Oversikt over relevante kurs innen IT-kurs i Rømskog

Understand the purpose and key concepts of Information Security Management, elucidating its significance in safeguarding the confidentiality, integrity, and availability of organisational information assets. This eLearning is: Interactive Self-paced   Device-friendly   2-3 hours content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

  COURSE OVERVIEW This course will provide foundational level knowledge of cloud services and how those services are provided with Microsoft Azure. The course can be taken as an optional first step in learning about cloud services and Microsoft Azure, before taking further Microsoft Azure or Microsoft cloud services courses. The course will cover general cloud computing concepts as well as general cloud computing models and services such as Public, Private and Hybrid cloud and Infrastructure-as-a-Service (IaaS), Platform-as-a-Service(PaaS) and Software-as-a-Service (SaaS). It will also cover some core Azure services and solutions, as well as key Azure pillar services concerning security, privacy, compliance and trust. It will finally cover pricing and support services available with Azure.   COURSE CONTENT  Module 1: Cloud Concepts -Learning Objectives-Why Cloud Services?-Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS)-Public, Private, and Hybrid cloud models Module 2: Core Azure Services -Core Azure architectural components-Core Azure Services and Products-Azure Solutions-Azure management tools Module 3: Security, Privacy, Compliance and Trust -Securing network connectivity in Azure-Core Azure Identity services-Security tools and features-Azure governance methodologies-Monitoring and Reporting in Azure-Privacy, Compliance and Data Protection standards in Azure Module 4: Azure Pricing and Support -Azure subscriptions-Planning and managing costs-Support options available with Azure-Service lifecycle in Azure     This course helps to prepare for exam AZ-900. [-]

Ønsker du å mestre Google Analytics 4 for å få dybdeinnsikt i kundeadferden på nettstedet eller appen din? Da er kurset "Google Analytics 4: Komplett", ledet av Espen Faugstad, perfekt for deg. Dette kurset er designet for å gi deg en helhetlig forståelse av Google Analytics 4, slik at du kan jobbe profesjonelt med dette kraftige analyseverktøyet. Kurset starter med grunnleggende om hvordan Google Analytics 4 fungerer og veileder deg gjennom installasjonen på din nettside. Du vil lære å konfigurere Google Analytics for å maksimere dets potensial, administrere brukere, spore nettstedsøk, og mye mer. I tillegg gir kurset deg en detaljert gjennomgang av standardrapporter og utforskninger som er tilgjengelige i Google Analytics 4. Mot slutten av kurset vil du dykke inn i mer avanserte temaer som opprettelse og sporing av egendefinerte hendelser, konverteringssporing, og hvordan du kan utnytte innsikter fra brukerdata for å forbedre dine digitale strategier. Dette kurset er din vei til å bli en ekspert i Google Analytics 4.   Innhold: Kapittel 1: Introduksjon Kapittel 2: Installer Kapittel 3: Konfigurer Kapittel 4: Rapporter Kapittel 5: Utforsk Kapittel 6: Hendelser Kapittel 7: Avansert Kapittel 8: Avslutning   Varighet: 4 timer og 48 minutter   Om Utdannet.no: Utdannet.no tilbyr noen av landets beste digitale nettkurs. Tjenesten fungerer på samme måte som strømmetjenester for musikk eller TV-serier. Våre kunder betaler en fast månedspris og får tilgang til alle kursene som er produsert så langt. Plattformen har hatt en god vekst de siste årene og kan skilte med 30.000 registrerte brukere og 1,5 millioner videoavspillinger. Vårt mål er å gjøre kompetanseutvikling moro, spennende og tilgjengelig for alle – og med oss har vi Innovasjon Norge og Forskningsrådet. [-]

. This course includes identity content for Azure AD, enterprise application registration, conditional access, identity governance, and other identity tools.   TARGET AUDIENCE This course is for the Identity and Access Administrators who are planning to take the associated certification exam, or who are performing identity and access administration tasks in their day-to-day job. This course would also be helpful to an administrator or engineer that wants to specialize in providing identity solutions and access management systems for Azure-based solutions; playing an integral role in protecting an organization. COURSE OBJECTIVES Implement an identity management solution Implement an authentication and access management solutions Implement access management for apps Plan and implement an identity governancy strategy COURSE CONTENT Module 1: Implement an identity management solution Learn to create and manage your initial Azure Active Directory (Azure AD) implementation and configure the users, groups, and external identities you will use to run your solution. Lessons M1 Implement Initial configuration of Azure AD Create, configure, and manage identities Implement and manage external identities Implement and manage hybrid identity Lab 1a: Manage user roles Lab 1b: Setting tenant-wide properties Lab 1c: Assign licenses to users Lab 1d: Restore or remove deleted users Lab 1e: Add groups in Azure AD Lab 1f: Change group license assignments Lab 1g: Change user license assignments Lab 1h: Configure external collaboration Lab 1i: Add guest users to the directory Lab 1j: Explore dynamic groups After completing module 1, students will be able to: Deploy an initail Azure AD with custom settings Manage both internal and external identities Implement a hybrid identity solution Module 2: Implement an authentication and access management solution Implement and administer your access management using Azure AD. Use MFA, conditional access, and identity protection to manager your identity solution. Lessons M2 Secure Azure AD user with MFA Manage user authentication Plan, implement, and administer conditional access Manage Azure AD identity protection Lab 2a: Enable Azure AD MFA Lab 2b: Configure and deploy self-service password reset (SSPR) Lab 2c: Work with security defaults Lab 2d: Implement conditional access policies, roles, and assignments Lab 2e: Configure authentication session controls Lab 2f: Manage Azure AD smart lockout values Lab 2g: Enable sign-in risk policy Lab 2h: Configure Azure AD MFA authentication registration policy After completing module 2, students will be able to: Configure and manage user authentication including MFA Control access to resources using conditional access Use Azure AD Identity Protection to protect your organization Module 3: Implement access management for Apps Explore how applications can and should be added to your identity and access solution with application registration in Azure AD. Lessons M3 Plan and design the integration of enterprise for SSO Implement and monitor the integration of enterprise apps for SSO Implement app registration Lab 3a: Implement access management for apps Lab 3b: Create a custom role to management app registration Lab 3c: Register an application Lab 3d: Grant tenant-wide admin consent to an application Lab 3e: Add app roles to applications and recieve tokens After completing module 3, students will be able to: Register a new application to your Azure AD Plan and implement SSO for enterprise application Monitor and maintain enterprise applications Module 4: Plan and implement an identity governancy strategy Design and implement identity governance for your identity solution using entitlement, access reviews, privileged access, and monitoring your Azure Active Directory (Azure AD). Lessons M4 Plan and implement entitlement management Plan, implement, and manage access reviews Plan and implement privileged access Monitor and maintain Azure AD Lab 4a: Creat and manage a resource catalog with Azure AD entitlement Lab 4b: Add terms of use acceptance report Lab 4c: Manage the lifecycle of external users with Azure AD identity governance Lab 4d: Create access reviews for groups and apps Lab 4e: Configure PIM for Azure AD roles Lab 4f: Assign Azure AD role in PIM Lab 4g: Assign Azure resource roles in PIM Lab 4h: Connect data from Azure AD to Azure Sentinel After completing module 4, students will be able to: Mange and maintain Azure AD from creation to solution Use access reviews to maintain your Azure AD Grant access to users with entitlement management [-]

Kursinnhold Enkle formler Cellereferanser Gi navn til celler og områder Feilkontroll og formelrevisjon Hente data fra andre ark og arbeidsbøker Egendefinerte tallformater Betinget formatering Utklippstavle og avansert innliming   Det er fordelaktig å ha to skjermer - en til å følge kurset og en til å gjøre det kursholder demonstrerer.   Kurset gjennomføres i sanntid med nettundervisning via Teams. Det blir mulighet for å stille spørsmål, ha diskusjoner, demonstrasjoner og øvelser. Du vil motta en invitasjon til Teams fra kursholder. [-]

 In Microsoft 365 security management, you will examine all the common types of threat vectors and data breaches facing organizations today, and you will learn how Microsoft 365’s security solutions address these security threats. Global Knowledge will introduce you to the Microsoft Secure Score, as well as to Azure Active Directory Identity Protection. You will then learn how to manage the Microsoft 365 security services, including Exchange Online Protection, Advanced Threat Protection, Safe Attachments, and Safe Links. Finally, you will be introduced to the various reports that monitor your security health. You will then transition from security services to threat intelligence; specifically, using the Security Dashboard and Advanced Threat Analytics to stay ahead of potential security breaches. TARGET AUDIENCE This course is designed for persons who are aspiring to the Microsoft 365 Enterprise Admin role and have completed one of the Microsoft 365 work load administrator certification paths. COURSE OBJECTIVES By actively participating in this course, you will learn about the following: Microsoft 365 Security Metrics Microsoft 365 Security Services Microsoft 365 Threat Intelligence Data Governance in Microsoft 365 Archiving and Retention in Office 365 Data Governance in Microsoft 365 Intelligence Search and Investigations Device Management Windows 10 Deployment Strategies Mobile Device Management COURSE CONTENT Module 1: Introduction to Microsoft 365 Security Metrics Threat Vectors and Data Breaches Security Solutions in Microsoft 365 Introduction to the Secure Score Introduction to Azure Active Directory Identity Protection Module 2: Managing Your Microsoft 365 Security Services Introduction to Exchange Online Protection Introduction to Advanced Threat Protection Managing Safe Attachments Managing Safe Links Monitoring and Reports Module 3: Lab 1 - Manage Microsoft 365 Security Services Exercise 1 - Set up a Microsoft 365 Trial Tenant Exercise 2 - Implement an ATP Safe Links policy and Safe Attachment policy Module 4: Microsoft 365 Threat Intelligence Overview of Microsoft 365 Threat Intelligence Using the Security Dashboard Configuring Advanced Threat Analytics Implementing Your Cloud Application Security Module 5: Lab 2 - Implement Alert Notifications Using the Security Dashboard Exercise 1 - Prepare for implementing Alert Policies Exercise 2 - Implement Security Alert Notifications Exercise 3 - Implement Group Alerts Exercise 4 - Implement eDiscovery Alerts Module 6: Introduction to Data Governance in Microsoft 365 Introduction to Archiving in Microsoft 365 Introduction to Retention in Microsoft 365 Introduction to Information Rights Management Introduction to Secure Multipurpose Internet Mail Extension Introduction to Office 365 Message Encryption Introduction to Data Loss Prevention Module 7: Archiving and Retention in Office 365 In-Place Records Management in SharePoint Archiving and Retention in Exchange Retention Policies in the SCC Module 8: Lab 3 - Implement Archiving and Retention Exercise 1 - Initialize Compliance in Your Organization Exercise 2 - Configure Retention Tags and Policies Exercise 3 - Implement Retention Policies Module 9: Implementing Data Governance in Microsoft 365 Intelligence Planning Your Security and Complaince Needs Building Ethical Walls in Exchange Online Creating a Simple DLP Policy from a Built-in Template Creating a Custom DLP Policy Creating a DLP Policy to Protect Documents Working with Policy Tips Module 10: Lab 4 - Implement DLP Policies Exercise 1 - Manage DLP Policies Exercise 2 - Test MRM and DLP Policies Module 11: Managing Data Governance in Microsoft 365 Managing Retention in Email Troubleshooting Data Governance Implementing Azure Information Protection Implementing Advanced Features of AIP Implementing Windows Information Protection Module 12: Lab 5 - Implement AIP and WIP Exercise 1 - Implement Azure Information Protection Exercise 2 - Implement Windows Information Protection Module 13: Managing Search and Investigations Searching for Content in the Security and Compliance Center Auditing Log Investigations Managing Advanced eDiscovery Module 14: Lab 6 - Manage Search and Investigations Exercise 1 - Investigate Your Microsoft 365 Data Exercise 2 - Configure and Deploy a Data Subject Request Module 15: Planning for Device Management Introduction to Co-management Preparing Your Windows 10 Devices for Co-management Transitioning from Configuration Manager to Intune Introduction to Microsoft Store for Business Planning for Mobile Application Management Module 16: Lab 7 - Implement the Microsoft Store for Business Exercise 1 - Configure the Microsoft Store for Business Exercise 2 - Manage the Microsoft Store for Business Module 17: Planning Your Windows 10 Deployment Strategy Windows 10 Deployment Scenarios Implementing Windows Autopilot Planning Your Windows 10 Subscription Activation Strategy Resolving Windows 10 Upgrade Errors Introduction to Windows Analytics Module 18: Implementing Mobile Device Management Planning Mobile Device Management Deploying Mobile Device Management Enrolling Devices to MDM Managing Device Compliance Module 19: Lab 8 - Manage Devices with Intune Exercise 1 - Enable Device Management Exercise 2 - Configure Azure AD for Intune Exercise 3 - Create Intune Policies Exercise 4 - Enroll a Windows 10 Device Exercise 5 - Manage and Monitor a Device in Intune TEST CERTIFICATION This course helps you to prepare for exam MS101. [-]

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

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     [-]

AI-3016: Develop custom copilots with Azure AI Studio [-]

Filer i SharePoint lagres i bibliotek. Her tar vi en grundig gjennomgang av bibliotek og tilpasningsmuligheter for disse, som versjonering, maler og Office-integrasjon. 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:   Om bibliotek Møt biblioteksmalene i SharePoint Opplasting, nedlasting Office-programmene og bibliotek Områdepapirkurv   Tilpasse bibliotek Endre Office-mal for et bibliotek Tilpass kolonner og metadata   Tips til bibliotek Bruke kolonner i Word Bibliotek i Windows Utforsker   Utvidet om bibliotek Gjennomgang av versjonering Bli kjent med godkjenning Arkivering og Send til   Veien videre Introduksjon til innholdstyper Introduksjon til dokumentsenter og innholds-sortering 3 gode grunner til å delta 1. Møt SharePoint sine bibliotek-apper og lær måter å åpne og lagre i bibliotek og håndtere innholdet 2. Forstå mer om versjonering, godkjenning og arkivering 3. Bli kjent med dokumentsenter og innholds-sortering   [-]

COURSE OVERVIEW   The delegate will learn how to exploit advanced features of the Python language to build complex and efficient applications. Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered. TARGET AUDIENCE The Python Programming 2 course is designed for existing Python developers who have a good grounding in the basics and want to exploit some of the advanced features of the language. For the delegate for whom Python is their first programming language,we recommend taking the Python Programming 1 course first,then taking some time to practice the skills gained,before returning to take the Python Programming 2 course.   COURSE OBJECTIVES This course aims to provide the delegate with the knowledge to be able to interpret,write,and troubleshoot complex Python applications exploiting inheritance and polymorphism,mixins,composition and aggregation,iterators,generators,decorators,comprehension,concurrency,functional programming,and RESTful web services. COURSE CONTENT DAY 1 COURSE INTRODUCTION Administration and Course Materials Course Structure and Agenda Delegate and Trainer Introductions SESSION 1: ADVANCED OBJECT ORIENTATION The self Keyword Constructors and Destructors Encapsulation Inheritance Introspection with __dict__,__name__,__module__,__bases__ The hasattr(obj,attr),dir(obj),help(obj) functions Polymorphism Abstract Classes Multiple Inheritance and Mixins Composition and Aggregation Static Members SESSION 2: ITERATORS & GENERATORS Iterables Iterators Custom Iterators Generators Yield vs. Return SESSION 3: COMPREHENSIONS List Comprehension Set Comprehension The zip Function Dictionary Comprehension DAY 2 SESSION 4: DECORATORS Decorators Decorator Functions Decorator Annotations Decorator Use Cases Labs SESSION 5: FUNCTIONAL PROGRAMMING Functional Programming Lambdas Immutability Mapping Filtering Reducing SESSION 6: MULTITHREADING Threads Multithreading Thread Construction Thread Execution Thread Sleep Joins Data Sharing Synchronisation Multithreading vs. Multiprocessing DAY 3 SESSION 7: WEB SERVICES RESTful Web Services JSON Data CRUD and HTTP RESTful Clients RESTful APIs SESSION 8: UNIT TESTING Unit Testing Terminology Test Classes Test Fixtures Test Cases Assertions Test Runners   FOLLOW ON COURSES Data Analysis Python [-]

COURSE OVERVIEW The course includes hands-on experience configuring, managing, and monitoring a firewall in a lab environment TARGET AUDIENCE This course is aimed at Security Engineers, Security Administrators, Security Operations Specialists, Security Analysts, and Support Staff. COURSE OBJECTIVES After you complete this course, you will be able to: Configure and manage the essential features of Palo Alto Networks next-generation firewalls Configure and manage Security and NAT policies to enable approved traffic to and from zones Configure and manage Threat Prevention strategies to block traffic from known and unknown IP addresses, domains, and URLs Monitor network traffic using the interactive web interface and firewall reports COURSE CONTENT 1 - Palo Alto Networks Portfolio and Architecture 2 - Configuring Initial Firewall Settings 3 - Managing Firewall Configurations 4 - Managing Firewall Administrator Accounts 5 - Connecting the Firewall to Production Networks with Security Zones 6 - Creating and Managing Security Policy Rules 7 - Creating and Managing NAT Policy Rules 8 - Controlling Application Usage with App-ID 9 - Blocking Known Threats Using Security Profiles 10 - Blocking Inappropriate Web Traffic with URL Filtering 11 - Blocking Unknown Threats with Wildfire 12 - Controlling Access to Network Resources with User-ID 13 - Using Decryption to Block Threats in Encrypted Traffic 14 - Locating Valuable Information Using Logs and Reports 15 - What's Next in Your Training and Certification Journey Supplemental Materials Securing Endpoints with GlobalProtect Providing Firewall Redundancy with High Availability Connecting Remotes Sites using VPNs Blocking Common Attacks Using Zone Protection   FURTHER INFORMATION Level: Introductory Duration: 5 days Format: Lecture and hands-on labs Platform support: Palo Alto Networks next-generation firewalls running PAN-OS® operating system version 11.0     [-]

AZ-400: Designing and Implementing Microsoft DevOps solutions [-]

Trenger du å bygge opp store og avanserte regneark? Ønsker du å lage rapporter og beregninger på store tallgrunnlag? Vil du finne ut hvordan du kan effektivisere arbeidet ditt i Excel? Ønsker du å lære de første stegene mot automatiserte rapporter? Kurset kan også spesialtilpasses og holdes bedriftsinternt i deres eller våre lokaler.   Kursinnhold:   Dag 1    Generelt om regneark Om regneark og infrastruktur Bruke tastatur og hurtigtaster effektiv Absolutte referanser og definerte navn   Funksjoner Mer om funksjoner, hvis, antall.hvis, summer.hvis.sett Lær om "må ha funksjonen" Finn.rad [Vlookup] Andre funksjoner for spesielle oppgaver   Avansert formatering Spesiell formatering – dato, tekst og egendefinert Betinget formatering og cellestiler   Dag 2    Lister og tabeller Viktige regler og råd Bruk av autofilter og sortering Tabellfunksjonalitet Validering ved inntasting Beregninger av store datamengder via gode funksjoner   Pivottabell Hva er pivottabell og hvordan lage raske og enkle rapporter Utvidede muligheter i Pivot som grupperinger, vis verdier som og slicer   Dag 3   Metoder for dataimport Direkte import fra database   Innføring til makro Spille inn /registrere makro Ord/uttrykk og VBA editor   Datavask Slette tommer rader, fylle tomme celler Bruk av funksjoner for å klargjøre datagrunnlag Identifisere og håndtere avvik i grunnlag   Alternative temaer (hvis tid) Tips til diagrammer Hva hvis analyse Konsolidering   4 gode grunner til å velge KnowledgeGroup 1. Best practice kursinnhold 2. Markedets beste instruktører 3. Små kursgrupper 4. Kvalitets- og startgaranti   [-]

  Studieår: 2013-2014   Gjennomføring: Høst og vår Antall studiepoeng: 5.0 Forutsetninger: Grunnleggende objektorientert programmering i for eksempel Java eller C++ Innleveringer: Øvinger: 8 av 11 må være godkjent.  Personlig veileder: ja Vurderingsform: Skriftlig eksamen, 4 timer. Case-beskrivelser etc. legges ut i ItsLearning 24 timer før. (NB! Eksamensform kan bli endret under forutsetning av at ny teknologi gjør det mulig å arrangere eksamen elektronisk.) Ansvarlig: Grethe Sandstrak Eksamensdato: 05.12.13 / 08.05.14         Læremål: Etter å ha gjennomført emnet skal kandidaten ha følgende samlete læringsutbytte: KUNNSKAPER:Kandidaten:- kan gjøre rede for sentrale begreper innen objektorientering- kan konstruere et objektorientert C#. NET-program ut fra en gitt problemstilling- kan finne fram, sette seg inn i og anvende dokumentasjon om .NET Framework library- kjenner til ulike GUI-komponenter og hvordan de brukes i C#-programmer FERDIGHETER:Kandidaten kan:- sette opp programmiljø for å utvikle og kjøre C#. NET applikasjoner på egen pc- kan anvende klasser fra .NET Framework library- lage C#.NET program* med fordeling av oppgaver mellom objekter og der arv og polymorfi benyttes* med grafiske brukergrensesnitt* som kommuniserer med en database via SQL* med LINQ, delegater, templates GENERELL KOMPETANSEKandidaten kan:- kommunisere om objektorientert programmering og databaser med relevant begrepsapparat Innhold:NET-arkitekturen. Utviklingsmiljøet. Grunnleggende C#-syntaks. Objektorientert programmering med arv og polymorfi. GUI. Datafiler. Programmering mot databaser. ADO.NET, LINQ, Templates, Collections.Les mer om faget her Påmeldingsfrist: 25.08.13 / 25.01.14         Velg semester:  Høst 2013    Vår 2014     Fag C#.NET 4980,-         Semesteravgift og eksamenskostnader kommer i tillegg.    [-]