Oversikt over relevante kurs innen IT-kurs i Møre og Romsdal

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 - 50 spørsmål skal besvares, og du består ved 50% korrekte svar (dvs 25 av 50 spørsmål). Deltakerne har 40 minutter til rådighet på eksamen.  Ingen hjelpemidler er tillatt.   Nødvendige forkunnskaper: Ingen [-]

Dette er et spesialkurs som fokuserer på analyse av store datasett ved hjelp av Pivottabell og Power Pivot, samt formelbasert analyse. Målet er å få frem styrker og svakheter ved de forskjellige metodene, og å se litt på hvilke forutsetninger som påvirker valg av løsning. For å ha utbytte av dette kurser forutsettes at man er vant bruker av Excel. Pivot og Power Pivot blir gjennomgått fra begynnelsen, så man trenger ikke være kjent med disse verktøyene fra før. Betingede formler kan være ganske krevende, så det er en fordel å være litt trygg på formelskriving. I en kurssituasjon blir selvsagt kurset tilpasset deltagernes nivå og forkunnskaper. I kurset gjennomgås bl.a.: Kontroll/gjennomgang av en del sentral funksjonalitet – bl.a. absolutte, relative og blandede referanser. Sammendrag av data fra flere ark i samme eller flere arbeidsbøker, bl.a. gjennomgående summering og tabulering v.hj.a. INDIREKTE-funksjonen. Betingende sammendrag v.hj.a. matriseformler og funksjoner Modifisere datasett med FINN.RAD, FINN.KOLONNE, matriseformler og andre teknikker Pivottabell, hvor vi bl.a. ser på: Sette sammen data fra forskjellige grunnlag før pivotering Vise dataserie på forskjellige måter (sum, gjennomsnitt, prosentfordelt, etc.) Hvordan foreta beregninger rett i pivottabellen, f.ex. inntekter – kostnader = resultat Pivottabell hvor datagrunnlaget er oppdelt i flere forskjellige Pivottabell rett mot en spørring i en database Power Pivot Forskjeller (og likheter) med «vanlig» Pivottabell Når forlater vi den vanlige pivottabellen til fordel for Power Pivot? Fordeler og ulemper med Pivot og Power Pivot. Lage Power Pivot-tabell med data fra flere forskjellige datasett. [-]

Bruker du Excel og ønsker å ta et steg videre? Forståelsen av referanser og større kjennskap til funksjoner gir deg mulighet til å utnytte mer av potensialet til Excel. Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Relative, absolutte og blandende cellereferanser Hvorfor og hvordan skal man låse cellereferanser ($)?   Generelt om bruk av funksjoner Hvordan finner du funksjoner du trenger Syntaks for funksjoner   Eksempler på noen enkle funksjoner: Idag(), Nå(), Antalla(), mm.   Gjennomgang av HVIS funksjonen og nøstet HVIS [-]

Kurs i PowerPoint med Jon-Gunnar Pettersen. Lær de viktigste funksjonene, arbeidsmåtene og metodene.   Få kontroll på innrykk og punktlister Effektive måter å lage tekstbokser på Organisasjonskart og flytdiagrammer Hente inn fra Excel på en proff måte Nyttige tips som gjør presentasjon for publikum tryggere Slik rydder du opp når ting har skjært seg Dette kurset passer for deg som bruker PowerPoint på en regelmessig basis. I dette kurset lærer du om smarte triks som vil ta opplevelsen din av programmet til neste nivå. Her finner du de viktigste funksjonene, de gode arbeidsmåtene og de ryddige metodene for deg som bruker PowerPoint ofte. Du vil lære mye du kanskje kan fra før av, men på en måte som gjør at du kan spare tid og krefter. Etter å ha tatt dette kurset vil du jobbe raskere i PowerPoint, og dine presentasjoner vil ha bedre tekniske kvalitet.   Introduksjon Nye sider og sideoppsett Punktlister Punktlister – del 2 Tegne figurer Tegne figurer – del 2 Tegne figurer – del 3 Smart art Kopiere fra Excel Animasjon Utskrift Visning [-]

Vi går gjennom praktiske øvelser og tips og triks for bruk av bl.a.  Passordløs ssh, scp og sftp ved bruk av nøkler Redigere flere dokumenter samtidig med vim Håndtere av brukere og grupper Navigere og håndtere filsystemer, mapper og filer, inkludert eierskap og tilganger Analyse av loggfiler og andre tabulære data med grep, cut, sort, unic, sed, awk, wc mm. Hente av eksterne filer med wget, og bruke eksterne api'er med curl Installere og konfigurere programvare med apt/yum/dnf og vim Sette opp og administrere brannmur med ufw Sjekke sikkerheten med nmap og ncrack Monitorering med top og htop Håndtere prosesser med ps, pstree, kill, killall, bg og fg Håndtere daemons med systemctl Logg-sjekk med journalctl og logwatch Backup, komprimering og arkivering med tar, rsync, zip, gzip og bzip2 Sette opp jobber i crontab og bruke anacron til å kjøre jobber til fastsatte tider Bruke drop-down terminal-emulatorer som guake og yakuake Bruke tmux/screen for å kjøre flere terminal sessions samtidig, bevare sessions etter utlogging osv. Kryptering og dekryptering med gnupg, inkludert lage nøkkelsett og publisere offentlig nøkkel på nøkkelservere.   [-]

Vue.js, Vuex & Router Course [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Studenten må kunne installere programvare, og programmere. Vanlige programmeringsspråk for opensource er C, C++, java, python, m.fl. Innleveringer:   Vurderingsform: Skriftlig eksamen, individuell, 3 timer, teller 60 %. Prosjektoppgave teller 40 %. Ansvarlig: Helge Hafting Eksamensdato: 02.12.13 / 05.05.14         Læremål: KUNNSKAPER:Kandidaten:- kan forklare bakgrunnen for open source, og forskjellen på de vanligste åpne lisensene.- kan beskrive bruk av flerspråklig programvare FERDIGHETER:Kandidaten:- kan bruke vanlige programmeringsverktøy GENERELL KOMPETANSE:Kandidaten:- kan delta i prosjekter som utvikler åpen programvare videre Innhold:Historien og filosofien bak åpen kildekode. Copyright, lisenser fri programvare. Praktisk bruk av verktøyer som make, diff, patch og versjonskontrollsystemer. Internasjonalisering, flerspråklig programvare. Prosjektarbeid med deltakelse i opensource-prosjekt.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Opensource-utvikling 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

Bli en mester i videoredigering med Adobe Premiere Pro gjennom dette dyptgående kurset ledet av Espen Faugstad, en erfaren kursholder hos Utdannet.no. Dette kurset krever ingen forkunnskaper og tar deg med fra grunnleggende til avanserte teknikker i Premiere Pro. Det er ideelt for alle som ønsker å lære profesjonell videoredigering, enten for personlig bruk eller for å utvikle karrieren som klipper. Kurset dekker alt fra opprettelse av prosjekter, organisering av filer, redigering av video og lyd, til bruk av effekter, overganger, og fargekorrigering. Du vil også lære å opprette titler, teksting, og bruke animasjon for å gi dine videoer et profesjonelt uttrykk. Ved kursets slutt vil du ha opparbeidet deg all den kunnskapen som trengs for å jobbe som en profesjonell videoredigerer.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Prosjekter Kapittel 3: Importere filer Kapittel 4: Redigere video Kapittel 5: Teknikker Kapittel 6: Redigere lyd Kapittel 7: Effekter og overganger Kapittel 8: Titler, grafikk og teksting Kapittel 9: Animere Kapittel 10: Fargekorrigere Kapittel 11: Eksportere Kapittel 12: Avslutning   Varighet: 6 timer og 5 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. [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Erfaring med programmering i et objektorientert språk. Innleveringer: Det blir gitt 10 øvinger. 8 må være godkjente for å gå opp til eksamen. Personlig veileder: ja Vurderingsform: individuell skriftlig eksamen, 3 timer. Ansvarlig: Tore Berg Hansen Eksamensdato: 17.12.13 / 20.05.14         Læremål: Etter å ha gjennomført emnet skal kandidaten ha følgende samlete læringsutbytte: KUNNSKAPER:Kandidaten kan forklare:- hva .NET-rammeverket er, dets hensikt og hva det inneholder- begrepene Common Language Runtime, Common Type System og managed code- begrepene Solution, Project og Assembly- hva en webtjeneste er- hvordan datakilder kan aksesseres ved hjelp av ADO.NET- hvordan webapplikasjoner kan lages ved hjelp av ASP.NET FERDIGHETER:Kandidaten:- kan skrive korte programmer i C#, Visual Basic .NET og C++/managed C++ som viser bruk av de sentrale konsepter som klassebiblioteket, ADO.NET og ASP.NET GENERELL KOMPETANSE:Kandidaten:- er klar over at .NET rammeverket har styrker og svakheter og at det finnes alternative teknologier Innhold:.NET-rammeverket og arkitekturen. Common Language Runtime (CLR). Common Type System (CTS). Klassebiblioteket. Common Language Specification (CLS). Assemblies. Programmeringsspråkene C#, C++, Visual Basic.NET. Managed code. Web services. ADO.NET. ASP.NET.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag Applikasjonsutvikling på .NET-plattformen 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]

Delta på webinar å lær hvordan man bygger en prosjektplan for å få god kontroll med gjennomføringen! Webinaret varer i 2 timer og består av to økter à 45 min. Etter hver økt er det 10 min spørsmålsrunde. Mellom øktene er det 10 min pause. Webinaret kan også spesialtilpasses og holdes bedriftsinternt kun for din bedrift.   Kursinnhold:   Tabeller og felt i Project Forskjellige typer felt. Forskjeller mellom felt i aktiviteter og ressurser Legge til og fjerne felt Opprette og tilpasse felt Endre eksisterende tabeller Opprette nye tabeller   Forskjellige visninger Sortering Filtrering - Innebygde filetere. Definere nye filtre Gruppering. Benytte grupper til bedre oversikt og kontroll   3 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Gratis support   [-]

COURSE OVERVIEW The course teaches IT Professionals how to implement and manage on-premises and hybrid solutions such as identity, management, compute, networking, and storage in a Windows Server hybrid environment. TARGET AUDIENCE This four-day course is intended for Windows Server Hybrid Administrators who have experience working with Windows Server and want to extend the capabilities of their on-premises environments by combining on-premises and hybrid technologies. Windows Server Hybrid Administrators implement and manage on-premises and hybrid solutions such as identity, management, compute, networking, and storage in a Windows Server hybrid environment. COURSE OBJECTIVES After you complete this course you will be able to: Use administrative techniques and tools in Windows Server. Identify tools used to implement hybrid solutions, including Windows Admin Center and PowerShell. Implement identity services in Windows Server. Implement identity in hybrid scenarios, including Azure AD DS on Azure IaaS and managed AD DS. Integrate Azure AD DS with Azure AD. Manage network infrastructure services. Deploy Azure VMs running Windows Server, and configure networking and storage. Administer and manage Windows Server IaaS Virtual Machine remotely. Manage and maintain Azure VMs running Windows Server. Configure file servers and storage. Implement File Services in hybrid scenarios, using Azure Files and Azure File Sync. COURSE CONTENT Module 1: Identity services in Windows Server This module introduces identity services and describes Active Directory Domain Services (AD DS) in a Windows Server environment. The module describes how to deploy domain controllers in AD DS, as well as Azure Active Directory (AD) and the benefits of integrating Azure AD with AD DS. The module also covers Group Policy basics and how to configure group policy objects (GPOs) in a domain environment. Lessons for module 1 Introduction to AD DS Manage AD DS domain controllers and FSMO roles Implement Group Policy Objects Manage advanced features of AD DS Lab : Implementing identity services and Group Policy Deploying a new domain controller on Server Core Configuring Group Policy After completing module 1, students will be able to: Describe AD DS in a Windows Server environment. Deploy domain controllers in AD DS. Describe Azure AD and benefits of integrating Azure AD with AD DS. Explain Group Policy basics and configure GPOs in a domain environment. Module 2: Implementing identity in hybrid scenarios This module discusses how to configure an Azure environment so that Windows IaaS workloads requiring Active Directory are supported. The module also covers integration of on-premises Active Directory Domain Services (AD DS) environment into Azure. Finally, the module explains how to extend an existing Active Directory environment into Azure by placing IaaS VMs configured as domain controllers onto a specially configured Azure virtual network (VNet) subnet. Lessons for module 2 Implement hybrid identity with Windows Server Deploy and manage Azure IaaS Active Directory domain controllers in Azure Lab : Implementing integration between AD DS and Azure AD Preparing Azure AD for AD DS integration Preparing on-premises AD DS for Azure AD integration Downloading, installing, and configuring Azure AD Connect Verifying integration between AD DS and Azure AD Implementing Azure AD integration features in AD DS After completing module 2, students will be able to: Integrate on-premises Active Directory Domain Services (AD DS) environment into Azure. Install and configure directory synchronization using Azure AD Connect. Implement and configure Azure AD DS. Implement Seamless Single Sign-on (SSO). Implement and configure Azure AD DS. Install a new AD DS forest on an Azure VNet. Module 3: Windows Server administration This module describes how to implement the principle of least privilege through Privileged Access Workstation (PAW) and Just Enough Administration (JEA). The module also highlights several common Windows Server administration tools, such as Windows Admin Center, Server Manager, and PowerShell. This module also describes the post-installation confguration process and tools available to use for this process, such as sconfig and Desired State Configuration (DSC). Lessons for module 3 Perform Windows Server secure administration Describe Windows Server administration tools Perform post-installation configuration of Windows Server Just Enough Administration in Windows Server Lab : Managing Windows Server Implementing and using remote server administration After completing module 3, students will be able to: Explain least privilege administrative models. Decide when to use privileged access workstations. Select the most appropriate Windows Server administration tool for a given situation. Apply different methods to perform post-installation configuration of Windows Server. Constrain privileged administrative operations by using Just Enough Administration (JEA). Module 4: Facilitating hybrid management This module covers tools that facilitate managing Windows IaaS VMs remotely. The module also covers how to use Azure Arc with on-premises server instances, how to deploy Azure policies with Azure Arc, and how to use role-based access control (RBAC) to restrict access to Log Analytics data. Lessons for module 4 Administer and manage Windows Server IaaS virtual machines remotely Manage hybrid workloads with Azure Arc Lab : Using Windows Admin Center in hybrid scenarios Provisioning Azure VMs running Windows Server Implementing hybrid connectivity by using the Azure Network Adapter Deploying Windows Admin Center gateway in Azure Verifying functionality of the Windows Admin Center gateway in Azure After completing module 4, students will be able to: Select appropriate tools and techniques to manage Windows IaaS VMs remotely. Explain how to onboard on-premises Windows Server instances in Azure Arc. Connect hybrid machines to Azure from the Azure portal. Use Azure Arc to manage devices. Restrict access using RBAC. Module 5: Hyper-V virtualization in Windows Server This modules describes how to implement and configure Hyper-V VMs and containers. The module covers key features of Hyper-V in Windows Server, describes VM settings, and how to configure VMs in Hyper-V. The module also covers security technologies used with virtualization, such as shielded VMs, Host Guardian Service, admin-trusted and TPM-trusted attestation, and Key Protection Service (KPS). Finally, this module covers how to run containers and container workloads, and how to orchestrate container workloads on Windows Server using Kubernetes. Lessons for module 5 Configure and manage Hyper-V Configure and manage Hyper-V virtual machines Secure Hyper-V workloads Run containers on Windows Server Orchestrate containers on Windows Server using Kubernetes Lab : Implementing and configuring virtualization in Windows Server Creating and configuring VMs Installing and configuring containers After completing module 5, students will be able to: Install and configure Hyper-V on Windows Server. Configure and manage Hyper-V virtual machines. Use Host Guardian Service to protect virtual machines. Create and deploy shielded virtual machines. Configure and manage container workloads. Orchestrate container workloads using a Kubernetes cluster. Module 6: Deploying and configuring Azure VMs This module describes Azure compute and storage in relation to Azure VMs, and how to deploy Azure VMs by using the Azure portal, Azure CLI, or templates. The module also explains how to create new VMs from generalized images and use Azure Image Builder templates to create and manage images in Azure. Finally, this module describes how to deploy Desired State Configuration (DSC) extensions, implement those extensions to remediate noncompliant servers, and use custom script extensions. Lessons for module 6 Plan and deploy Windows Server IaaS virtual machines Customize Windows Server IaaS virtual machine images Automate the configuration of Windows Server IaaS virtual machines Lab : Deploying and configuring Windows Server on Azure VMs Authoring Azure Resource Manager (ARM) templates for Azure VM deployment Modifying ARM templates to include VM extension-based configuration Deploying Azure VMs running Windows Server by using ARM templates Configuring administrative access to Azure VMs running Windows Server Configuring Windows Server security in Azure VMs After completing module 6, students will be able to: Create a VM from the Azure portal and from Azure Cloud Shell. Deploy Azure VMs by using templates. Automate the configuration of Windows Server IaaS VMs. Detect and remediate noncompliant servers. Create new VMs from generalized images. Use Azure Image Builder templates to create and manage images in Azure. Module 7: Network infrastructure services in Windows Server This module describes how to implement core network infrastructure services in Windows Server, such as DHCP and DNS. This module also covers how to implement IP address managment and how to use Remote Access Services. Lessons for module 7 Deploy and manage DHCP Implement Windows Server DNS Implement IP address management Implement remote access Lab : Implementing and configuring network infrastructure services in Windows Server Deploying and configuring DHCP Deploying and configuring DNS After completing module 7, students will be able to: Implement automatic IP configuration with DHCP in Windows Server. Deploy and configure name resolution with Windows Server DNS. Implement IPAM to manage an organization’s DHCP and DNS servers, and IP address space. Select, use, and manage remote access components. Implement Web Application Proxy (WAP) as a reverse proxy for internal web applications. Module 8: Implementing hybrid networking infrastructure This module describes how to connect an on-premises environment to Azure and how to configure DNS for Windows Server IaaS virtual machines. The module covers how to choose the appropriate DNS solution for your organization’s needs, and run a DNS server in a Windows Server Azure IaaS VM. Finally, this module covers how to manage manage Microsoft Azure virtual networks (VNets) and IP address configuration for Windows Server infrastructure as a service (IaaS) virtual machines. Lessons for module 8 Implement hybrid network infrastructure Implement DNS for Windows Server IaaS VMs Implement Windows Server IaaS VM IP addressing and routing Lab : Implementing Windows Server IaaS VM networking Implementing virtual network routing in Azure Implementing DNS name resolution in Azure After completing module 8, students will be able to: Implement an Azure virtual private network (VPN). Configure DNS for Windows Server IaaS VMs. Run a DNS server in a Windows Server Azure IaaS VM. Create a route-based VPN gateway using the Azure portal. Implement Azure ExpressRoute. Implement an Azure wide area network (WAN). Manage Microsoft Azure virtual networks (VNets). Manage IP address configuration for Windows Server IaaS virtual machines (VMs). Module 9: File servers and storage management in Windows Server This module covers the core functionality and use cases of file server and storage management technologies in Windows Server. The module discusses how to configure and manage the Windows File Server role, and how to use Storage Spaces and Storage Spaces Direct. This module also covers replication of volumes between servers or clusters using Storage Replica. Lessons for module 9 Manage Windows Server file servers Implement Storage Spaces and Storage Spaces Direct Implement Windows Server Data Deduplication Implement Windows Server iSCSI Implement Windows Server Storage Replica Lab : Implementing storage solutions in Windows Server Implementing Data Deduplication Configuring iSCSI storage Configuring redundant Storage Spaces Implementing Storage Spaces Direct After completing module 9, students will be able to: Configure and manage the Windows Server File Server role. Protect data from drive failures using Storage Spaces. Increase scalability and performance of storage management using Storage Spaces Direct. Optimize disk utilization using Data DeDuplication. Configure high availability for iSCSI. Enable replication of volumes between clusters using Storage Replica. Use Storage Replica to provide resiliency for data hosted on Windows Servers volumes. Module 10: Implementing a hybrid file server infrastructure This module introduces Azure file services and how to configure connectivity to Azure Files. The module also covers how to deploy and implement Azure File Sync to cache Azure file shares on an on-premises Windows Server file server. This module also describes how to manage cloud tiering and how to migrate from DFSR to Azure File Sync. Lessons for module 10 Overview of Azure file services Implementing Azure File Sync Lab : Implementing Azure File Sync Implementing DFS Replication in your on-premises environment Creating and configuring a sync group Replacing DFS Replication with File Sync–based replication Verifying replication and enabling cloud tiering Troubleshooting replication issues After completing module 10, students will be able to: Configure Azure file services. Configure connectivity to Azure file services. Implement Azure File Sync. Deploy Azure File Sync Manage cloud tiering. Migrate from DFSR to Azure File Sync.   [-]

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     [-]

Course Description This course then focuses on enterprise application to cloud concerns including planning and executing a migration, building the business case, managing application dependencies, selecting a proof of concept, and serverless/managed services. A series of instructor-led demonstrations and hands-on activities provide students with practical, hands-on experience. Learning Objectives Learn what technologies enable cloud computing Understand the definition and characteristics of cloud computing Compare service models: IaaS, PaaS, SaaS, Serverless Develop the business case for a cloud migration Plan a successful cloud migration Decipher the risks of both development and security with cloud computing Analyze the costs of using cloud computing and an approach to calculating them Objection handling when dealing with projects situations around risk All courses will be delivered in partnership with ROI Training, Google Cloud Premier Partner, using a Google Authorized Trainer. Course Outline Unit 1: Enabling Technologies -Networking-Virtualization-Overview of Virtualization-Hypervisors and Containers-Security and Virtualization-Multi-tenancy Unit 2: Cloud Computing Concepts -Cloud Definition-Characteristics of Clouds-Cloud Service and Deployment Models-Public Cloud Products and Services Unit 3: Cloud Service Models -Comparing Services Offered by Google Cloud Platform (GCP), Amazon Web Services (AWS), and Azure-Compute Services-Storage Services-Kubernetes Services-Serverless and Managed Services-Big Data and Machine Learning Unit 4: Building a Business Case for the Cloud -Economic and Financial-Understand the Cloud Cost Model-Calculating the Cost of a Cloud Solution-Transform Capital Expenditures to Operating Expenditures-Agility-Lower Risk of Adopting and Evaluating New Technology-Reduce Time to Market-Quickly React as Markets and Requirements Change-Risk Mitigation-High Quality Infrastructure-Reduce Downtime-Cloud SLAs-Leveraging Hybrid and Multi-Cloud Solutions-Staff Utilization-Eliminate Mundane Operational Tasks-Harness Monitoring and Logging-Onboarding Applications and Users Unit 5: Migrating to the Public Cloud -Phases in a Successful Migration-Assessment-Proof of Concept-Data Migration-Application Migration-Employ Cloud Native Services-Cloud Native Development-Selecting Workloads-Backup / Disaster Recovery-Packaged Enterprise Software-Custom Applications-Open-Source Applications Unit 6: Security and the Cloud -Cloud-based Security Issues-Shared Responsibility Model-Security Auditing in the Cloud-Compliance with Regulatory Constraints [-]

Kursinstruktør Geir Johan Gylseth Geir Johan Gylseth er utdannet ved Universitetet i Oslo med hovedvekt på Informatikk og har over 30 års erfaring som instruktør. Geir sin styrke ligger innenfor MS Office. Han har lang erfaring med skreddersøm av kurs, kursmanualer og oppgaver. Geir er en entusiastisk og dyktig instruktør som får meget gode evalueringer. Kursinnhold Med Microsoft Project 365 får du et godt verktøy for planlegging og oppfølging av prosjekter. Dette kurset lærer deg å håndtere ressurser, aktiviteter og budsjett. Du kan opprette, oppdatere og gjøre enkel oppfølging i et prosjekt. Vi går igjennom hvordan du, både grafisk og i tekst, ser effekten av forandringer i prosjekt og hvordan du kan skrive ut dine prosjektplaner. Målet med kurset er å gi deg en prossessorientert tilnærming i Microsoft Project 365 slik at du er i stand til å arbeide målrettet og effektivt med programvaren etter kurset.   Sett opp Project for bruk i din bedrift – tips og triks. Lag egne kalendere for enkeltpersoner og/eller grupper. Hold oversikt over tids- og ressursbruk. Vit hvem som jobber hvor – på tvers av prosjekter. Kontroller kostnadene i prosjektet. Ta hensyn til lønnsøkninger og variable kostnader. Vis og kontroller hvordan prosjektet går i forhold til opprinnelig plan (Baseline). Presenter fremdrift på papir og på nett. Utnytt de nye rapportmulighetene. Ta hensyn til at arbeid noen ganger foregår på kvelden og i helger. Se hvordan du kan få vakre utskrifter med egendefinerte komponenter ved hjelp av Project sine rapportegenskaper. Lag dine egne tabeller og visninger, skreddersydd til ditt bruk. Gjør rapportering og oppfølging enkel slik at du kan konsentrere deg om å lede prosjektet. Bruk tidslinje for enkelkommunikasjon av fremdrift. Kommunikasjon med andre programmer.   I tillegg får du en rekke tips og triks du kan bruke i din arbeidsdag.  Alt du lærer får du repetert gjennom aktivoppgaveløsning slik at du husker det du har lært når du kommer tilbake på jobb. Kursdokumentasjon, lunsj og pausemat er selvsagt inkludert! NB: Ta med egen PC     Av innhold kan vi nevne: - Innstilling av programvaren – en reprise fra grunnkurset - Hva vil jeg ha ut av mine planer og hvordan får jeg det - Effektiv og målrettet planlegging - Bruk av ressurspool – Ressursstyring på tvers av prosjekter - Integrasjon og kobling mot Excel i rapportering og kostnadsoppfølging - En grundig gjennomgang av mulighetene i Project - Bygg dine egne rapporter og visninger - Bruk av flere kalendere - Detaljert budsjettering og kostnadsoppfølging - Få hjelp og råd med dine konkrete utfordringer i Project    Meld deg på Project-kurs allerede i dag og sikre deg plass!   "Kurset var svært konkret, nyttig med tanke på mine daglige oppgaver."Henning Colbjørnsen- Rælingen kommune   [-]

Acrobat Pro DC er et kraftig verktøy som gir deg muligheten til å opprette, redigere og signere PDF-dokumenter. PDF, som står for Portable Document Format, er en standard for å presentere og dele dokumenter uavhengig av programvare, maskinvare og operativsystem. Med Acrobat Pro DC kan du arbeide med tekst, bilder, videoer, koblinger, knapper og skjemaer i PDF-format. PDF-formatet ble introdusert i 1991 av Dr. John Warnock, medgrunnleggeren av Adobe, med målet om å gjøre det enkelt for alle å samle, dele og skrive ut dokumenter fra hvilket som helst program. I dag foretrekkes PDF-formatet av bedrifter over hele verden. I dette kurset, ledet av Espen Faugstad hos Utdannet.no, vil du lære å utnytte Adobe Acrobat Pro DC til fulle. Kurset vil ta deg gjennom programmets organisasjon, verktøy og paneler. Du vil lære å opprette, søke, redigere og organisere PDF-dokumenter. I tillegg vil du bli kjent med elektronisk signering, passordbeskyttelse, skjemaoppretting og kryptering av PDF-dokumenter.   Innhold: Kapittel 1: Organisering og Verktøy Kapittel 2: Opprette PDF Kapittel 3: Søke og Erstatte Kapittel 4: Redigere PDF Kapittel 5: Organisere Sider Kapittel 6: Kommentarer Kapittel 7: Skjema og Signatur Kapittel 8: Beskyttelse og Kryptering Kapittel 9: Lagre PDF Kapittel 10: Avslutning   Varighet: 2 timer og 23 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Vår tjeneste fungerer som strømmetjenester for musikk eller TV-serier, der kundene våre betaler en fast månedspris for tilgang til alle kursene vi har produsert. Plattformen har hatt betydelig vekst de siste årene, med over 30 000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling morsomt, spennende og tilgjengelig for alle, og vi har støtte fra Innovasjon Norge og Forskningsrådet. [-]