams. Innsetting av lenker, lydnotater, nettvideo og filer i dine notater. Benytte OneNote til møtenotater og koble til notatene når du sender en møteinnkalling i Outlook. OneNote og koblinger mot andre programmer i Microsoft 365. NB: Sørg for å ha den siste OneNote appen installert på din PC (Microsoft har lansert flere versjoner av OneNote). Vi skal benytte OneNote for Windows 10 appen på kurset. Appen kan du laste ned her.  Mål med webinaret: Etter endt webinar skal du kunne jobbe mer smart og effektivt i Excel. Forkunnskap: Det er en fordel at du kjenner til Microsoft 365. Målgruppe: Dette webinaret er for deg som ønsker å ta digitale notater på din PC, din smarttelefon eller nettbrett ved hjelp av Microsoft OneNote. Undervisningsform: Dette er et webinar som du følger via din PC eller nettbrett. Vi vil sende deg påloggingsinformasjon etter at du har meldt deg på kurset. Pris: 1200 kroner (Ansatte og studenter ved UiS har egne betingelser). Varighet: 3 timer [-]

Kurset er rettet mot deg som skal armere i Autodesk Revit. Dette er et praktisk kurs som gjør deg i stand til å armere betongkonstruksjoner, lage armeringstegninger og bøyelister. Hensikten med kurset er å gjøre deg i stand til bruke armerinsgverktøyene i Revit samt lage armeringstegninger og bøyelister ved hjelp av verktøyene som ligger i Revit-applikasjonen Focus RAT Bygg. Du vil lære hvordan manuelt armere betongkonstruksjoner. Du vil også lære verktøyene for å lage løpemeterarmering, armeringsnett og kantarmering. Du vil lære å bruke Revit Extensions for å armere konstruksjoner automatisk. Vi skal også lage armeringstegninger og bøyelister i henhold til NS 3766. Kursinnhold: Manuell armering av betongkonstruksjoner Løpemeterarmering Kantarmering Armeringsnett Automatisk armering av betongkonstruksjoner med Revit Extensions Armere avanserte betongkonstruksjoner Lage armeringstegninger Lage bøyelister [-]

UTDANNINGSMÅLKursdeltakerne skal etter endt kurs kunne bruke AutoCAD til å: Opprette nye tegninger Gjøre forandringer i eksisterende tegninger Bruke og forstå de vanligste tegne- og editeringskommandoer Målsette og påføre tekst i en tegning Bruke og forstå lagoppbyggingen i AutoCAD Symbol- og blokkhåndtering Enkel layout/plotting Lagre tegninger KURSINNHOLD: Introduksjon av AutoCAD 2-dimensjonal tegning Zoom og skjermbehandlingsteknikk Redigeringsfunksjoner Målsettingsfunksjoner Laghåndtering Symbol- og blokkhåndtering Hente symboler og parametere (målsettings- og tekststiler, osv.) fra eksisterende tegninger Layout/plotting av tegninger Lagrings- og avslutningsrutiner [-]

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     [-]

Her starter vi ganske på begynnelsen, og går ut fra at deltagerne har liten eller ingen erfaring med Excel. Vi starter med de grunnleggende prinsippene, og bygger så videre på dem. I stikkordsform ser innholdet ut slik: Regnearkets oppbygning – grunnprinsipper Tall og tekst – identifisere og korrigere/konvertere Listefunksjonalitet – sortere, filtrere, redigere, skrive ut, etc. Grunnleggende formelbygging – de fire regneartene Kopiering av formler – absolutte og relative referanser Sentrale funksjoner: SUMMER, GJENNOMSNITT, HVIS,   HVISFEIL Modellbyggingsteknikker - sammendrag av data over flere ark Grafiske fremstillinger av numeriske data Identifisere og fjerne duplikatverdier fra en liste Arbeide med tid (dager, klokkeslett, etc.) i Excel [-]