Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Jämtlands län

Kurset er rettet mot deg som vil lære grunnprinsippene og arbeidsmetodikk for prosjektering av terreng og landskap i AutoCAD Civil 3D. Kurset er øvelsesbasert og følger en kursbok med tilhørende øvelsesfiler. Hensikten med kurset er å gi deg dypere innføring i prosjektering av landskap og terrengbearbeiding med AutoCAD Civil 3D og Focus CAT Basis og Landskap.   Kursinnhold: Forslag til oppstart av et prosjekt i AutoCAD Civil 3D prosjekter ved bruk av Data Shortcuts Hvordan bruke gradings (skråningsutslag) Hvordan bruke assemblies til å lage en enkel vei Hvordan bruke assemblies til å lage en murvegg Masseberegning av en del av terrenget Opptegning av ryddige snitt av terrenget Visualisering i AutoCAD Civil 3D Gjennomgang av nyttige funksjoner i Focus CAT landskap Kursdokumentasjonen er på norsk. [-]

E-læringskurset gir deg en opplevelsesrik og praktisk opplæring i regnearkprogrammet Excel 2016. En kombinasjon av videoer, teori, oppskrifter, oppgaver og tester gjør det enkelt å lære seg de nye verktøyene. E-læringskurset inneholder 59 opplæringsvideoer.E-læringskurset er tilpasset Office 365.Testene i e-læringskurset måler kunnskap før, under og etter opplæringen. Når ettertesten er bestått får du tilgang til et kursbevis i PDF-format som enkelt kan lagres eller skrives ut.Jobb smart og effektivt!- Office 365 gir deg alltid den nyeste versjonen av Excel.- Maler er tilgjengelig ved oppstart.- Enklere åpning og lagring av arbeidsbøker.- Microsoft-kontoen kobler enheten til OneDrive, slik at du alltid har tilgang til filene dine.- Egen modus som er optimalisert for berøring.- Enklere søk etter kommandoer, handlinger og hjelp.- Formler utfører beregninger raskt og enkelt.- Autofyll forenkler arbeidet med å fylle inn data i et regneark.- Håndskriftsformler gjør det enklere å skrive inn formler for hånd.- Cellestiler gjør formateringen mer konsekvent.- Bruk av tema gir en konsekvent layout på alle Office-dokumenter.- Betinget formatering gjør det enkelt å følge med sentrale verdier i regnearket.- Hurtiganalyse gjør det raskt og enkelt å tolke og analysere et dataområde.- Du har tilgang til et utall ferdigdefinerte funksjoner som utfører alle slags beregninger.- Diagram egner seg godt for å gi et visuelt, lettfattelig inntrykk av tallverdier.- Sparkline-grafikk kan brukes for å visualisere data direkte i regnearkceller.- Sortering og filtrering gjør arbeidet med lister og tabeller enkelt og effektivt.- Bruk av flere regneark gjør større regnearkmodeller mer oversiktlig.- Et integrert utskriftsmiljø med både utskriftsinnstillinger og forhåndsvisning.Innhold:- Før du starter- Redigering- Formler- Formatering- Funksjoner- Diagram- Lister og tabeller- Flere regneark- UtskriftKURSET KAN KJØRES PÅ NETTBRETT! [-]

På webinaret vil vi vise deg hvordan du kan lage nettbaserte spørreundersøkelse, tester og påmeldingsskjemaer ved hjelp av Microsoft Forms. Microsoft Forms er en enkel og elegant app i Microsoft 365 familien for opprettelse av undersøkelser og tester. Du kan lage skjema med flere språk i samme skjema. Du kan ha forgreninger til ulike svarretninger alt etter hva som velges som svar. Det er mange ulike spørsmålsalternativer å velge mellom. Svarene kan være anonyme om ønskelig. Du kan også sette inn undersøkelser (poll) i et Teams-møte eller som en del av en presentasjon i PowerPoint. Resultatene behandler og analyserer du enkelt i Excel. Hva er Forms | Forskjell undersøkelser og tester | Personlige skjema vs gruppeskjema | Opprette skjema | Spørsmålstyper | Forgreninger | Innstillinger | Flere språk i samme skjema | Simulere skjema | Delingsmåter (samle inn svar) | Samarbeide om samme skjema eller duplisere skjema (gi kopi til andre) | Resultater og analyser | Forms og Teams | Forms og PowerPoint Pris: 1690 kroner [-]

Kurs beskrivelse Er det vanskelig å skjønne hvordan Access fungerer? Har du databaser med bare 1 tabell og ikke flere som de skal ha? Får du ikke orden på dine data? Må du skrive data inn i tabellen istedenfor gjennom et skjema? Er det vanskelig å få data ut fra databasen din? Blir databasen din lite brukervennlig? Dette er vanlige problemstillinger mange sliter med og som blir borte etter endt kurs! Kurset passer for deg med liten erfaring og som ønsker å lære Access fra grunnen av. Kurset passer også for deg som er selvlært og som ønsker å jobbe mer effektivt. På kun 2 dager vil du mestre de vanligste arbeidsoppgavene i Access. Du lærer gode rutiner du trenger for å kunne arbeide raskt og effektivt. Du vil kunne lage og strukturere alt fra enkle til mer avanserte databaser og vil føle deg trygg på at det er du som kontrollerer Access og ikke omvendt! Du lærer også hvordan du skal få data ut fra databasen din gjennom spørringer og rapporter. I tillegg får du 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! Kursholderne har mer enn 20 års Access erfaring som de gjerne deler med deg! Meld deg på Access-kurs allerede i dag og sikre deg plass!   Kursinnhold Grunnleggende begreper Introduksjon til databasedesign/datamodellering Planlegging av en ny databaseOrganisering av data i en database Opprette en database   Tabeller Tabellens utformingsvisningTabellens data arkvisning Spørringer/Queries UtvalgsspørringerHandlingsspørringer/ActionqueriesLag tabell-spørringSlettespørring-Føy til-spørringOppdateringsspørringParameterspørringer Skjemaer Opprette skjemaer med skjemaveivisereSkjemavisningUformingsvisning for skjemaerGjennomgang av designverktøyVerktøyboksenFargepalett, fonter, tekstplasseringBundne, ubundne og kalkulerte kontrollerPostkilde og feltliste for et skjemaEn-til-mange-skjema Rapporter Opprette rapporter med RapportveiviserenUtformingsvisning for en rapportGruppering og sortering i rapporter Etiketter Spesialtilpassede etikettmalerEtikettveiviseren   [-]

AI-900: Microsoft Azure AI Fundamentals [-]

Objectives This course teaches participants the following skills: Design and build data processing systems on Google Cloud Platform Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow Derive business insights from extremely large datasets using Google BigQuery Train, evaluate, and predict using machine learning models using Tensorflow and Cloud ML Leverage unstructured data using Spark and ML APIs on Cloud Dataproc Enable instant insights from streaming data   All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Module 1: Introduction to Data Engineering -Explore the role of a data engineer-Analyze data engineering challenges-Intro to BigQuery-Data Lakes and Data Warehouses-Demo: Federated Queries with BigQuery-Transactional Databases vs Data Warehouses-Website Demo: Finding PII in your dataset with DLP API-Partner effectively with other data teams-Manage data access and governance-Build production-ready pipelines-Review GCP customer case study-Lab: Analyzing Data with BigQuery Module 2: Building a Data Lake -Introduction to Data Lakes-Data Storage and ETL options on GCP-Building a Data Lake using Cloud Storage-Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions-Securing Cloud Storage-Storing All Sorts of Data Types-Video Demo: Running federated queries on Parquet and ORC files in BigQuery-Cloud SQL as a relational Data Lake-Lab: Loading Taxi Data into Cloud SQL Module 3: Building a Data Warehouse -The modern data warehouse-Intro to BigQuery-Demo: Query TB+ of data in seconds-Getting Started-Loading Data-Video Demo: Querying Cloud SQL from BigQuery-Lab: Loading Data into BigQuery-Exploring Schemas-Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA-Schema Design-Nested and Repeated Fields-Demo: Nested and repeated fields in BigQuery-Lab: Working with JSON and Array data in BigQuery-Optimizing with Partitioning and Clustering-Demo: Partitioned and Clustered Tables in BigQuery-Preview: Transforming Batch and Streaming Data Module 4: Introduction to Building Batch Data Pipelines -EL, ELT, ETL-Quality considerations-How to carry out operations in BigQuery-Demo: ELT to improve data quality in BigQuery-Shortcomings-ETL to solve data quality issues Module 5: Executing Spark on Cloud Dataproc -The Hadoop ecosystem-Running Hadoop on Cloud Dataproc-GCS instead of HDFS-Optimizing Dataproc-Lab: Running Apache Spark jobs on Cloud Dataproc Module 6: Serverless Data Processing with Cloud Dataflow -Cloud Dataflow-Why customers value Dataflow-Dataflow Pipelines-Lab: A Simple Dataflow Pipeline (Python/Java)-Lab: MapReduce in Dataflow (Python/Java)-Lab: Side Inputs (Python/Java)-Dataflow Templates-Dataflow SQL Module 7: Manage Data Pipelines with Cloud Data Fusion and Cloud Composer -Building Batch Data Pipelines visually with Cloud Data Fusion-Components-UI Overview-Building a Pipeline-Exploring Data using Wrangler-Lab: Building and executing a pipeline graph in Cloud Data Fusion-Orchestrating work between GCP services with Cloud Composer-Apache Airflow Environment-DAGs and Operators-Workflow Scheduling-Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, -Cloud Storage, and BigQuery-Monitoring and Logging-Lab: An Introduction to Cloud Composer Module 8: Introduction to Processing Streaming Data Processing Streaming Data Module 9: Serverless Messaging with Cloud Pub/Sub -Cloud Pub/Sub-Lab: Publish Streaming Data into Pub/Sub Module 10: Cloud Dataflow Streaming Features -Cloud Dataflow Streaming Features-Lab: Streaming Data Pipelines Module 11: High-Throughput BigQuery and Bigtable Streaming Features -BigQuery Streaming Features-Lab: Streaming Analytics and Dashboards-Cloud Bigtable-Lab: Streaming Data Pipelines into Bigtable Module 12: Advanced BigQuery Functionality and Performance -Analytic Window Functions-Using With Clauses-GIS Functions-Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz-Performance Considerations-Lab: Optimizing your BigQuery Queries for Performance-Optional Lab: Creating Date-Partitioned Tables in BigQuery Module 13: Introduction to Analytics and AI -What is AI?-From Ad-hoc Data Analysis to Data Driven Decisions-Options for ML models on GCP Module 14: Prebuilt ML model APIs for Unstructured Data -Unstructured Data is Hard-ML APIs for Enriching Data-Lab: Using the Natural Language API to Classify Unstructured Text Module 15: Big Data Analytics with Cloud AI Platform Notebooks -What’s a Notebook-BigQuery Magic and Ties to Pandas-Lab: BigQuery in Jupyter Labs on AI Platform Module 16: Production ML Pipelines with Kubeflow -Ways to do ML on GCP-Kubeflow-AI Hub-Lab: Running AI models on Kubeflow Module 17: Custom Model building with SQL in BigQuery ML -BigQuery ML for Quick Model Building-Demo: Train a model with BigQuery ML to predict NYC taxi fares-Supported Models-Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML-Lab Option 2: Movie Recommendations in BigQuery ML Module 18: Custom Model building with Cloud AutoML -Why Auto ML?-Auto ML Vision-Auto ML NLP-Auto ML Tables [-]

Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Windows server 2008/2012 - god kjennskap om Windows server Innleveringer: Øvinger: 8 av må være godkjent. Personlig veileder: ja Vurderingsform: Eksamen blir arrangert som 2 dagers hjemmeeksamen (start kl 09.00 og innlevering kl 15.00 dagen etter). Hver student får tildelt et virtuelt område. Det skal også leveres en skriftelig begrunnelse for de valg som er foretatt. Hjemmeeksamen, individuell, 2 dager, 0 Ansvarlig: Stein Meisingseth Eksamensdato: 10.12.13 / 13.05.14         Læremål: KUNNSKAPER:Kandidaten:- har innsikt i drift av nettverk basert på Windows Server, programvaredistribusjon og kjenner til hvilke verktøy som kan brukes for administrasjon av virtuelle maskiner og nettverk- kan forklare systemer som kan benyttes til overvåkning og vedlikehold FERDIGHETER:Kandidaten kan:- installere og konfigurere System Center Configuration Manager 2012- automatisere manuelle operasjoner- sikre, oppdatere og overvåke IT-systemer GENERELL KOMPETANSE:Kandidaten har:- perspektiv og kompetanse i å velge riktige og tilpassete driftsløsninger- kompetanse i å formidle driftsterminologi, både muntlig og skriftlig Innhold:- Automatisering og sikring ved hjelp av System Center Cooperation Manager 2012 (SCCM 2012) - Applikasjonsutrulling - Operativ System utrulling - Klient tilstands-monitorering - Programvare oppdateringer - Sikkerhetsbeskyttelse vha Endpoint ProtectionLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Microsoft System Center i overvåkning og drift 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

Ønsker du å mestre Google Analytics 4 for å få dybdeinnsikt i kundeadferden på nettstedet eller appen din? Da er kurset "Google Analytics 4: Komplett", ledet av Espen Faugstad, perfekt for deg. Dette kurset er designet for å gi deg en helhetlig forståelse av Google Analytics 4, slik at du kan jobbe profesjonelt med dette kraftige analyseverktøyet. Kurset starter med grunnleggende om hvordan Google Analytics 4 fungerer og veileder deg gjennom installasjonen på din nettside. Du vil lære å konfigurere Google Analytics for å maksimere dets potensial, administrere brukere, spore nettstedsøk, og mye mer. I tillegg gir kurset deg en detaljert gjennomgang av standardrapporter og utforskninger som er tilgjengelige i Google Analytics 4. Mot slutten av kurset vil du dykke inn i mer avanserte temaer som opprettelse og sporing av egendefinerte hendelser, konverteringssporing, og hvordan du kan utnytte innsikter fra brukerdata for å forbedre dine digitale strategier. Dette kurset er din vei til å bli en ekspert i Google Analytics 4.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Installer Kapittel 3: Konfigurer Kapittel 4: Rapporter Kapittel 5: Utforsk Kapittel 6: Hendelser Kapittel 7: Avansert Kapittel 8: Avslutning   Varighet: 4 timer og 48 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. [-]

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     [-]

Objectives Understand how software containers work Understand the architecture of Kubernetes Understand the architecture of Google Cloud Understand how pod networking works in Google Kubernetes Engine Create and manage Kubernetes Engine clusters using the Google Cloud Console and gcloud/kubectl commands   Course Outline Module 1: Introduction to Google Cloud -Use the Google Cloud Console-Use Cloud Shell-Define Cloud Computing-Identify Google Cloud compute services-Understand Regions and Zones-Understand the Cloud Resource Hierarchy-Administer your Google Cloud Resources Module 2: Containers and Kubernetes in Google Cloud -Create a Container Using Cloud Build-Store a Container in Container Registry-Understand the Relationship Between Kubernetes and Google Kubernetes Engine (GKE)-Understand how to Choose Among Google Cloud Compute Platforms Module 3: Kubernetes Architecture -Understand the Architecture of Kubernetes: Pods, Namespaces-Understand the Control-plane Components of Kubernetes-Create Container Images using Cloud Build-Store Container Images in Container Registry-Create a Kubernetes Engine Cluster Module 4: Introduction to Kubernetes Workloads -The kubectl Command-Introduction to Deployments-Pod Networking-Volumes Overview [-]

COURSE OVERVIEW  Learn how to implement security principles within an enterprise network and how to overlay network design by using solutions such as SD-Access and SD-WAN. The automation and programmability of Enterprise networks is also incorporated in this course. This course will help you: Configure, troubleshoot, and manage enterprise wired and wireless networks Implement security principles within an enterprise network Earn 64 CE credits toward recertification   Please note that this course is a combination of Instructor-Led and Self-Paced Study - 5 days in the classroom and approx. 3 days of self study. The self-study content will be provided as part of the digital courseware that you receive at the beginning of the course and should be part of your preparation for the exam. Additional lab access will be provided at the end of the class, this will be valid for 60 hours or 90 days whichever is the shorter. It will be possible to complete all but 7 of the labs after the class. TARGET AUDIENCE Network engineers involved in the installation, support and troubleshooting of enterprise networks. COURSE OBJECTIVES After completing this course you should be able to: Illustrate the hierarchical network design model and architecture using the access, distribution, and core layers Compare and contrast the various hardware and software switching mechanisms and operation, while defining the Ternary Content Addressable Memory (TCAM) and Content Addressable Memory (CAM), along with process switching, fast switching, and Cisco Express Forwarding concepts Troubleshoot Layer 2 connectivity using VLANs and trunking Implementation of redundant switched networks using Spanning Tree Protocol Troubleshooting link aggregation using EtherChannel Describe the features, metrics, and path selection concepts of Enhanced Interior Gateway Routing Protocol (EIGRP) Implementation and optimization of Open Shortest Path First (OSPF)v2 and OSPFv3, including adjacencies, packet types, and areas, summarization, and route filtering for IPv4 and IPv6 Implementing External Border Gateway Protocol (EBGP) interdomain routing, path selection, and single and dual-homed networking Implementing network redundancy using protocols including Hot Standby Routing Protocol (HSRP) and Virtual Router Redundancy Protocol (VRRP) Implementing internet connectivity within Enterprise using static and dynamic Network Address Translation (NAT) Describe the virtualization technology of servers, switches, and the various network devices and components Implementing overlay technologies such as Virtual Routing and Forwarding (VRF), Generic Routing Encapsulation (GRE), VPN, and Location Identifier Separation Protocol (LISP) Describe the components and concepts of wireless networking including Radio Frequency (RF) and antenna characteristics, and define the specific wireless standards Describe the various wireless deployment models available, include autonomous Access Point (AP) deployments and cloud-based designs within the centralized Cisco Wireless LAN Controller (WLC) architecture Describe wireless roaming and location services Describe how APs communicate with WLCs to obtain software, configurations, and centralized management Configure and verify Extensible Authentication Protocol (EAP), WebAuth, and Pre-shared Key (PSK) wireless client authentication on a WLC Troubleshoot wireless client connectivity issues using various available tools Troubleshooting Enterprise networks using services such as Network Time Protocol (NTP), Simple Network Management Protocol (SNMP), Cisco Internetwork Operating System (Cisco IOS®) IP Service Level Agreements (SLAs), NetFlow, and Cisco IOS Embedded Event Manager Explain the use of available network analysis and troubleshooting tools, which include show and debug commands, as well as best practices in troubleshooting Configure secure administrative access for Cisco IOS devices using the Command-Line Interface (CLI) access, Role-Based Access Control (RBAC), Access Control List (ACL), and Secure Shell (SSH), and explore device hardening concepts to secure devices from less secure applications, such as Telnet and HTTP Implement scalable administration using Authentication, Authorization, and Accounting (AAA) and the local database, while exploring the features and benefits Describe the enterprise network security architecture, including the purpose and function of VPNs, content security, logging, endpoint security, personal firewalls, and other security features Explain the purpose, function, features, and workflow of Cisco DNA Centre™ Assurance for Intent-Based Networking, for network visibility, proactive monitoring, and application experience Describe the components and features of the Cisco SD-Access solution, including the nodes, fabric control plane, and data plane, while illustrating the purpose and function of the Virtual Extensible LAN (VXLAN) gateways Define the components and features of Cisco SD-WAN solutions, including the orchestration plane, management plane, control plane, and data plane Describe the concepts, purpose, and features of multicast protocols, including Internet Group Management Protocol (IGMP) v2/v3, Protocol-Independent Multicast (PIM) dense mode/sparse mode, and rendezvous points Describe the concepts and features of Quality of Service (QoS), and describe the need within the enterprise network Explain basic Python components and conditionals with script writing and analysis Describe network programmability protocols such as Network Configuration Protocol (NETCONF) and RESTCONF Describe APIs in Cisco DNA Centre and vManage COURSE CONTENT Examining Cisco Enterprise Network Architecture Cisco Enterprise Architecture Model Campus LAN Design Fundamentals Traditional Multilayer Campus Layer Design Campus Distribution Layer Design   Understanding Cisco Switching Paths Layer 2 Switch Operation Control and Data Plane Cisco Switching Mechanisms Implementing Campus LAN Connectivity Revisiting VLANs Trunking with 802.1Q Inter-VLAN Routing Building Redundant Switched Topology Spanning-Tree Protocol Overview Spanning-Tree Protocol Operation Spanning-Tree Protocols Types and Features Multiple Spanning Tree Protocol PortFast and BPDU Implementing Layer 2 Port Aggregation (Self-Study) Need for EtherChannel EtherChannel Mode Interactions Layer 2 EtherChannel Configuration Guidelines EtherChannel Load-Balancing Options Troubleshoot EtherChannel Issues Understanding EIGRP EIGRP Features EIGRP Reliable Transport Establishing EIGRP Neighbour Adjacency EIGRP Metrics EIGRP Path Selection Explore EIGRP Path Selection Explore EIGRP Load Balancing and Sharing EIGRP for IPv6 Compare EIGRP and OSPF Routing Protocols Implementing OSPF Describe OSPF The OSPF Process OSPF Neighbour Adjacencies Building a Link-State Database OSPF LSA Types Compare Single-Area and Multi-Area OSPF OSPF Area Structure OSPF Network Types Optimizing OSPF OSPF Cost OSPF Route Summarization Benefits OSPF Route Filtering Tools Compare OSPFv2 and OSPFv3 Exploring EBGP Interdomain Routing with BGP BGP Operations Types of BGP Neighbour Relationships BGP Path Selection BGP Path Attributes Implementing Network Redundancy Need for Default Gateway Redundancy Define FHRP HSRP Advanced Features Cisco Switch High Availability Features Implementing NAT Define Network Address Translation NAT Address Types Explore NAT Implementations NAT Virtual Interface Introducing Virtualisation Protocols and Techniques Server Virtualisation Need for Network Virtualisation Path Isolation Overview Introducing VRF Introducing Generic Routing Encapsulation Introducing Virtualisation Protocols and Techniques Server Virualization Need for Network Virtualisation Path Isolation Overview Introducing VRF Introducing Generic Routing Encapsulation Understanding Virtual Private Networks and Interfaces Site-to-Site VPN Technologies IPSec VPN Overview IPSec: IKE IPsec Modes IPsec VPN Types Cisco IOS VTI Understanding Wireless Principles Explain RF Principles Describe Watts and Decibels Describe Antenna Characteristics Describe IEEE Wireless Standards Identify Wireless Component Roles Examining Wireless Deployment Options Wireless Deployment Overview Describe Autonomous AP Deployment Describe Centralized Cisco WLC Deployment Describe FlexConnect Deployment Cloud Deployment and Its Effect on Enterprise Networks Describe the Cloud-Managed Meraki Solution Cisco Catalyst 9800 Series Controller Deployment Options Describe Cisco Mobility Express Understanding Wireless Roaming and Location Services Wireless Roaming Overview Mobility Groups and Domains Wireless Roaming Types Describe Location Services Examining Wireless AP Operation Universal AP Priming Explore the Controller Discovery Process Describe AP Failover Explain High Availability Explore AP Modes Understanding Wireless Client Authentication Authentication Methods Pre-Shared Key (PSK) Authentication 802.1X User Authentication Overview PKI and 802.1X Certificate Based Authentication Introduction to Extensible Authentication Protocol EAP-Transport Layer Security (EAP-TLS) Protected Extensible Authentication Protocol EAP-FAST Guest Access with Web Auth Troubleshooting Wireless Client Connectivity Wireless Troubleshooting Tools Overview Spectrum Analysis Wi-Fi Scanning Packet Analysis Cisco AireOS GUI and CLI Tools Cisco Wireless Config Analyzer Express Common Wireless Client Connectivity Issues Overview Client to AP Connectivity WLAN Configuration Infrastructure Configuration Introducing Multicast Protocols (Self-study) Multicast Overview Internet Group Management Protocol Multicast Distribution Trees IP Multicasting Routing Rendezvous Point Introducing QoS (Self-study) Understand the Impact of User Applications on the Network Need for Quality of Service (QoS) Describe QoS Mechanisms Define and Interpret a QoS Policy Implementing Network Services Understanding Network Time Protocol Logging Services Simple Network Management Protocol Introducing NetFlow Flexible NetFlow Understanding Cisco IOS Embedded Event Manager Using Network Analysis Tools Troubleshooting Concepts Network Troubleshooting Procedures: Overview Network Troubleshooting Procedures: Case Study Basic Hardware Diagnostics Filtered Show Commands Cisco IOS IP SLAs Switched Port Analyzer(SPAN) Overview Remote SPAN (RSPAN) Encapsulated Remote Switched Port Analyzer(ERSAPN) Cisco Packet Capture Tools Overview Implementing Infrastructure Security ACL Overview ACL Wildcard Masking Types of ACLs Configure Numbered Access Lists Use ACLs to Filter Network Traffic Apply ACLs to Interfaces Configured Named Access Lists Control Plane Overview Control Plane Policing Implementing Secure Access Control Securing Device Access AAA Framework Overview Benefits of AAA Usage Authentication Options RADIUS and TACACS+ Enabling AAA and Configuring a Local User for Fallback Configuring RADIUS for Console and VTY Access Configuring TACACS+ for Console and VTY Access Configure Authorization and Accounting Understanding Enterprise Network Security Architecture (Self-study) Explore Threatscape Cisco Intrusion Prevention Systems Virtual Private Networks Content Security Logging Endpoint Security Personal Firewalls Antivirus and Antispyware Centralized Endpoint Policy Enforcement Cisco AMP for Endpoints Firewall Concepts TrustSec MACsec Identity Management 802.1X for Wired and Wireless Endpoint Authentication MAC Authentication Bypass Web Authentication Exploring Automation and Assurance Using Cisco DNA Centre (Self-study) Need for Digital Transformation Cisco Digital Network Architecture Cisco Intent-Based Networking Cisco DNA Centre Automation Overview Cisco DNA Centre Platform Overview Cisco DNA Centre Design Cisco DNA Centre Inventory Overview Cisco DNA Centre Configuration and Management Overview Onboarding of Network Devices Using Cisco DNA Centre Cisco DNA Centre Software Image Management Overview Cisco DNA Assurance Key Features and Use Cases Cisco DNA Centre Assurance Implementation Workflow Examining the Cisco SD-Access Solution (Self-study) Need for Cisco SD-Access Cisco SD Access Overview Cisco SD-Access Fabric Components Cisco SD-Access Fabric Control Plane Based on LISP Cisco SD-Access Fabric Control Plane Based on VXLAN Cisco SD-Access Fabric Control Plane Based on Cisco TrustSec Role of Cisco ISE and Cisco DNA Centre in SD-Access Cisco SD-Access Wireless Integration Traditional Campus Interoperating with Cisco SD-Access Understanding the Working Principles of the Cisco SD-WAN Solution (Self-study) Need for Software Defined Networking for WAN Cisco SD-WAN Components and Functions Cisco SD-WAN Orchestration Plane Cisco SD-WAN Management Plane- vManage Cisco SD-WAN Control Plane - vSmart Cisco SD-WAN Data Plane - WAN Edge Cisco SD-WAN Programmatic APIs Cisco SD-WAN Automation and Analytics Cisco SD-WAN Terminology Cisco IOS XE and IOS XE SD-WAN Software Flexible Controller Deployment Options Cisco SD-WAN Security Understanding the Basics of Python Programming Describe Python Concepts String Data Types Numbers Data Types Boolean Data Types Script Writing and Execution Analyse Code Introducing Network Programmability Protocols Configuration Management Evolution of Device Management and Programmability Data Encoding Formats Understanding JSON Model Driven Programmability Stack Introduction to YANG Types of YANG Models Understanding NETCONF Explain NETCONF and YANG REST Understanding RESTCONF Protocol Introducing APIs in Cisco DNA Centre and vManage (Self-study) Application Programming Interfaces REST API Response Codes and Results REST API Security Cisco DNA Centre APIs Cisco SD-WAN REST API Overview Labs Lab 1: Investigate the CAM Lab 2: Analyse Cisco Express Forwarding Lab 3: Troubleshoot VLAN and Trunk Issues Lab 4: Tuning STP and Configuring RSTP Lab 5: Configure Multiple Spanning Tree Protocol Lab 6: Troubleshoot EtherChannel Lab 7: Implementing Multiarea OSPF Lab 8: Implement OSPF Tuning Lab 9: Apply OSPF Optimization Lab 10: Implement OSPFv3 Lab 11: Configure and Verify Single-Homed EBGP Lab 12: Implementing HSRP Lab 13: Configure VRRP Lab 14: Implement NAT Lab 15: Configure and Verify VRF Lab 16: Configure and Verify a GRE Tunnel Lab 17: Configure Static VTI Point-to-Point Tunnels Lab 18: Configure Wireless Client Authentication in a Centralized Deployment (No Extended Access) Lab 19: Troubleshoot Wireless Client Connectivity Issues (No Extended Access) Lab 20: Configure Syslog Lab 21: Configure and Verify Flexible NetFlow Lab 22: Configuring Cisco IOS Embedded Event Manager (EEM) Lab 23: Troubleshoot Connectivity and Analyse Traffic with Ping, Traceroute and Debug Lab 24: Configure and Verify Cisco IP SLA's Lab 25: Configure Standard and Extended ACLs Lab 26: Configure Control Plane Policing Lab 27: Implement Local and Server-Based AAA (No Extended Access) Lab 28: Writing and Troubleshooting Python Scripts (No Extended Access) Lab 29: Explore JSON Objects and Scripts in Python (No Extended Access) Lab 30: Use NETCONF via SSH (No Extended Access) Lab 31: Use RESTCONF with Cisco IOS XE Software (No Extended Access) [-]

I dette kurset kombinerer vi teori med praksis. Gjennom relevante oppgaver får du forståelse og ferdigheter til å trekke ut data og gjøre analyser av hva som skjer på ditt nettsted. Du vil lære hvordan du kan måle effekt av endringer i løsningen, design og markedsføringstiltak. Google Analytics gir deg det datagrunnlaget du trenger for å lage rapporter og analyser for en faktabasert forståelse av hvordan den digitale løsningen fungerer.  Etter kurset vil du kunne hente ut data og lage analyserapporter som gir innsikt og støtte til din markedsføring og kommunikasjon, samt en god utvikling og forbedring av nettstedet. Noen av temaene som dekkes i kurset er: Hva er webanalyse og hvordan fungerer Google Analytics Sentrale begreper De viktigste rapportene Eventtracking / brukeradferd Hva må du vite om oppsett KPIer og måling - hva er viktig å måle Hvordan bruke GA sammen med andre relevante verktøy som Google Data Studio, Google Tag Manager, Google Search Console [-]

    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 MEP MagiCAD El Basis II   Her er et utvalg av temaene du vil lære på kurset: Bruke funksjoner i Revit MEP, MagiCAD og NTI Tools Gjennomføre prosjektoppstart med en sentral fil Bruk av Revit familier i sammenheng med delte parametere Utforme elektroinstallasjonstegninger slik at mest mulig informasjon kan utnyttes med hensyn til BIM. Etter kurset kan du bruke funksjoner i Revit MEP, MagiCAD og NTI Tools. Gjennomføre prosjektoppstart med en sentral fil. Bruk av Revit familier i sammenheng med delte parametere. Lære å utforme elektroinstallasjonstegninger slik at mest mulig informasjon kan utnyttes med hensyn til BIM.   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 [-]

Bli en ekspert i online annonsering med Google Ads gjennom dette dyptgående kurset ledet av Espen Faugstad, gründer av Utdannet.no og en veteran med over 10 års erfaring i digital markedsføring. Dette kurset er skreddersydd for alle, fra de som aldri har brukt Google Ads før, til de som har erfaring men ønsker å heve sin kompetanse til ekspertnivå. Kurset starter med grunnleggende om hvordan du oppretter og konfigurerer en Google Ads-konto. Du vil lære å installere Google Ads-taggen og konverteringssporing, utføre målgruppe- og søkeordsanalyse, og forstå hvordan Google Ads-auksjonen fungerer. Kurset dekker også hvordan du oppretter og optimaliserer ulike typer annonser, inkludert tekst-, bilde-, video- og remarketingannonser. Med en praktisk tilnærming vil kurset guide deg gjennom prosessen med å sette opp effektive kampanjer, forstå auksjonssystemet, og bruke analyseverktøy for å forbedre dine resultater. Ved kursets slutt vil du ha tilegnet deg den kunnskapen du trenger for å mestre Google Ads og drive effektiv annonsering på vegne av deg selv eller dine klienter.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Målgruppe Kapittel 3: Søkeord Kapittel 4: Auksjon Kapittel 5: Tekstannonser Kapittel 6: Bildeannonser Kapittel 7: Videoannonser Kapittel 8: Remarketing Kapittel 9: Analyse Kapittel 10: Avslutning   Varighet: 5 timer og 12 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. [-]

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 [-]