Oversikt over relevante kurs innen IT-kurs i Aust-Agder

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 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 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 Plan, Implement, and Control practices, highlighting their importance in establishing, executing, and governing IT service strategies effectively. This eLearning is: Interactive Self-paced Device-friendly 2-3 hours content Mobile-optimised Practical exercises   Exam: 60 questions Multiple Choice 90 Minutes Closed book Pass Mark: 65% [-]

Kurset fokuserer på konvertering av etterspørsel til verdi via IT-relaterte tjenester. Modulen dekker sentrale emner som SLA-design, styring av flere leverandører, kommunikasjon, relasjonsstyring, CX- og UX-design, kartlegging av kunder og mer. E-læringskurset inneholder 18 timer med undervisning, og er delt inn i 8 moduler. Les mer om ITIL® 4 på  AXELOS sine websider. Du vil motta en e-post med tilgang til e-læringen, sertifiseringsvoucher og digital bok fra Peoplecert. Du avtaler tid for sertifiseringen som beskrevet i e-posten fra Peoplecert. [-]

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 the Monitor, Support, and Fulfil practices, elucidating their importance in maintaining, supporting, and delivering IT services effectively.InteractiveOur eLearning:Self-pacedDevice-friendly12 hour contentMobile-optimised Exam:60 questionsMultiple Choice90 minutesClosed bookMinimum required score to pass: 65%  [-]

DP-900: Microsoft Azure Data Fundamentals [-]

COURSE OVERVIEW  Through a combination of lecture and hands-on labs, you will learn how to install, operate, configure, and verify a basic IPv4 and IPv6 network. The course covers configuring network components such as switches, routers, and Wireless LAN Controllers; managing network devices; and identifying basic security threats. Network programmability, automation, and software-defined networking are also covered at a foundational level.   This course helps you prepare to take the 200-301 Cisco Certified Network Associate (CCNA) exam.   Please note that this course is a combination of Instructor-Led and Self-Paced Study - 5 days in the classroom and approx 3 days of self study. The self-study content will be provided as part of the digital courseware that you recieve at the beginning of the course and should be part of your preparation for the exam. Lab access is provided for both the class and the self- study sections, lab access is valid for 60 hours or 90 days whichever is the shorter, so please ensure you exit the lab exercises when not in use. TARGET AUDIENCE Anyone looking to start a career in networking or wishing to achieve the Cisco CCNA Certification. COURSE OBJECTIVES After completing this course you should be able to: Identify the components of a computer network and describe their basic characteristics Understand the model of host-to-host communication Describe the features and functions of the Cisco IOS Software Describe LANs and the role of switches within LANs Describe Ethernet as the network access layer of TCP/IP and describe the operation of switches Install a switch and perform the initial configuration Describe the TCP/IP internet Layer, IPv4, its addressing scheme, and subnetting Describe the TCP/IP Transport layer and Application layer Explore functions of routing Implement basic configuration on a Cisco router Explain host-to-host communications across switches and routers Identify and resolve common switched network issues and common problems associated with IPv4 addressing Describe IPv6 main features, addresses and configure and verify basic IPv6 connectivity Describe the operation, benefits, and limitations of static routing Describe, implement and verify VLANs and trunks Describe the application and configuration of inter-VLAN routing Explain the basics of dynamic routing protocols and describe components and terms of OSPF Explain how STP and RSTP work Configure link aggregation using EtherChannel Describe the purpose of Layer 3 redundancy protocols Describe basic WAN and VPN concepts Describe the operation of ACLs and their applications in the network Configure internet access using DHCP clients and explain and configure NAT on Cisco routers Describe the basic QoS concepts Describe the concepts of wireless networks, which types of wireless networks can be built and how to use WLC Describe network and device architectures and introduce virtualization Introduce the concept of network programmability and SDN and describe the smart network management solutions like Cisco DNA Center, SD-Access and SD-WAN Configure basic IOS system monitoring tools Describe the management of Cisco devices Describe the current security threat landscape Describe threat defense technologies Implement a basic security configuration of the device management plane Implement basic steps to harden network devices COURSE CONTENT Exploring the Functions of Networking Introducing the Host-To-Host Communications Model Operating Cisco IOS Software Introducing LANs Exploring the TCP/IP Link Layer Starting a Switch Introducing the TCP/IP Internet Layer, IPv4 Addressing, and Subnets Explaining the TCP/IP Transport Layer and Application Layer Exploring the Functions of Routing Configuring a Cisco Router Exploring the Packet Delivery Process Troubleshooting a Simple Network Introducing Basic IPv6 Configuring Static Routing Implementing VLANs and Trunks Routing Between VLANs Introducing OSPF Building Redundant Switched Topologies (Self-Study) Improving Redundant Switched Topologies with EtherChannel Exploring Layer 3 Redundancy (Self-Study) Introducing WAN Technologies (Self-Study) Explaining Basics of ACL Enabling Internet Connectivity Introducing QoS (Self-Study) Explaining Wireless Fundamentals (Self-Study) Introducing Architectures and Virtualization (Self-Study) Explaining the Evolution of Intelligent Networks Introducing System Monitoring Managing Cisco Devices Examining the Security Threat Landscape (Self-Study) Implementing Threat Defense Technologies (Self-Study) Securing Administrative Access Implementing Device Hardening Labs: Get Started with Cisco CLI Observe How a Switch Operates Perform Basic Switch Configuration Inspect TCP/IP Applications Configure an Interface on a Cisco Router Configure and Verify Layer 2 Discovery Protocols Configure Default Gateway Explore Packet Forwarding Troubleshoot Switch Media and Port Issues Troubleshoot Port Duplex Issues Configure Basic IPv6 Connectivity Configure and Verify IPv4 Static Routes Configure IPv6 Static Routes Configure VLAN and Trunk Configure a Router on a Stick Configure and Verify Single-Area OSPF Configure and Verify EtherChannel Configure and Verify IPv4 ACLs Configure a Provider-Assigned IPv4 Address Configure Static NAT Configure Dynamic NAT and PAT Log into the WLC Monitor the WLC Configure a Dynamic (VLAN) Interface Configure a DHCP Scope Configure a WLAN Define a RADIUS Server Explore Management Options Explore the Cisco DNA Center Configure and Verify NTP Create the Cisco IOS Image Backup Upgrade Cisco IOS Image Configure WLAN Using WPA2 PSK Using the GUI Secure Console and Remote Access Enable and Limit Remote Access Connectivity Secure Device Administrative Access Configure and Verify Port Security Implement Device Hardening TEST CERTIFICATION Recommended as preparation for the following exams:  200-301 -  Cisco Certified Network Associate Exam (CCNA) [-]

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     [-]

TOGAF® EA Course Combined [-]

MS-101: Microsoft 365 Mobility and Security [-]

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 [-]

Oracle GoldenGate 19c: Fundamentals for Oracle [-]