IT-kurs

Understand the purpose and key concepts of Problem Management, including its role in identifying and managing the root causes of incidents to prevent recurrence.   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% [-]

Understand the purpose and key concepts of Release Management, elucidating its significance in planning, scheduling, and controlling the build, test, and deployment of releases to ensure they deliver the expected outcomes. The eLearning course: Interactive Self-paced Device-friendly 2-3 hour content Mobile-optimised Practical exercises   Exam:   20 questions Multiple choise Closed book 30 minutes Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of the Service Desk practice, including how it serves as the central point of contact between the service provider and the users, facilitating effective communication. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content mobil-optimised practical exercises     Exam: 20 questions Multiple Choice 30 minutes Closed book Minimum required score to pass: (65%)   [-]

Understand the purpose and key concepts of the Supplier Management Practice, elucidating its importance in managing supplier relationships and ensuring value delivery from third-party services. 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% [-]

Understand the purpose and key concepts of Incident Management, including its role in restoring normal service operations swiftly following disruptions.   This eLearning is: Interactive Self-paced   Device-friendly   2-3 hour content   Mobile-optimised   Practical exercises   Exam: 20 questions Multiple choise 30 minutes Closed book Minimum required score to pass: 65% [-]

Understand the purpose and key concepts of the Continual Improvement Practice, elucidating its significance in fostering a culture of ongoing improvement and innovation within the organisation. 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% [-]

SAFe® Scrum Master Certification [-]

COURSE OVERVIEW The Certified Information Systems Security Professional (CISSP) is the most globally recognized certification in the cybersecurity market. CISSP validates a cybersecurity professional’s deep technical and managerial knowledge and experience to effectively design, engineer and manage an organization’s overall security posture. Please note an exam voucher is included as part of this course TARGET AUDIENCE Cybersecurity professionals with at least 5 years in the information security field. Member data has shown that amajority of CISSP holders are in middle management and a much smaller proportion are in senior or junior/entry-level positions. Roles include:• Chief Information Officer• Chief Information Security Officer• Chief Technology Officer• Compliance Manager / Officer• Director of Security• Information Architect• Information Manager / Information RiskManager or Consultant• IT Specialist / Director / Manager• Network / System Administrator• Security Administrator• Security Architect / Security Analyst• Security Consultant• Security Manager• Security Systems Engineer / Security EngineerSectorsCISSP is relevant across all sectors and industries, including:• Aerospace• Automotive• Banking, financial services, insurance (BFSI)• Construction• Cybersecurity• Energy• Engineering• Government• Healthcare, IT products, services, consulting• Manufacturing• Pharma• Retail• Telecom COURSE OBJECTIVESAfter completing this course you should be able to: Understand and apply fundamental concepts and methods related to the fields of information technology and security Align overall organizational operational goals with security functions and implementations. Understand how to protect assets of the organization as they go through their lifecycle. Understand the concepts, principles, structures and standards used to design, implement, monitor and secure operating systems, equipment, networks, applications and those controls used to enforce various levels of confidentiality, integrity and availability. Implement system security through the application of security design principles and application of appropriate security control mitigations for vulnerabilities present in common information system types and architectures. Understand the importance of cryptography and the security services it can provide in today’s digital and information age. Understand the impact of physical security elements on information system security and apply secure design principles to evaluate or recommend appropriate physical security protections. Understand the elements that comprise communication and network security coupled with a thorough description of how the communication and network systems function. List the concepts and architecture that define the associated technology and implementation systems and protocols at Open Systems Interconnection (OSI) model layers 1-7. Identify standard terms for applying physical and logical access controls to environments related to their security practice. Appraise various access control models to meet business security requirements. Name primary methods for designing and validating test and audit strategies that support business requirements. Enhance and optimize an organization’s operational function and capacity by applying and utilizing appropriate security controls and countermeasures. Recognize risks to an organization’s operational endeavours and assess specific threats, vulnerabilities and controls. Understand the System Lifecycle (SLC) and the Software Development Lifecycle (SDLC) and how to apply security to it; identify which security control(s) are appropriate for the development environment; and assess the effectiveness of software security. COURSE CONTENT Domain 1: Security and Risk Management Domain 2: Asset Security Domain 3: Security Architecture and Engineering Domain 4: Communication and Network Security Domain 5: Identity and Access Management (IAM) Domain 6: Security Assessment and Testing Domain 7: Security Operations Domain 8: Software Development Security TEST CERTIFICATION Recommended as preparation for the following exam: (ISC)2 Certified Information Systems Security Professional Gaining this accreditation is not just about passing the exam, there are a number of other criteria that need to be met including 5 years of cumulative, paid work experience in two or more of the eight domains of the (ISC)²® CISSP CBK . Full details can be found at https://www.isc2.org/cissp/default.aspx Those without the required experience can take the exam to become an Associate of (ISC)²  while working towards the experience needed for full certification Please note an exam voucher is included as part of this course   [-]

 This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. TARGET AUDIENCE This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. COURSE CONTENT Module 1: Introduction to Azure Machine Learning In this module, you will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace. Getting Started with Azure Machine Learning Azure Machine Learning Tools Lab : Creating an Azure Machine Learning WorkspaceLab : Working with Azure Machine Learning Tools After completing this module, you will be able to Provision an Azure Machine Learning workspace Use tools and code to work with Azure Machine Learning Module 2: No-Code Machine Learning with Designer This module introduces the Designer tool, a drag and drop interface for creating machine learning models without writing any code. You will learn how to create a training pipeline that encapsulates data preparation and model training, and then convert that training pipeline to an inference pipeline that can be used to predict values from new data, before finally deploying the inference pipeline as a service for client applications to consume. Training Models with Designer Publishing Models with Designer Lab : Creating a Training Pipeline with the Azure ML DesignerLab : Deploying a Service with the Azure ML Designer After completing this module, you will be able to Use designer to train a machine learning model Deploy a Designer pipeline as a service Module 3: Running Experiments and Training Models In this module, you will get started with experiments that encapsulate data processing and model training code, and use them to train machine learning models. Introduction to Experiments Training and Registering Models Lab : Running ExperimentsLab : Training and Registering Models After completing this module, you will be able to Run code-based experiments in an Azure Machine Learning workspace Train and register machine learning models Module 4: Working with Data Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments. Working with Datastores Working with Datasets Lab : Working with DatastoresLab : Working with Datasets After completing this module, you will be able to Create and consume datastores Create and consume datasets Module 5: Compute Contexts One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware. In this module, you'll learn how to manage experiment environments that ensure consistent runtime consistency for experiments, and how to create and use compute targets for experiment runs. Working with Environments Working with Compute Targets Lab : Working with EnvironmentsLab : Working with Compute Targets After completing this module, you will be able to Create and use environments Create and use compute targets Module 6: Orchestrating Operations with Pipelines Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this module. Introduction to Pipelines Publishing and Running Pipelines Lab : Creating a PipelineLab : Publishing a Pipeline After completing this module, you will be able to Create pipelines to automate machine learning workflows Publish and run pipeline services Module 7: Deploying and Consuming Models Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing. Real-time Inferencing Batch Inferencing Lab : Creating a Real-time Inferencing ServiceLab : Creating a Batch Inferencing Service After completing this module, you will be able to Publish a model as a real-time inference service Publish a model as a batch inference service Module 8: Training Optimal Models By this stage of the course, you've learned the end-to-end process for training, deploying, and consuming machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use hyperparameter tuning and automated machine learning to take advantage of cloud-scale compute and find the best model for your data. Hyperparameter Tuning Automated Machine Learning Lab : Tuning HyperparametersLab : Using Automated Machine Learning After completing this module, you will be able to Optimize hyperparameters for model training Use automated machine learning to find the optimal model for your data Module 9: Interpreting Models Many of the decisions made by organizations and automated systems today are based on predictions made by machine learning models. It's increasingly important to be able to understand the factors that influence the predictions made by a model, and to be able to determine any unintended biases in the model's behavior. This module describes how you can interpret models to explain how feature importance determines their predictions. Introduction to Model Interpretation using Model Explainers Lab : Reviewing Automated Machine Learning ExplanationsLab : Interpreting Models After completing this module, you will be able to Generate model explanations with automated machine learning Use explainers to interpret machine learning models Module 10: Monitoring Models After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes techniques for monitoring models and their data. Monitoring Models with Application Insights Monitoring Data Drift Lab : Monitoring a Model with Application InsightsLab : Monitoring Data Drift After completing this module, you will be able to Use Application Insights to monitor a published model Monitor data drift   [-]

Introduction to C# and .NET [-]

Progressive Web Apps and JavaScript [-]

COURSE OVERVIEW   This Python Programming 1 course is designed to give delegates the knowledge to develop and maintain Python scripts using the current version (V3) of Python. There are many similarities between Python V2 and Python V3. The skills gained on this course will allow the delegate to develop their own skills further using Python V2 or V3 to support the development and maintenance of scripts. The Python Programming 1 course comprises sessions dealing with syntax,variables and data types,operators and expressions,conditions and loops,functions,objects,collections,modules and packages,strings,pattern matching,exception handling,binary and text files,and databases. Exercises and examples are used throughout the course to give practical hands-on experience with the techniques covered. TARGET AUDIENCE The Python Programming 1 course course is aimed at those who want to improve their Python programming skills,and for developers/engineers who want to migrate to Python from another language,particularly those with little or no object-oriented knowledge. For those wishing to learn Python and have no previous knowledge of programming,they should look to attend our foundation course Introduction to Programming - Python. COURSE OBJECTIVES This course aims to provide the delegate with the knowledge to be able to produce Python scripts and applications that exploit all core elements of the language including variables,expressions,selection and iteration,functions,objects,collections,strings,modules,pattern matching,exception handling,I/O,and classes. COURSE CONTENT DAY 1 COURSE INTRODUCTION Administration and Course Materials Course Structure and Agenda Delegate and Trainer Introductions SESSION 1: GETTING STARTED About Python Python versions Python documentation Python runtimes Installing Python The REPL shell Python editors SESSION 2: PYTHON SCRIPTS & SYNTAX Script naming Comments Docstring Statements The backslash Code blocks Whitespace Console IO (to enable the writing of simple programs) A first Python program Script execution SESSION 3: VARIABLES & DATA TYPES Literals Identifiers Assignment Numbers (bool,int,float,complex) Binary,octal,and hexadecimal numbers Floating point accuracy Collections (str,list,tuple,set,dict) None Implicit and explicit type conversion (casting) The type function SESSION 4: OPERATORS & EXPRESSIONS Arithmetic Operators Assignment Operators Comparison Operators Logical Operators Membership Operators Bitwise Operators Identity Operators SESSION 5: CONDITIONS & LOOPS Conditional statements (if,elif,else) Nested conditional statements Short hand if/if else Python's alternative to the ternary operator Iterative statements (while,for,else) The range function Iterating over a list Break Continue Nested conditional/iterative statements COURSE CONTENTS - DAY 2 SESSION 6: FUNCTIONS Declaration Invocation Default values for parameters Named arguments args and kwargs Returning multiple values None returned Variable scope Masking and shadowing The pass keyword Recursive functions SESSION 7: OBJECTS AND CLASSES About objects Attributes and the dot notation The dir function Dunder attributes Mutability The id function Pass by reference Introduction to Classes Class Declaration and Instantiation Data attributes Methods Composition SESSION 8: LISTS About lists List syntax including slicing Getting and setting list elements Iterating over a list Checking for the presence of a value The len function List methods incl. append,insert,remove,pop,clear,copy,sort,reverse etc. The del keyword Appending to and combining lists List comprehension SESSION 9: TUPLES About tuples Tuple syntax Getting tuple elements including unpacking Iterating over a tuple Checking for the presence of a value The len function Appending to and combining tuples SESSION 10: SETS About Sets Dictionary syntax Creating,adding and removing set elements Iterating over a set Membership Testing Sorting Copying Set methods incl. union,intersection,difference,symmetric_difference etc. COURSE CONTENTS - DAY 3 SESSION 11: DICTIONARIES About dictionaries Dictionary syntax Getting and setting dictionary elements Iterating over a dictionary (keys,values,and items) Checking for the presence of a key The len function Dictionary methods incl. keys,values,items,get,pop,popitem,clear etc. The del keyword Dictionary comprehension SESSION 12: STRINGS About strings String syntax including slicing Escape characters Triple-quoted strings Concatenation Placeholders The format method Other methods e.g. endswith,find,join,lower,replace,split,startswith,strip,upper etc. A string as a list of bytes SESSION 13: MODULES & PACKAGES About modules Inbuilt modules math,random and platform the dir() and help() functions Creating and using modules the __pycache__ and the .pyc files The module search path Importing modules Namespaces Importing module objects The import wildcard Aliases Importing within a function Executable modules Reloading a module About packages Importing packaged modules Importing packaged module objects Package initialisation Subpackages Referencing objects in sibling packages The Standard Library Installing modules and packages using pip SESSION 14: PATTERN MATCHING About regular expressions Regular expression special characters Raw strings About the re module re module functions incl. match,search,findall,full match,split,sub   COURSE CONTENTS - DAY 4 SESSION 15: EXCEPTION HANDLING About exceptions and exception handling Handling exceptions (try,except,else,finally) Exception types The exception object Raising exceptions Custom exception types Built-in exceptions hierarchy SESSION 16: FILES & THE FILESYSTEM The open function Methods for seeking (seekable,seek) Methods for reading from a file (readable,read,readline,readlines) Iterating over a file Methods for writing to a file (writable,write,writelines) Introduction to context managers Text encoding schemes,codepoints,codespace ASCII and UNICODE (UTF schemes) UTF-8,binary and hexadecimal representations The ord() and chr() functions Binary files,bytes and bytearray I/O layered abstraction. About the os module os module functions incl. getcwd,listdir,mkdir,chdir,remove,rmdir etc. OSError numbers and the errno module SESSION 17: DATABASES The DB-API DP-API implementations Establishing a connection Creating a cursor Executing a query Fetching results Transactions Inserting,updating,and deleting records FOLLOW ON COURSES Python Programming 2  Data Analysis Python  Apache Web Server PHP Programming  PHP & MySQL for Web Development  PHP & MariaDB for Web Development  Perl Programming  Ruby Programming  Introduction to MySQL  Introduction to MariaDB [-]

Fleksible kurs for fremtidenNy kunnskap skal gi umiddelbar effekt, og samtidig være holdbar og bærekraftig på lang sikt. NTI AS har 30 års erfaring innen kurs og kompetanseheving, og utdanner årlig rundt 10.000 personer i Nord Europa innen CAD, BIM, industri, design og konstruksjon.   Inventor Professional Tube & Pipe design Her er et utvalg av temaene du vil lære på kurset: Manual og automatisk routing Oppbygging av rørstandarder Kollisjonsjekk Filstruktur til "tunge" samlinger i Inventor Med dette kurset lærer du de grunnleggende prinsippene og en anbefalt arbeidsflyt for å lage og dokumentere slanger og rør som skal benyttes i ditt system. Tube and Pipe Design er en del av Autodesk Inventor Professional eller Routed System Suite.   Tilpassete kurs for bedrifterVi vil at kundene våre skal være best på det de gjør - hele tiden.  Derfor tenker vi langsiktig om kompetanseutvikling og ser regelmessig kunnskapsløft som en naturlig del av en virksomhet. Vårt kurskonsept bygger på et moderne sett av ulike læringsmiljøer, som gjør det enkelt å finne riktig løsning uansett behov. Ta kontakt med oss på telefon 483 12 300, epost: salg@nticad.no eller les mer på www.nticad.no [-]

COURSE OVERVIEW If you are new to Citrix or if you are planning a move to Citrix Cloud, this course is a necessary step in enabling you with the right training and skills to manage and deploy Citrix Workspace successfully. This foundational administration course covers the aspects of installing, configuring and managing a Citrix Virtual Apps and Desktops 7 environment, how to manage an on-premises Citrix solution and migrate from an on-premises solution to cloud using the Citrix Cloud management plane. This five-day course will teach you how to deploy, install, configure, setup profile management, configure policies, printing and basic security features for on-premises Virtual Apps and Desktop solution building, and then migrating to Citrix Cloud. This course includes the exam voucher. TARGET AUDIENCE Experienced IT Professionals who want to be familiar with Citrix Virtual Apps and Desktops 7 in an on-premises environment and Citrix Cloud. Potential students include administrators or engineers responsible for the end user workspace and overall health and performance of the solution. COURSE OBJECTIVES After completing this course you should be able to: Install, configure, and manage a Citrix Virtual Apps and Desktops 7 site and Cloud connectors Identify the considerations between Citrix Virtual Apps and Desktops on-premises and the Citrix Virtual Apps and Desktops Service Deliver app and desktop resources COURSE CONTENT Architecture Overview Introduction to Citrix Virtual Apps and Desktops Architecture Overview Features Hosting Platform Considerations Citrix Virtual Apps and Desktops Service Connection Flow Process Introduction Deploy the Site Pre-Deployment Considerations Citrix Licensing Setup Delivery Controller Setup Site Setup And Management Redundancy Considerations The Apps and Desktops Images Consider Master Image Creation Methods Master Image Requirements Provision and Deliver App and Desktop Resources Machine Catalogs and Delivery Groups Provisioning Methods and Considerations Machine Creation Services (MCS) Deep Dive MCS Environment Considerations Resource Locations Provide Access to App and Desktop Resources  Consider Workspace Experience versus StoreFront  Workspace Experience User Authentication  Workspace App  Communication Flow Manage the User Experience Methods to Manage the User Experience Common User Experience Settings Published App and Desktop Presentation and Management  Published App Properties Server OS Published App Optimizations Published App Presentation Application Groups Apps and Desktops Presentation Manage Printing for User Sessions Map Printers to the User Session Printer Drivers Print Environment Considerations Citrix Profile Management Introduction and Considerations Configure Citrix Profile Management Manage the Site Delegated Administration Use PowerShell with Citrix Virtual Apps and Desktops Power Management Considerations Citrix Virtual Apps and Desktops Basic Security Considerations Citrix Admin Security Considerations XML Service Security Considerations Secure HDX External Traffic Monitor the Site Citrix Director Introduction Monitor and Interact with User Sessions Published Apps Analysis Monitor the Machines Running the VDA Site Specific Common Monitoring Alerts and Notifications Optimize Citrix Director Monitoring with Citrix ADM Introduction to Supporting and Troubleshooting Citrix Virtual Apps and Desktops Introduction to Supporting a Citrix Virtual Apps and Desktops Site Tools Proactive Administration Common Tasks Migrate To Citrix Cloud Migration Considerations Citrix Cloud Connector Deployment Citrix Virtual Apps and Desktops with an On-Premises Resource Location The Migration Process Citrix Analytics Citrix Analytics Introduction Prepare to Use Citrix Analytics Types of Analytics TEST CERTIFICATION Recommended as preparation for the following exams: CCA-V Certification exam. [-]

Excel – Du trodde du kunne det? [-]