Kurs i Vestland

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     [-]

ENCOR: Implementing and Operating Cisco Enterprise Network Core Technologies [-]

Modern Service Oriented Architecture [-]

Mastering Microsoft Endpoint Manager (Intune) [-]

Generelt om våre kurs En stor del av det teoretiske innholdet er tidligere benyttet under undervisning på Høgskolen i Østfold som er akkreditert av NOKUT. Madisa Consulting AS er Lean-entusiaster med svart belte sertifisering og tilbyr kurs, sertifiseringer, Lean-spill og rådgivning i Lean til private og offentlige virksomheter.  Vårt Lean sertifiseringsprogram består av 4 nivåer: Lean hvitt, gult, grønt og svart belte. For å bli sertifisert på de ulike nivåene, må kurset for hvert trinn gjennomføres og bestå skriftlig eksamen. Når det foregående nivået er utført, er du kvalifisert til neste nivå og til slutt oppnås den høyeste Lean sertifiseringen, svart belte. Men Lean svart belte sertifisering blir du også Lean Manager MC!   Vi tilbyr også skreddersydde bedriftsinterne Lean-kurs og foredrag.   Lean hvitt belte sertifiseringskurs På hvitt belte får du en GRATIS introduksjon til Lean via nettkurs. Dette er for mange starten på den magiske Lean-reisen. Nettkurset varer i ca. 45 minutter. Hvitt belte er et valgfritt steg i sertifiseringsprogrammet. Kurset kan være verdifullt hvis du kun ønsker å kunne litt om Lean. Selv om kurset passer for alle, kan dette kan være midt i blinken for styremedlemmer og ledere. Kanskje vil dette hjelpe deg eller i noen i teamet ditt for å finne ut mer om Lean eller om Lean er relevant for nettopp din virksomhet?     Ferdighetsmål Få et innblikk i driftsstrategien Lean og noen enkle metoder for forbedringsarbeid.   Kompetansemål for Lean hvitt belte sertifiseringskurs Kurset er delt opp i 3 deler. Etter introduksjonskurset skal du ha innblikk i: Del 1: Hvorfor bør du jobbe med Lean? Del 2: Hva er Lean? Her får du kjennskap til grunnleggende Lean prinsipper og historikken bak Lean. Del 3: Hvordan jobbe med Lean? Her får du kjennskap til noen utvalgte metoder/verktøy som Prosesser inklusive sløsingsanalyse, 5S og Tavlemøter     Sertifiseringskriterier Etter at kurset er gjennomført og du har svart riktig på de 10 spørsmålene, mottar du et elektronisk Lean hvitt belte sertifiseringsbevis.   Varighet Kurset varer i ca. 45 minutter.   Sted Kopier linken og lim inn i nettleseren din:  https://madisaconsulting.no/gratis-lean-hvitt-belte-sertifiseringskurs/   Pris GRATIS!!   Noen av våre referanser Høgskolen i Østfold Tine Cirkle K Europris DNB Norske Skog Flere kommuner og offentlige virksomheter   Øvrig Vi tilbyr også skreddersydde bedriftsinterne Lean-kurs og foredrag. [-]

SFWIPF: Fundamentals of Cisco Firewall Threat Defense and Intrusion Prevention [-]

SAFe® Product Owner/Product Manager Certification [-]

Bli kjent med Revu (Bruksområder, grensesnitt, menyer, verktøy, paneler og profiler) Grunnleggende PDF-håndtering med Revu Markeringsverktøy og måleverktøy Innføring i Tool Chest Innføring i Markeringslisten Innføring i Studio [-]

PRINCE2® 7 Practitioner  all english [-]

MS-102: Microsoft 365 Administrator [-]

Change Management Practitioner [-]

Engelsk Vg1 Generell studiekompetanse Dette kurset gir deg kunnskap innen engelsk på videregående skole og inngår i generell studiekompetanse. Nettkurset er basert på læreplanen i engelsk, og tar for seg språklæring, kommunikasjon, kultur, samfunn og litteratur. Varighet: Du får tilgang til kurset i 180 dager fra du melder deg på. Studiebelastning: 5 uketimer som tilsvarer 140 årstimer Finansiering: NooA Videregående er godkjent for lån i Lånekassen. Støtte er avhengig av at studenten selv har rett til støtte. Læremidler som inngår i kursprisen: Engelsk for NooA Videregående: Nettsider med oppgaver: NooA 2022. Læremidler som ikke inngår i kursprisen: Det er ikke nødvendig å kjøpe lærebok, men elevene må selv skaffe en av romanene som anbefales i kurset. Målgruppe: Kurset er utviklet for deg som vil ha generell studiekompetanse, eller ønsker å gå opp til eksamen på nytt for å få en bedre karakter. Karakterer og kursbevis: Innsendingene og kurset bedømmes med karakterer fra 1 til 6. Når alle innsendingene er bestått, vil du automatisk få tilgang til et elektronisk kursbevis. For å oppnå studiekompetanse, må du melde deg opp og bestå privatisteksamen. Eksamen: Du må selv melde deg opp til privatisteksamen  . Privatisteksamen arrangeres normalt to ganger i året ved videregående skoler. Kursbeskrivelse: Kurset er basert på læreplanen i engelsk ENG01-04 med fagkoder ENG1007 og ENG1008.   [-]

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     [-]

Kursene er inkludert lunsj! Kurs innhold: Kurset består av i alt 40 teoritimer hvor en undervisningstime varer i 45 min: 16 timer teori om traverskran 16 timer om løfteredskap 8 timer om sikkerhet, ansvar og kontroll. I tillegg til teoridelen kommer: 8 timer praktisk bruk mod 3.7 med godkjent instruktør. 16 timer praksis hos opplæringsvirksomhetene eller32 timer praksis hos en fadderbedrift Praktisk informasjon om kurs for traverskraner: Undervisningen er tilpasset de som har lese- og skrivevansker.   Pris teori del Kr. 5.950,- ink stroppekurs G11. Prisene er inkludert lunsj! Kurset vil bli holdt i våre lokaler i Fyllingsdalen Er det noe du lurer på, ta gjerne kontakt med oss på telefon 55 32 32 94 eller 450 444 33 eller fyll ut vårt kontaktskjema. [-]

Kurs i Målesystemanalyse (MSA) Du lærer å forstå variasjon i målinger og bestemme egenskaper som presisjon (repeterbarhet & reproduserbarhet), nøyaktighet (stabilitet, bias & linearitet), følsomhet og kapabilitet (evne til å detektere forskjeller i medier som måles). Du lærer også hvordan du kan designe en målesystemanalyse. Pålitelige målinger er en forutsetning for gode beslutninger.   Målesystemanalyse kan brukes til: Bestemme hvor mye av variasjonen i en prosess som er forårsaket av målesystemet. Sammenligne interne inspeksjonsstandarder med kundens standarder. Fremheve områder der kalibreringstrening er nødvendig. Evaluere opplæring i hvordan analyser utføres. Sammenligne eksisterende måleutstyr. Kvalifisere nytt måleutstyr. Sammenligne målinger mellom operatører. Sammenligne målinger mellom to (eller flere) instrument. Sammenligne målemetoder. Gi kriterier for nye målesystemer. Forstå unormale målinger. Evaluer et måleinstrument før og etter reparasjon.   Du lærer følgende: Designe målesystemanalyse Målesystemanalyse med vurdering av: presisjon og nøyaktighet repeterbarhet og reproduserbarhet stabilitet, bias og linearitet følsomhet (oppløsning) kapabilitet – evne til å detektere forskjeller i mediet som måles Du lærer å lage prosesskart for målemetoden for å forstå metoden bedre, og identifisere mulige årsaker til uønsket variasjon. Vi bruker kontrolldiagram til å bestemme egenskaper ved målemetoden. Kursdeltagerne får oppgaver underveis og svarer via avstemninger i nettmøtet.     Kursholder Kursholder Sissel Pedersen Lundeby er IASSC (International association for Six Sigma certification) akkreditert kursholder (eneste i Norge per januar 2022): "This accreditation publically reflects that you have met the standards established by IASSC such that those who participate in a training program led by you can expect to receive an acceptable level of knowledge transfer consistent with the Lean Six Sigma belt Bodies of Knowledge as established by IASSC."  Målesystemanalyse er et av verktøyene som benyttes innen Lean Six Sigma, og Sissel har bred praktisk erfaring med gjennomføring av målesystemanalyser.   Sissel er utdannet sivilingeniør i kjemiteknikk fra NTNU, og har mer enn 20 års erfaring innen produksjon og miljøteknologi. Hennes Lean Six Sigma opplæring startet i 2002, hos et amerikansk firma, hvor hun ble Black Belt sertifisert. I 2017 ble hun også Black Belt sertifisert gjennom IASSC. Sissel har svært god erfaring med å bruke Lean Six Sigma til forbedringer, og fokuserer på å skape målbare resultater. Kursene bruker praktiske, gjenkjennelige eksempler, og formidler Lean Six Sigma på en enkel, forståelig måte.     Tilbakemeldinger "Inspirerende, faglig dyktig, folkeliggjør et teoretisk fagområde" Espen Fjeld, Kommersiell direktør hos Berendsen "Faglig meget dyktig og klar fremføring. Morsom og skaper tillit" Jon Sørensen, Produksjonsleder hos Berendsen "10/10 flink til å nå alle" Erlend Stene, Salgsleder hos Berendsen "Tydelig og bra presentert. God til å kontrollspørre og lytte (sjekke forståelse)" Morten Bodding, Produksjonsleder hos Berendsen "Utgjorde en forskjell, engasjert og dyktig" Kursdeltager fra EWOS "Du er inspirerende, positiv og dyktig i faget" Kursdeltager fra EWOS "Jeg var veldig imponert over Sissels Lean Six Sigma kunnskap. Hun gjør det enkelt å identifisere forbedringer og skape resultater" Daryl Powell, Lean Manager, Kongsberg Maritime Subsea "Sissel sin evne til å raskt sette seg inn i problemstillingen, samt hennes engasjement og kompetanse er imponerende. Hennes analytiske og gjennomgående bidrag til vårt måleprogram har vært presis og målrettet, samt meget lærerik" Thomas Løvik, TWMA Norge     [-]