Oversikt over relevante kurs innen Kurs i programvare og applikasjoner i Hamar

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

Dette kurset er skreddersydd for deltakere som allerede har fullført vårt grunnleggende Excel-kurs og nå ønsker å ta sine ferdigheter til et avansert nivå. Kursinstruktør Espen Faugstad vil veilede deg gjennom en rekke avanserte emner, inkludert opprettelse av pivottabeller, bruk av funksjoner og formler, og mye mer. Kurset dekker grundig bruken av en rekke funksjoner og formler, inkludert SUMMER, MIN, MAKS, AVKORT, AVRUND, ANTALL, ANTALLA, KJEDE.SAMMEN, TRIMME, VENSTRE, HØYRE, DELTEKST, FINN.RAD, HVIS, SUMMERHVIS, ANTALL.HVIS og GJENNOMSNITTHVIS. I tillegg vil kurset veilede deg gjennom henting av ekstern data, sortering og filtrering, fjerning av duplikater, og gruppering av data.   Innhold: Kapittel 1: Pivottabeller Kapittel 2: Formler og Funksjoner Kapittel 3: Formelrevisjon Kapittel 4: Ekstern Data Kapittel 5: Sortering og Filtrering Kapittel 6: Dataverktøy Kapittel 7: Gruppering av Data Kapittel 8: Arkbeskyttelse Kapittel 9: Avslutning   Varighet: 2 timer og 17 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer på samme måte som strømmetjenester for musikk eller TV-serier, der våre kunder betaler en fast månedspris for tilgang til alle kursene vi har tilgjengelig. Vi har opplevd betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling engasjerende, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]

Develop dynamic reports with Microsoft Power BI [-]

Hvem passer kurset for? Deg som lager presentasjoner eller presenterer/markedsfører innhold på nett. Forhåndskunnskap: Kurset llustrator innføring eller tilsvarende kunnskap Dette lærer du: Teknikker til tegning av enkle figurer og former til bruk i illustrasjonen Bruk av tall i diagrammer Tilpasning av diagrammets utseende Fargebruk Teknikker for å hente inn nye idéer https://igm.no/infografikk/ [-]

Kurset består av tre AutoCAD kursmoduler - 2D Grunnkurs og 2D Videregående, samt introduksjon til 3D modellering. Kurset gir deltakeren meget god kunnskap om AutoCAD, samt erfaring med utarbeidelse og oppbygging av tegninger. Mange av våre deltakerere har gjennomført kurset for å benytte dette på en CV ved søknad på ny jobb.   Følgende kursmoduler gjennomføres i dette kurset:  AutoCAD 2D Grunnkurs Hovedprinsipper i AutoCAD's brukergrensesnitt Oppretting og lagring av tegninger Tegne- og editeringskommandoer Hjelpefunksjoner for å tegne nøyaktig Skjermstyring Lagoppbygging og struktur Målsetting, teksting og skravering Symbol- og blokkhåndtering Layout/plotting AutoCAD 2D Videregående kurs Tilpasse AutoCAD til eget brukermiljø Blokker med attributter og uttrekk til tabell/Excel Tabeller og Fields XREF - eksterne referanser Import og håndtering av PDF filer Innsetting av andre filformater som eks. DWF, raster filer og DGN Definering og bruk av annotative objekter ved målsetting og teksting. Avansert plotting Funksjoner i Express Tools AutoCAD 3D introduksjonskurs Koordinatsystemer Angivelse av punkter i rommet Solid modellering Surface modellering Mesh modellering Sette opp Layout i paperspace, projeksjoner og snitt Lagstruktur og lagdefinisjon, farger, linjetyper, målsetting Lyssetting, naturlig sollys og lokale lyskilder Knytte materialer til objekt eller til lag Renderfunksjoner Animasjon   Oppgaver Oppgaver knyttet til kurset, samt tilleggsoppgaver ift. fag.   Prosjektoppgaver Prosjektoppgaver knyttet til 2D tegning og 3D modellering.     [-]

Elæring Cisco U. All ACCESS [-]

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 [-]

COURSE OVERVIEW ou will learn how to configure and manage Cisco Nexus 9000 Series Switches in ACI mode, how to connect the Cisco ACI fabric to external networks and services, and fundamentals of Virtual Machine Manager (VMM) integration. You will gain hands-on practice implementing key capabilities such as fabric discovery, policies, connectivity, VMM integration, and more. This course is based on ACI Software v5.2 release.   This course helps you prepare to take the exam, Implementing Cisco Application Centric Infrastructure(300-620 DCACI), which leads to CCNP® Data Center and Cisco Certified Specialist – Data Center ACI Implementation certifications. TARGET AUDIENCE Individuals who need to understand how to configure and manage a data center network environment with the Cisco Nexus 9000 Switch operating in ACI Mode.   COURSE OBJECTIVES After completing this course, you should be able to: Describe Cisco ACI Fabric Infrastructure and basic Cisco ACI concepts Describe Cisco ACI policy model logical constructs Describe Cisco ACI basic packet forwarding Describe external network connectivity Describe VMM Integration Describe Layer 4 to Layer 7 integrations Explain Cisco ACI management features COURSE CONTENT Introducing Cisco ACI Fabric Infrastructure and Basic Concepts What Is Cisco ACI? Cisco ACI Topology and Hardware Cisco ACI Object Model Faults, Event Record, and Audit Log Cisco ACI Fabric Discovery Cisco ACI Access Policies Describing Cisco ACI Policy Model Logical Constructs Cisco ACI Logical Constructs Tenant Virtual Routing and Forwarding Bridge Domain Endpoint Group Application Profile Tenant Components Review Adding Bare-Metal Servers to Endpoint Groups Contracts Describing Cisco ACI Basic Packet Forwarding Endpoint Learning Basic Bridge Domain Configuration **** Introducing External Network Connectivity Cisco ACI External Connectivity Options External Layer 2 Network Connectivity External Layer 3 Network Connectivity Introducing VMM Integration VMware vCenter VDS Integration Resolution Immediacy in VMM Alternative VMM Integrations Describing Layer 4 to Layer 7 Integrations Service Appliance Insertion Without ACI L4-L7 Service Graph Service Appliance Insertion via ACI L4-L7 Service Graph Service Graph Configuration Workflow Service Graph PBR Introduction Explaining Cisco ACI Management Out-of-Band Management In-Band Management Syslog Simple Network Management Protocol Configuration Backup Authentication, Authorization, and Accounting Role-Based Access Control Cisco ACI Upgrade Collect Tech Support Labs Validate Fabric Discovery Configure Network Time Protocol (NTP) Create Access Policies and Virtual Port Channel (vPC) Enable Layer 2 Connectivity in the Same Endpoint Group (EPG) Enable Inter-EPG Layer 2 Connectivity Enable Inter-EPG Layer 3 Connectivity Compare Traffic Forwarding Methods in a Bridge Domain Configure External Layer 2 (L2Out) Connection Configure External Layer 3 (L3Out) Connection Integrate Application Policy Infrastructure Controller (APIC) With VMware vCenter Using VMware Distributed Virtual Switch (DVS) TEST CERTIFICATION Recommended as preparation for the following exams: 300-620 DCACI - Implementing Cisco Application Centric Infrastructure [-]

The students will learn how to interactively explore data stored in files in a data lake. They will learn the various ingestion techniques that can be used to load data using the Apache Spark capability found in Azure Synapse Analytics or Azure Databricks, or how to ingest using Azure Data Factory or Azure Synapse pipelines. The students will also learn the various ways they can transform the data using the same technologies that is used to ingest data. The student will spend time on the course learning how to monitor and analyze the performance of analytical system so that they can optimize the performance of data loads, or queries that are issued against the systems. They will understand the importance of implementing security to ensure that the data is protected at rest or in transit. The student will then show how the data in an analytical system can be used to create dashboards, or build predictive models in Azure Synapse Analytics. After completing this course, students will be able to: Explore compute and storage options for data engineering workloads in Azure Design and Implement the serving layer Understand data engineering considerations Run interactive queries using serverless SQL pools Explore, transform, and load data into the Data Warehouse using Apache Spark Perform data Exploration and Transformation in Azure Databricks Ingest and load Data into the Data Warehouse Transform Data with Azure Data Factory or Azure Synapse Pipelines Integrate Data from Notebooks with Azure Data Factory or Azure Synapse Pipelines Optimize Query Performance with Dedicated SQL Pools in Azure Synapse Analyze and Optimize Data Warehouse Storage Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link Perform end-to-end security with Azure Synapse Analytics Perform real-time Stream Processing with Stream Analytics Create a Stream Processing Solution with Event Hubs and Azure Databricks Build reports using Power BI integration with Azure Synpase Analytics Perform Integrated Machine Learning Processes in Azure Synapse Analytics Course prerequisites Successful students start this course with knowledge of cloud computing and core data concepts and professional experience with data solutions.Recommended prerequisites:M-DP900 - Microsoft Azure Data FundamentalsM-AZ900 - Microsoft Azure Fundamentals Agenda Module 1: Explore compute and storage options for data engineering workloads This module provides an overview of the Azure compute and storage technology options that are available to data engineers building analytical workloads. This module teaches ways to structure the data lake, and to optimize the files for exploration, streaming, and batch workloads. The student will learn how to organize the data lake into levels of data refinement as they transform files through batch and stream processing. Then they will learn how to create indexes on their datasets, such as CSV, JSON, and Parquet files, and use them for potential query and workload acceleration. Module 2: Design and implement the serving layer This module teaches how to design and implement data stores in a modern data warehouse to optimize analytical workloads. The student will learn how to design a multidimensional schema to store fact and dimension data. Then the student will learn how to populate slowly changing dimensions through incremental data loading from Azure Data Factory. Module 3: Data engineering considerations for source files This module explores data engineering considerations that are common when loading data into a modern data warehouse analytical from files stored in an Azure Data Lake, and understanding the security consideration associated with storing files stored in the data lake. Module 4: Run interactive queries using Azure Synapse Analytics serverless SQL pools In this module, students will learn how to work with files stored in the data lake and external file sources, through T-SQL statements executed by a serverless SQL pool in Azure Synapse Analytics. Students will query Parquet files stored in a data lake, as well as CSV files stored in an external data store. Next, they will create Azure Active Directory security groups and enforce access to files in the data lake through Role-Based Access Control (RBAC) and Access Control Lists (ACLs). Module 5: Explore, transform, and load data into the Data Warehouse using Apache Spark This module teaches how to explore data stored in a data lake, transform the data, and load data into a relational data store. The student will explore Parquet and JSON files and use techniques to query and transform JSON files with hierarchical structures. Then the student will use Apache Spark to load data into the data warehouse and join Parquet data in the data lake with data in the dedicated SQL pool. Module 6: Data exploration and transformation in Azure Databricks This module teaches how to use various Apache Spark DataFrame methods to explore and transform data in Azure Databricks. The student will learn how to perform standard DataFrame methods to explore and transform data. They will also learn how to perform more advanced tasks, such as removing duplicate data, manipulate date/time values, rename columns, and aggregate data. Module 7: Ingest and load data into the data warehouse This module teaches students how to ingest data into the data warehouse through T-SQL scripts and Synapse Analytics integration pipelines. The student will learn how to load data into Synapse dedicated SQL pools with PolyBase and COPY using T-SQL. The student will also learn how to use workload management along with a Copy activity in a Azure Synapse pipeline for petabyte-scale data ingestion. Module 8: Transform data with Azure Data Factory or Azure Synapse Pipelines This module teaches students how to build data integration pipelines to ingest from multiple data sources, transform data using mapping data flowss, and perform data movement into one or more data sinks. Module 9: Orchestrate data movement and transformation in Azure Synapse Pipelines In this module, you will learn how to create linked services, and orchestrate data movement and transformation using notebooks in Azure Synapse Pipelines. Module 10: Optimize query performance with dedicated SQL pools in Azure Synapse In this module, students will learn strategies to optimize data storage and processing when using dedicated SQL pools in Azure Synapse Analytics. The student will know how to use developer features, such as windowing and HyperLogLog functions, use data loading best practices, and optimize and improve query performance. Module 11: Analyze and Optimize Data Warehouse Storage In this module, students will learn how to analyze then optimize the data storage of the Azure Synapse dedicated SQL pools. The student will know techniques to understand table space usage and column store storage details. Next the student will know how to compare storage requirements between identical tables that use different data types. Finally, the student will observe the impact materialized views have when executed in place of complex queries and learn how to avoid extensive logging by optimizing delete operations. Module 12: Support Hybrid Transactional Analytical Processing (HTAP) with Azure Synapse Link In this module, students will learn how Azure Synapse Link enables seamless connectivity of an Azure Cosmos DB account to a Synapse workspace. The student will understand how to enable and configure Synapse link, then how to query the Azure Cosmos DB analytical store using Apache Spark and SQL serverless. Module 13: End-to-end security with Azure Synapse Analytics In this module, students will learn how to secure a Synapse Analytics workspace and its supporting infrastructure. The student will observe the SQL Active Directory Admin, manage IP firewall rules, manage secrets with Azure Key Vault and access those secrets through a Key Vault linked service and pipeline activities. The student will understand how to implement column-level security, row-level security, and dynamic data masking when using dedicated SQL pools. Module 14: Real-time Stream Processing with Stream Analytics In this module, students will learn how to process streaming data with Azure Stream Analytics. The student will ingest vehicle telemetry data into Event Hubs, then process that data in real time, using various windowing functions in Azure Stream Analytics. They will output the data to Azure Synapse Analytics. Finally, the student will learn how to scale the Stream Analytics job to increase throughput. Module 15: Create a Stream Processing Solution with Event Hubs and Azure Databricks In this module, students will learn how to ingest and process streaming data at scale with Event Hubs and Spark Structured Streaming in Azure Databricks. The student will learn the key features and uses of Structured Streaming. The student will implement sliding windows to aggregate over chunks of data and apply watermarking to remove stale data. Finally, the student will connect to Event Hubs to read and write streams. Module 16: Build reports using Power BI integration with Azure Synapase Analytics In this module, the student will learn how to integrate Power BI with their Synapse workspace to build reports in Power BI. The student will create a new data source and Power BI report in Synapse Studio. Then the student will learn how to improve query performance with materialized views and result-set caching. Finally, the student will explore the data lake with serverless SQL pools and create visualizations against that data in Power BI. Module 17: Perform Integrated Machine Learning Processes in Azure Synapse Analytics This module explores the integrated, end-to-end Azure Machine Learning and Azure Cognitive Services experience in Azure Synapse Analytics. You will learn how to connect an Azure Synapse Analytics workspace to an Azure Machine Learning workspace using a Linked Service and then trigger an Automated ML experiment that uses data from a Spark table. You will also learn how to use trained models from Azure Machine Learning or Azure Cognitive Services to enrich data in a SQL pool table and then serve prediction results using Power BI. [-]

Jira Project Administration (Cloud) [-]

COURSE OVERVIEW The course is not designed to teach students to become professional data scientists or software developers, but rather to build awareness of common AI workloads and the ability to identify Azure services to support them. The course is designed as a blended learning experience that combines instructor-led training with online materials on the Microsoft Learn platform (https://azure.com/learn). The hands-on exercises in the course are based on Learn modules, and students are encouraged to use the content on Learn as reference materials to reinforce what they learn in the class and to explore topics in more depth. TARGET AUDIENCE The Azure AI Fundamentals course is designed for anyone interested in learning about the types of solution artificial intelligence (AI) makes possible, and the services on Microsoft Azure that you can use to create them. You don’t need to have any experience of using Microsoft Azure before taking this course, but a basic level of familiarity with computer technology and the Internet is assumed. Some of the concepts covered in the course require a basic understanding of mathematics, such as the ability to interpret charts. The course includes hands-on activities that involve working with data and running code, so a knowledge of fundamental programming principles will be helpful. COURSE OBJECTIVES  After completing this course, you will be able to: Describe Artificial Intelligence workloads and considerations Describe fundamental principles of machine learning on Azure Describe features of computer vision workloads on Azure Describe features of Natural Language Processing (NLP) workloads on Azure Describe features of conversational AI workloads on Azure   COURSE CONTENT Module 1: Introduction to AI In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development. Artificial Intelligence in Azure Responsible AI After completing this module you will be able to Describe Artificial Intelligence workloads and considerations Module 2: Machine Learning Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models. Introduction to Machine Learning Azure Machine Learning After completing this module you will be able to Describe fundamental principles of machine learning on Azure Module 3: Computer Vision Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services. Computer Vision Concepts Computer Vision in Azure After completing this module you will be able to Describe features of computer vision workloads on Azure Module 4: Natural Language Processing This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands. After completing this module you will be able to Describe features of Natural Language Processing (NLP) workloads on Azure Module 5: Conversational AI Conversational AI enables users to engage in a dialog with an AI agent, or *bot*, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions. Conversational AI Concepts Conversational AI in Azure After completing this module you will be able to Describe features of conversational AI workloads on Azure   TEST CERTIFICATION Recommended as preparation for the following exams: Exam AI-900: Microsoft Azure AI Fundamentals. HVORFOR VELGE SG PARTNER AS:  Flest kurs med Startgaranti Rimeligste kurs Beste service og personlig oppfølgning Tilgang til opptak etter endt kurs Partner med flere av verdens beste kursleverandører [-]

ISTQB Foundation v4.0 Certificate [-]

Dette kurset tilbys som bedriftsinternt kurs   Workshopen består av et minimum med teori og et maksimum av praktiske øvelser hvor vi lager streams av  Arrays, List, Set, Map og Files - filtrerer, mapper til nye objekter, utfører aggregeringer og konverterer tilbake til nye collections mm.   Workshopen vil dekke bl.a. Sette opp en stream, med Stream.of(), IntStream.of() og DoubleStream.of() Konvertere et Array til en stream med Arrays.stream() Konvertere en collection av typen List, Set eller Map til en stream med stream() Filtrere ut verdier med filter() Mappe til nye objekter med map() og flatMap() Sortere med sorted() og ulike typer Comparators Aggregere med reduce() og collect() Behandle hvert element med forEach() og forEachOrdered() Gruppere og telle opp forekomster i hver gruppe med collect() Konvertere tilbake til en collection med collect() Konvertere til et objekt med get() Begrense reultatet med limit() Hente enkel statistikk (min, max, average, sum) med reduce() og collect() og bl.a. summarizingInt() Bruke :: til metodereferanser Lese en fil inn i en stream med Files.lines() Behandle hvert element med forEach() og forEachOrdered() Workshopen holdes på norsk og går over 2 dager, fra 10.00-14.00, for tiden online, med dedikert lærer og Microsoft Teams som kommunikasjonsplattform.   [-]

Vil du jobbe enklere og mer effektivt i InDesign? På dette kurset vil du lære å lage gode, avanserte og tidsbesparende maler for sider, tekst og objekter, samt gjenbruk via biblioteker. Etter kurset kan du lage egne maler som automatiserer mange arbeidsprosesser og sparer deg for mye tid og arbeid. Gode maler kvalitetsikrer produktetene dine og gir deg mere tid til å være kreativ. Hvem passer kurset for? Kurset passer for deg som jobber i Adobe InDesign og ønsker å utnytte programmets potensiale. Forhåndskunnskap i InDesign: «InDesign grunnkurs» eller tilsvarende kunnskap. Dette lærer du: God, effektiv og avansert bruk av maler for sider, tekst og objekter i Adobe InDesign Spar på elementer du lager med CC Libraries Lage automatisk innholdsfortegnelse Bruk av tabell Tekstlenker og registerlinjer Hvordan tilpasse en layout til ulike størrelser i samme dokument Lage egne tastatursnarveier https://igm.no/indesign-kurs-videregaende/ [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Du må ha god kjennskap til Windows 2008 server, oppsett av AD og helst Exchange server Innleveringer: Øvinger: 8 må være godkjent.  Vurderingsform: 5 timer praktisk hjemmeeksamen med både teoretiske og praktiske oppgaver. Ansvarlig: Stein Meisingseth Eksamensdato: 09.12.13 / 12.05.14         Læremål: KUNNSKAPER:Kandidaten:- kjenner til bruken av skripting i forskjellige situasjoner i en bedrift/organisasjon- kjenner til forskjellige skripspråk- kan gjøre rede for hvordan skripting kan automatisere oppgaver i en driftssituasjon- kan bruke PowerShell for å automatisere driftsoppgaver i Windows server, VMware og andre driftsmiljøer FERDIGHETER:Kandidaten:- Powershell - historie- kan vise hvordan er PowerShell bygd opp- kan bruke PowerShell i Windows server- kan lage kommandoer og scripts i Powershell- PowerShell og .NET- kan bruke av PowerShell i Active Directory- kan bruke av PowerShell i VMware- kan bruke PowerShell i Exchange GENERELL KOMPETANSE:Kandidaten:- har kompetanse til selvstendig både å formidle og å ta i bruk sine kunnskaper og ferdigheter i en bedrift som vil automatisere typiske driftsoppgaver Innhold:- introduksjon til PowerShell 2 og 3 - hvordan lage script i PowerShell - kommandoer i PowerShell - forenkling og automatisering av drift av Windows OS med PowerShell - forenkling og automatisering av drift av Windows server med PowerShell - forenkling og automatisering av drift av Exchange server med PowerShell - forenkling og automatisering av drift av VMware med PowerShellLes mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Powershell i praktisk scripting 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]