Oversikt over relevante kurs innen IT-kurs i Trysil

ITIL® 4 Practitioner: Monitoring and Event Management [-]

Du vil få tilsendt en «Core guidance» bok og sertifiserings-voucher slik at du kan ta sertifiseringstesten for eksempel hjemme eller på jobb. Denne vil være gyldig i ett år. Tid for sertifiseringstest avtales som beskrevet i e-post med voucher. Eksamen overvåkes av en web-basert eksamensvakt.   Eksamen er på engelsk. Eksamensformen er multiple choice - 40 spørsmål skal besvares, og du består med 70% riktige svar (dvs. 28 av 40). Deltakerne har 1 time og 30 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.  Nødvendige forkunnskaper: Bestått ITIL Foundation sertifisering Gjennomført godkjent kurs/e-læring [-]

På webinaret går vi gjennom oppsett og presentasjonsopplevelser i Microsoft Teams møter. Teams er i rask utvikling og spesielt på presentasjonsfronten har det skjedd store endringer i det siste. Dette webinaret tar for seg alternativer i oppsett av møtet, tildele riktige roller og skape den beste presentasjonsopplevelsen for deltakerne. Opprette Teams-møter – via Outlook eller Teams | Ad Hoc møter | Kanalmøter | Innstillinger for møtet – i innkallelsen | Innstillinger og administrasjon av møtet – når møtet er i gang i Teams | Visninger | Presentere/dele – hva og hvordan| Presentasjonsvisninger | Teksting | Opptak | Møtenotater – før, under og etter Pris: 1690 kroner [-]

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     [-]

C# 12 Development and .NET 8 [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: Av totalt 12 øvinger må 4 tekstbaserte øvinger og 4 flervalgstester (totalt 8 øvinger) være godkjent for å få gå opp til eksamen. Øvingene som blir godkjent må dekke et bredt spekter av de temaene som gjennomgås i emnet. Nærmere opplysninger ved studiestart. Personlig veileder: ja Vurderingsform: Skriftlig, individuell, 3 timer,  Ansvarlig: Knut Arne Strand Eksamensdato: 17.12.13 / 20.05.14         Læremål: KUNNSKAPER:Kandidaten kan:- tegne opp og forklare livssyklusmodellen til ITIL versjon 3- gjengi suksessfaktorer knyttet til innføring av ITIL- forklare hva som menes med begrepene tjenestestrategi, tjenestedesign, tjenesteovergang, tjenesteadministrasjon og kontinuerlig tjenesteforbedring- beskrive utvalgte prosesser som inngår i ITIL-standarden og hvilke arbeidsoppgaver som utføres av de aktuelle prosessene FERDIGHETER:Kandidaten kan:- redegjøre for hvordan utvalgte ITIL-prosesser typisk kan implementeres og forvaltes i en gitt organisasjon- redegjøre for hvordan utvalgte prosesser fungerer sammen og hvilken informasjon som flyter mellom dem- lage en plan for innføring av utvalgte ITIL-prosesser i en konkret bedrift- implementere utvalgte deler av ITIL-standarden i en konkret bedrift GENERELL KOMPETANSE:Kandidaten har:- grunnleggende forståelse av ITIL som beste praksis for drift av IT-systemer- forståelse for at ITIL kan implementeres i små trinn og med noen få prosesser, samt utvides til å være svært omfattende, med mange operative prosesser og funksjoner Innhold:Introduksjon til ITIL v3 med utgangspunkt i tjenestelivssyklusmodellen. Gjennomgang av livssyklusfasene tjenestestrategi, tjenestedesign, tjenesteovergang, tjenesteadministrasjon og kontinuerlig tjenesteforbedring med tilhørende prosesser og funksjoner.Les mer om faget herDemo: Her er en introduksjonsvideo for faget Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag ITIL v3 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.  [-]

Blended Virtual CourseThe course is a hybrid of virtual training and self-study which will be a mixture of teaching using Microsoft Teams 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. You do not have to install Microsoft Teams, you will receive a link and can access the course using the web browser.  Remote proctored examTake your exam from any location. Read about iSQI remote proctored exam here Requirements for the exam: The exam will be using Google Chrome and there is a plug-in that needs to be installed  You will need a laptop/PC with a camera and a microphone  A current ID with a picture    KursinnholdDette 3 dagers kurset er rettet mot de som arbeider med teknisk testanalyse og som ønsker ytterligere kompetanse innenfor området. TTA kurset er basert på Foundation kursets pensum og gir grunnleggende ferdigheter innnenfor teknisk testanalyse til deltakerne. Bouvet sine kursdeltakeres testresultater vs ISTQB gjennomsnitt   Course content The course provides essential skills for every Technical Test Analyst -recognising typical risks related to technical aspects of the system being developed- select and apply appropriate structural design techniques for the required level of coverage- to effectively participate in a variety of documentation reviews- to understand the technical issues and concepts in applying test automation Other skills that will be attained include:-the ability to perform static and dynamic analysis and identify the various defects that can be found using these techniques. Quality characteristics are as important and, in some instances, more important than functionality, participants will understand the types of quality characteristics that the technical tester should identify and test. Exam The ISTQB Advanced TTA exam is a 2-hour multiple choice exam with the pass mark being 65%. There is an extra 30 minutes allowed for candidates whose first language is not English. You must hold the ISTQB Foundation certificate in software testing in order to sit this exam. The exam is a remote proctored exam. [-]

Students will learn how to plan for DevOps, use source control, scale Git for an enterprise, consolidate artifacts, design a dependency management strategy, manage secrets, implement continuous integration, implement a container build strategy, design a release strategy, set up a release management workflow, implement a deployment pattern, and optimize feedback mechanisms TARGET AUDIENCE Students in this course are interested in designing and implementing DevOps processes or in passing the Microsoft Azure DevOps Solutions certification exam. COURSE OBJECTIVES Plan for the transformation with shared goals and timelines Select a project and identify project metrics and Key Performance Indicators (KPI's) Create a team and agile organizational structure Design a tool integration strategy Design a license management strategy (e.g., Azure DevOps and GitHub users) Design a strategy for end-to-end traceability from work items to working software Design an authentication and access strategy Design a strategy for integrating on-premises and cloud resources Describe the benefits of using Source Control Describe Azure Repos and GitHub Migrate from TFVC to Git Manage code quality, including technical debt SonarCloud, and other tooling solutions Build organizational knowledge on code quality Explain how to structure Git repos Describe Git branching workflows Leverage pull requests for collaboration and code reviews Leverage Git hooks for automation Use Git to foster inner source across the organization Explain the role of Azure Pipelines and its components Configure Agents for use in Azure Pipelines Explain why continuous integration matters Implement continuous integration using Azure Pipelines Design processes to measure end-user satisfaction and analyze user feedback Design processes to automate application analytics Manage alerts and reduce meaningless and non-actionable alerts Carry out blameless retrospectives and create a just culture Define an infrastructure and configuration strategy and appropriate toolset for a release pipeline and application infrastructure Implement compliance and security in your application infrastructure Describe the potential challenges with integrating open-source software Inspect open-source software packages for security and license compliance Manage organizational security and compliance policies Integrate license and vulnerability scans into build and deployment pipelines Configure build pipelines to access package security and license ratings   COURSE CONTENT Module 1: Get started on a DevOps transformation journey Module 1 Lessons Introduction to DevOps Choose the right project Describe team structures Choose the DevOps tools Plan Agile with GitHub Projects and Azure Boards Introduction to source control Describe types of source control systems Work with Azure Repos and GitHub Lab 1: Agile planning and portfolio management with Azure Boards   Lab 2: Version controlling with Git in Azure Repos   After completing Module 1, students will be able to: Understand what DevOps is and the steps to accomplish it Identify teams to implement the process Plan for the transformation with shared goals and timelines Plan and define timelines for goals Understand different projects and systems to guide the journey Select a project to start the DevOps transformation Identify groups to minimize initial resistance Identify project metrics and Key Performance Indicators (KPI's) Understand agile practices and principles of agile development Create a team and agile organizational structure Module 2: Development for enterprise DevOps Module 2 Lessons Structure your Git Repo Manage Git branches and workflows Collaborate with pull requests in Azure Repos Explore Git hooks Plan foster inner source Manage Git repositories Identify technical debt Lab 3: Version controlling with Git in Azure Repos   After completing Module 2, students will be able to: Understand Git repositories Implement mono repo or multiple repos Explain how to structure Git Repos Implement a change log Describe Git branching workflows Implement feature branches Implement GitFlow Fork a repo Leverage pull requests for collaboration and code reviews Give feedback using pull requests Module 3: Implement CI with Azure Pipelines and GitHub Actions Module 3 Lessons Explore Azure Pipelines Manage Azure Pipeline agents and pools Describe pipelines and concurrency Explore Continuous integration Implement a pipeline strategy Integrate with Azure Pipelines Introduction to GitHub Actions Learn continuous integration with GitHub Actions Design a container build strategy Lab 4: Configuring agent pools and understanding pipeline styles   Lab 5: Enabling continuous integration with Azure Pipelines   Lab 6: Integrating external source control with Azure Pipelines   Lab 7: Implementing GitHub Actions by using DevOps Starter   Lab 8: Deploying Docker Containers to Azure App Service web apps   After completing Module 3, students will be able to: Describe Azure Pipelines Explain the role of Azure Pipelines and its components Decide Pipeline automation responsibility Understand Azure Pipeline key terms Choose between Microsoft-hosted and self-hosted agents Install and configure Azure pipelines Agents Configure agent pools Make the agents and pools secure Use and estimate parallel jobs Module 4: Design and implement a release strategy Module 4 Lessons Introduction to continuous delivery Create a release pipeline Explore release strategy recommendations Provision and test environments Manage and modularize tasks and templates Automate inspection of health Lab 9: Creating a release dashboard   Lab 10: Controlling deployments using Release Gates   After completing Module 4, students will be able to: Explain continuous delivery (CD) Implement continuous delivery in your development cycle Understand releases and deployment Identify project opportunities to apply CD Explain things to consider when designing your release strategy Define the components of a release pipeline and use artifact sources Create a release approval plan Implement release gates Differentiate between a release and a deployment Module 5: Implement a secure continuous deployment using Azure Pipelines Module 5 Lessons Introduction to deployment patterns Implement blue-green deployment and feature toggles Implement canary releases and dark launching Implement A/B testing and progressive exposure deployment Integrate with identity management systems Manage application configuration data Lab 11: Configuring pipelines as code with YAML   Lab 12: Setting up and running functional tests   Lab 13: Integrating Azure Key Vault with Azure DevOps   After completing Module 5, students will be able to: Explain the terminology used in Azure DevOps and other Release Management Tooling Describe what a Build and Release task is, what it can do, and some available deployment tasks Implement release jobs Differentiate between multi-agent and multi-configuration release job Provision and configure target environment Deploy to an environment securely using a service connection Configure functional test automation and run availability tests Setup test infrastructure Use and manage task and variable groups Module 6: Manage infrastructure as code using Azure and DSC Module 6 Lessons Explore infrastructure as code and configuration management Create Azure resources using Azure Resource Manager templates Create Azure resources by using Azure CLI Explore Azure Automation with DevOps Implement Desired State Configuration (DSC) Implement Bicep Lab 14: Azure deployments using Azure Resource Manager templates   After completing Module 6, students will be able to: Understand how to deploy your environment Plan your environment configuration Choose between imperative versus declarative configuration Explain idempotent configuration Create Azure resources using ARM templates Understand ARM templates and template components Manage dependencies and secrets in templates Organize and modularize templates Create Azure resources using Azure CLI Module 7: Implement security and validate code bases for compliance Module 7 Lessons Introduction to Secure DevOps Implement open-source software Software Composition Analysis Static analyzers OWASP and Dynamic Analyzers Security Monitoring and Governance Lab 15: Implement security and compliance in Azure Pipelines   Lab 16: Managing technical debt with SonarQube and Azure DevOps   After completing Module 7, students will be able to: Identify SQL injection attack Understand DevSecOps Implement pipeline security Understand threat modeling Implement open-source software Explain corporate concerns for open-source components Describe open-source licenses Understand the license implications and ratings Work with Static and Dynamic Analyzers Configure Microsoft Defender for Cloud Module 8: Design and implement a dependency management strategy Module 8 Lessons Explore package dependencies Understand package management Migrate, consolidate, and secure artifacts Implement a versioning strategy Introduction to GitHub Packages Lab 17: Package management with Azure Artifacts   After completing Module 8, students will be able to: Define dependency management strategy Identify dependencies Describe elements and componentization of a dependency management Scan your codebase for dependencies Implement package management Manage package feed Consume and create packages Publish packages Identify artifact repositories Migrate and integrate artifact repositories Module 9: Implement continuous feedback Module 9 Lessons Implement tools to track usage and flow Develop monitor and status dashboards Share knowledge within teams Design processes to automate application analytics Manage alerts, Blameless retrospectives and a just culture Lab 18: Monitoring application performance with Application Insights   Lab 19: Integration between Azure DevOps and Microsoft Teams   Lab 20: Sharing Team Knowledge using Azure Project Wikis   After completing Module 9, students will be able to: Implement tools to track feedback Plan for continuous monitoring Implement Application Insights Use Kusto Query Language (KQL) Implement routing for mobile applications Configure App Center Diagnostics Configure alerts Create a bug tracker Configure Azure Dashboards Work with View Designer in Azure Monitor [-]

Dette kurset tilbys som bedriftsinternt kurs   Fra mars 2020 til oktober 2020 økte antall daglige teams-brukere på verdensbasis fra 44 millioner til 115 millioner. Dette er en enorm økning og for mange innebar det å bli kastet inn i noe nytt uten opplæring. Det som egentlig skulle gjøre arbeidsdagen lettere, lagde flere utfordringer.  Appen er brukervennlig og fungerer sømløst med de andre verktøyene i Microsoft 365, men appen vokser ettersom behovene endrer seg, og med 115 millioner brukere er behovene også mange. Microsoft Teams er derfor et stort verktøy for god og effektiv samhandling, men verktøyene kan være så gode de bare vil – dersom de som bruker det ikke er kjent med funksjonalitetene som gjør Microsoft sømløst. I løpet av kurset vil deltagere få en god oversikt på hva Teams er, hva det kan brukes for, og hvordan best mulig bruke det. Kurset gir også tips og triks for best practice, samt hvordan man kan holde seg oppdatert på ny funksjonalitet som kommer.    Metode: Digitalkurs: Kursholder holder informative økter med gjennomgang og demonstrasjoner av de viktigste verktøyene, hvordan de virker sammen, og hvordan du og dine kollegaer bruker de effektivt sammen. Kursdeltakerne vil ha mulighet til å stille spørsmål, enten muntlig eller skriftlig, avhengig av gruppestørrelsen.    Kurs på Bouvethuset på Majorstuen: Kursholdere vil bruke en kombinasjon av undervisning, demonstrasjoner og øvelser så deltagerne kan raskt komme i gang og samtidig bygge en bevisst og robust tilnærming til verktøyet. Deltagkerne får da mulighet til å teste ut funksjonalitet underveis i Bouvet sitt kursmiljø.    Målgruppe Alle som ønsker en grundig introduksjon og opplæring i Teams applikasjonen. Det passer både for deg som aldri har brukt Teams før, og for deg som ønsker å utvikle deg til superbruker. Spesielt egnet for deg som skal være med å spesifisere, tilrettelegge eller være ansvarlig for innføring av samhandlingsløsninger.   Kursinnhold •    Teams som del av Office 365 plattformen•    Oversikt over begreper, lisensmodeller og relatert•    Pålogging, navigasjon og grensesnitt i Teams•    Hvordan strukturere velfungerende Teams: kanaler, faner, og mer•    Fillagring og fildeling•    Samtaler, chat og virtuelle møter•    Samtidig redigering•    Beste praksiser, tips og triks•    Innstillinger og sikkerhet•    Administrasjon for Teams eiere•    Kontinuerlig læring i bruk av verktøyet•    Samhandlingsstrategier   [-]

Studieår: 2013-2014   Gjennomføring: Høst Antall studiepoeng: 5.0 Forutsetninger: Programmeringserfaring i et eller flere objektorienterte programmeringsspråk Innleveringer: 8 bestemte øvinger kreves godkjent for å få gå opp til eksamen. Personlig veileder: ja Vurderingsform: 4 timer skriftlig eksamen. Ansvarlig: Else Lervik Eksamensdato: 05.12.13         Læremål: KUNNSKAPERKandidaten:- kan definere begrepene pekere og referanser og forholdet mellom pekere og tabeller- kan redegjøre for hva konstruktører og destruktører er, og kan forklare når det er nødvendig å lage dem.- kan gjøre rede for «overloading» av operatorer- kan forklare begrepet «templates» og hvordan det brukes- kan forklare behovet for Standard Template Library og hva det inneholder- kan forklare bruken av RTTI og Exceptions FERDIGHETER:Kandidaten:- kan lage programmer i C++ som demonstrerer bruk av pekere, «overloading», templates, RTTI, exceptions og elementer fra Standard Template Library- kan lage programmer i C++ som bruker pekere og det frie lageret på en forsvarlig måte og med nødvendig opprydding GENERELL KOMPETANSE:Kandidaten:- er opptatt av at som profesjonell yrkesutøver skal man lage programmer som skal lette arbeidet for andre yrkesutøvere eller generelt være til nytte for folk og samfunn Innhold:Adresser og pekere, pekere og tabeller, det frie lageret, operator overloading, konstruktører og destruktører, templates, introduksjon til STL, RTTI og exceptions.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Dette faget går: Høst 2013    Fag C++ for programmerere 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

The students will also explore how to implement data security including authentication, authorization, data policies and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing and streaming data solutions. Agenda Module 1: Azure for the Data Engineer -Explain the evolving world of data-Survey the services in the Azure Data Platform-Identify the tasks that are performed by a Data Engineer-Describe the use cases for the cloud in a Case Study Module 2: Working with Data Storage. -Choose a data storage approach in Azure-Create an Azure Storage Account-Explain Azure Data Lake storage-Upload data into Azure Data Lake Module 3: Enabling Team Based Data Science with Azure Databricks. -Explain Azure Databricks and Machine Learning Platforms-Describe the Team Data Science Process-Provision Azure Databricks and workspaces-Perform data preparation tasks Module 4: Building Globally Distributed Databases with Cosmos DB. -Create an Azure Cosmos DB database built to scale-Insert and query data in your Azure Cosmos DB database-Provision a .NET Core app for Cosmos DB in Visual Studio Code-Distribute your data globally with Azure Cosmos DB Module 5: Working with Relational Data Stores in the Cloud. -SQL Database and SQL Data Warehouse-Provision an Azure SQL database to store data-Provision and load data into Azure SQL Data Warehouse Module 6: Performing Real-Time Analytics with Stream Analytics. Module 7: Orchestrating Data Movement with Azure Data Factory. -Explain how Azure Data Factory works-Create Linked Services and datasets-Create pipelines and activities-Azure Data Factory pipeline execution and triggers Module 8: Securing Azure Data Platforms. -Configuring Network Security-Configuring Authentication-Configuring Authorization-Auditing Security Module 9: Monitoring and Troubleshooting Data Storage and Processing. -Data Engineering troubleshooting approach-Azure Monitoring Capabilities-Troubleshoot common data issues-Troubleshoot common data processing issues Module 10: Integrating and Optimizing Data Platforms. -Integrating data platforms-Optimizing data stores-Optimize streaming data-Manage disaster recovery [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Ingen Innleveringer: 6 AV 10 øvinger må være godkjent for å kunne gå opp til eksamen. Vurderingsform: En individuell 4-timers nettbasert hjemmeeksamen. Ansvarlig: Svend Andreas Horgen Eksamensdato: 17.12.13 / 20.05.14         Læremål: KUNNSKAPER:Kandidaten:- kan forklare hva et program er- kan redegjøre for grunnleggende byggestener i programmering, så som variabler, kontrollstrukturer, matriser (arrays) og funksjoner- kan analysere en spesiell problemstilling og planlegge hvordan den kan løses generelt med programkode FERDIGHETER:Kandidaten:- kan bruke et .NET-basert utviklingsmiljø i kodeutvikling- kan lage funksjonelle brukergrensesnitt- kan identifisere feil i programkode- kan lage strukturert programkode som løser enkle problemstillinger- kan anvende innebygde funksjoner fra .NET-rammeverket i egen kode GENERELL KOMPETANSE:Kandidaten:- er bevisst på viktigheten av å eliminere feilsituasjoner Innhold:Introduksjon til grunnleggende programmeringsprinsipper som variabler, datatyper, kontrollstrukturer (løkker og beslutninger), matriser (arrays), egendefinerte funksjoner og innebyde funksjoner. Utforme brukergrensesnitt som er fine å se på og enkle å bruke. Feilhåndtering. Strukturere og planlegge koden på en god måte.Les mer om faget herDemo: Her er en introduksjonsvideo for faget Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Programmering i Visual Basic 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.  [-]

CCNA: Implementing and Administering Cisco Solutions [-]

Utvid din Excel-kunnskap til et ekspertnivå med "Excel: Ekspert", et dyptgående kurs ledet av Espen Faugstad hos Utdannet.no. Dette kurset er ideelt for de som allerede har en solid forståelse av Excel gjennom tidligere kurs og ønsker å utvikle avanserte ferdigheter for å håndtere komplekse dataanalyser og problemstillinger. Kurset vil dekke avanserte teknikker og funksjoner i Excel, inkludert ulike variasjoner av HVIS-funksjonen, FINN.RAD, FINN.KOLONNE, tekstbehandlingsfunksjoner som SØK og DELTEKST, samt dato- og tidsfunksjoner. Du vil også lære om avanserte oppslagsfunksjoner, matematiske formler og statistiske analyser ved hjelp av Excel. I tillegg til å lære om avanserte formler, vil kurset veilede deg gjennom bruk av matrisefunksjoner og feilsøking i Excel. Ved kursets slutt vil du ha en omfattende forståelse av Excel på et ekspertnivå, noe som gjør deg i stand til å utføre sofistikerte dataanalyser og rapporteringer.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Formelhåndtering Kapittel 3: HVIS Kapittel 4: GJØR.HVIS Kapittel 5: FINN Kapittel 6: Tekst Kapittel 7: Dato Kapittel 8: Oppslag Kapittel 9: Matematikk Kapittel 10: Statistikk Kapittel 11: Matrise Kapittel 12: Diverse Kapittel 13: Avslutning   Varighet: 4 timer   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. [-]

MCA: Microsoft 365 Modern Desktop Administrator Associate - Boot Camp [-]