122 Programming Languages courses in Ashtead delivered Live Online

Advanced C++ training course description The course will give a broad overview of the C++ Programming language, focusing on modern C++, up to C++17. This course will cover the use of the Standard Library, including containers, iterator, function objects and algorithms. From the perspective of application development, a number of design patterns will be considered. What will you learn Write C++ programs using the more esoteric language features. Utilise OO techniques to design C++ programs. Use the standard C++ library. Exploit advanced C++ techniques Advanced C++ training course details Who will benefit: Programmers needing to write C++ code. Programmers needing to maintain C++ code. Prerequisites: C++ programming foundation. Duration 5 days Advanced C++ training course contents Study of a string class Create a string class as a means to investigate many issues, involving the use of operator overloading and including overloading new and delete. Creation of the class will also require consideration of 'const correctness'. Exception handling Consider the issues involved in exception handling including the concept of exception safety. Templates Review definition of template functions, including template parameter type deduction. Introduction to template metaprogramming. Newer features including template template parameters and variadic templates. Creation of template classes. Design patterns Introduction to Design Patterns and consideration of a number of patterns, such as, factory method, builder, singleton and adapter. The standard C++ library (STL) Standard Library features, such as, Containers, Iterator, Function Objects and Algorithms. Introduction to Lambda expressions. C++ and performance The writing of code throughout the course will be oriented towards performant code, including use of R Value references and 'move' semantics. Pointers The use of pointers will be considered throughout the course. Smart pointers will be considered to improve program safety and help avoid the use of 'raw' pointers. Threading This section will consider the creation of threads and synchronisation issues. A number of synchronisation primitives will be considered. Async and the use of Atomic will also be considered. New ANSI C++ features Summarising some of the newer features to be considered are: Auto, Lambdas expression, smart pointers, variadic templates and folds, R Value references and tuple together with structured binding.

Complete C# programming training course description This training course teaches developers the programming skills that are required for developers to create Windows applications using the C# language. Students review the basics of C# program structure, language syntax, and implementation details, and then consolidate their knowledge throughout the week as they build an application that incorporates several features of the .NET Framework. What will you learn Use the syntax and features of C#. Create and call methods, catch and handle exceptions, and describe the monitoring requirements of large-scale applications. Implement a typical desktop application. Create class, define and implement interfaces, and create and generic collections. Read and write data to/from files. Build a GUI using XAML. Complete C# programming training course details Who will benefit: Programmers wishing to learn C#. Prerequisites: Developers attending this course should already have gained some limited experience using C# to complete basic programming tasks. Duration 5 days Complete C# programming training course contents Review of C# Syntax Overview of Writing Applications using C#, Datatypes, Operators, and Expressions. C# Programming Language Constructs. Hands on Developing the Class Enrolment Application. Methods, exceptions and monitoring apps Creating and Invoking Methods. Creating Overloaded Methods and Using Optional and Output Parameters. Handling Exceptions. Monitoring Applications. Hands on Extending the Class Enrolment Application Functionality. Developing a graphical application Implementing Structs and Enums. Organizing Data into Collections. Handling Events. Hands on Writing the Grades Prototype Application. Classes and Type-safe collections Creating Classes. Defining and Implementing Interfaces. Implementing Type-safe Collections. Hands on Adding Data Validation and Type-safety to the Grades Application. Class hierarchy using Inheritance Class hierarchies. Extending .NET framework classes. Creating generic types. Hands on Refactoring common functionality into the User Class. Reading and writing local data Reading and Writing Files. Serializing and Deserializing Data. Performing I/O Using Streams. Hands on Generating the Grades Report. Accessing a Database Creating and using entity data models. Querying and updating data by using LINQ. Hands on Retrieving and modifying grade data. Accessing remote data Accessing data across the web and in the cloud. Hands on Modifying grade data in the Cloud. Designing the UI for a graphical applicatione Using XAML to design a User Interface. Binding controls to data. Styling a UI. Hands on Customizing Student Photographs and Styling the Application. Improving performance and responsiveness Implementing Multitasking by using tasks and Lambda Expressions. Performing operations asynchronously. Synchronizing concurrent data access. Hands on Improving the responsiveness and performance of the application. Integrating with unmanaged code Creating and using dynamic objects. Managing the Lifetime of objects and controlling unmanaged resources. Hands on Upgrading the grades report. Creating reusable types and assemblies Examining Object Metadata. Creating and Using Custom Attributes. Generating Managed Code. Versioning, Signing and Deploying Assemblies. Hands on Specifying the Data to Include in the Grades Report. Encrypting and Decrypting Data Implementing Symmetric Encryption. Implementing Asymmetric Encryption. Hands on Encrypting and Decrypting Grades Reports.

Complete Python training course description Python is an agile, robust, expressive, fully objectoriented, extensible, and scalable programming language. It combines the power of compiled languages with the simplicity and rapid development of scripting languages. This course covers Python from the very basics of 'hello world!' through to object oriented programming and advanced topics such as multi threading. Hands on follows all the major sections in order to reinforce the theory. What will you learn Read Python programs. Write Python programs. Debug Python programs. Use Python's objects and memory model as well as its OOP features. Complete Python programming training course details Who will benefit: Anyone wishing to learn Python. Prerequisites: None. Duration 5 days Complete Python programming training course contents Welcome to Python: What is Python? Origins, features. Downloading and installing Python, Python manuals, comparing Python, other implementations. Getting started: Program output, the print statement, "hello world!", Program input, raw_input(), comments, operators, variables and assignment, numbers, strings, lists and tuples, dictionaries, indentation, if statement, while Loop, for loop. range(), list comprehensions. Files, open() and file() built-in functions. Errors and exceptions. Functions, Classes, Modules, useful functions. Python basics: Statements and syntax, variable assignment, identifiers, basic style guidelines, memory management, First Python programs, Related modules/developer tools. Python Objects: Other built-in types, Internal Types, Standard type operators, Standard type built-in functions, Categorizing standard types, Unsupported types. Numbers: Integers, Double precision floating point numbers, Complex numbers, Operators, Built-in and factory functions, Other numeric types. Sequences: strings, lists, and tuples: Sequences, Strings, Strings and operators, String-only operators, Built-in functions, String built-in methods, Special features of strings, Unicode, Summary of string highlights, Lists, Operators, Built-in functions, List type built-in methods, Special features of lists, Tuples, Tuple operators and built-in functions, Tuples special features, Copying Python objects and shallow and deep copies. Mapping and set types: Mapping Type: dictionaries and operators, Mapping type built-in and factory functions, Mapping type built-in methods, Dictionary keys, Set types, Set type operators, Built-in functions, Set type built-in methods. Conditionals and loops: If, else and elif statements, Conditional expressions, while, for, break, continue and pass statements, else statement . . . take two, Iterators and iter(), List comprehensions, Generator expressions. Files and input/output: File objects, File built-in functions [open() and file()], File built-in methods and attributes, Standard files, Command-line arguments, File system, File execution, Persistent storage modules. Errors and exceptions: What are exceptions? Detecting and handling exceptions, Context management, Exceptions as strings, Raising exceptions, Assertions, Standard exceptions, Creating Exceptions, Why exceptions, Exceptions and the sys module. Functions: Calling, creating and passing functions, formal arguments, variable-length arguments, functional programming, Variable scope, recursion, generators. Modules: Modules and files, Namespaces, Importing modules, Module import features, Module built-in functions, Packages, Other features of modules. Object-Oriented Programming (OOP): Classes, Class attributes, Instances, Instance attributes, Binding and method invocation, Static methods and class methods, Composition, Sub-classing and derivation, Inheritance, Built-in functions for classes, and other objects, Customizing classes with special methods, Privacy, Delegation, Advanced features of new-style classes (Python 2.2+), Related modules and documentation. Execution environment: Callable and code Objects, Executable object statements and built-in functions, Executing other programs. 'Restricted' and 'Terminating' execution, operating system interface. Regular expressions: Special symbols and characters, REs and Python, Regular expressions example. Network programming: Sockets: communication endpoints, Network programming in Python, SocketServer module, Twisted framework introduction. Internet client programming: What are internet clients? Transferring files, Network news, E-mail. Multithreaded Programming: Threads and processes Python, threads, and the global interpreter lock, The thread and threading Modules. GUI programming: Tkinter and Python programming, Tkinter Examples, Brief tour of other GUIs. Web programming: Web surfing with Python: creating simple web clients, Advanced Web clients, CGI: helping web servers process client data, Building CGI applications, Using Unicode with CGI, Advanced CGI, Web (HTTP) Servers. Database programming: Python database application programmer's interface (DB-API), ORMs. Miscellaneous Extending Python by writing extensions, Web Services, programming MS Office with Win32 COM, Python and Java programming with Jython.

Learn how to work with data using Python (the coding language) as a tool. Learn how data is structured and how to manipulate it into a usable, clean form ready for analysis. Work on a small real-life project from conception to solution, in a team or on your own.

Duration 5 Days 30 CPD hours This course is intended for This intermediate and beyond level course is geared for experienced technical professionals in various roles, such as developers, data analysts, data engineers, software engineers, and machine learning engineers who want to leverage Scala and Spark to tackle complex data challenges and develop scalable, high-performance applications across diverse domains. Practical programming experience is required to participate in the hands-on labs. Overview Working in a hands-on learning environment led by our expert instructor you'll: Develop a basic understanding of Scala and Apache Spark fundamentals, enabling you to confidently create scalable and high-performance applications. Learn how to process large datasets efficiently, helping you handle complex data challenges and make data-driven decisions. Gain hands-on experience with real-time data streaming, allowing you to manage and analyze data as it flows into your applications. Acquire practical knowledge of machine learning algorithms using Spark MLlib, empowering you to create intelligent applications and uncover hidden insights. Master graph processing with GraphX, enabling you to analyze and visualize complex relationships in your data. Discover generative AI technologies using GPT with Spark and Scala, opening up new possibilities for automating content generation and enhancing data analysis. Embark on a journey to master the world of big data with our immersive course on Scala and Spark! Mastering Scala with Apache Spark for the Modern Data Enterprise is a five day hands on course designed to provide you with the essential skills and tools to tackle complex data projects using Scala programming language and Apache Spark, a high-performance data processing engine. Mastering these technologies will enable you to perform a wide range of tasks, from data wrangling and analytics to machine learning and artificial intelligence, across various industries and applications.Guided by our expert instructor, you?ll explore the fundamentals of Scala programming and Apache Spark while gaining valuable hands-on experience with Spark programming, RDDs, DataFrames, Spark SQL, and data sources. You?ll also explore Spark Streaming, performance optimization techniques, and the integration of popular external libraries, tools, and cloud platforms like AWS, Azure, and GCP. Machine learning enthusiasts will delve into Spark MLlib, covering basics of machine learning algorithms, data preparation, feature extraction, and various techniques such as regression, classification, clustering, and recommendation systems. Introduction to Scala Brief history and motivation Differences between Scala and Java Basic Scala syntax and constructs Scala's functional programming features Introduction to Apache Spark Overview and history Spark components and architecture Spark ecosystem Comparing Spark with other big data frameworks Basics of Spark Programming SparkContext and SparkSession Resilient Distributed Datasets (RDDs) Transformations and Actions Working with DataFrames Spark SQL and Data Sources Spark SQL library and its advantages Structured and semi-structured data sources Reading and writing data in various formats (CSV, JSON, Parquet, Avro, etc.) Data manipulation using SQL queries Basic RDD Operations Creating and manipulating RDDs Common transformations and actions on RDDs Working with key-value data Basic DataFrame and Dataset Operations Creating and manipulating DataFrames and Datasets Column operations and functions Filtering, sorting, and aggregating data Introduction to Spark Streaming Overview of Spark Streaming Discretized Stream (DStream) operations Windowed operations and stateful processing Performance Optimization Basics Best practices for efficient Spark code Broadcast variables and accumulators Monitoring Spark applications Integrating External Libraries and Tools, Spark Streaming Using popular external libraries, such as Hadoop and HBase Integrating with cloud platforms: AWS, Azure, GCP Connecting to data storage systems: HDFS, S3, Cassandra, etc. Introduction to Machine Learning Basics Overview of machine learning Supervised and unsupervised learning Common algorithms and use cases Introduction to Spark MLlib Overview of Spark MLlib MLlib's algorithms and utilities Data preparation and feature extraction Linear Regression and Classification Linear regression algorithm Logistic regression for classification Model evaluation and performance metrics Clustering Algorithms Overview of clustering algorithms K-means clustering Model evaluation and performance metrics Collaborative Filtering and Recommendation Systems Overview of recommendation systems Collaborative filtering techniques Implementing recommendations with Spark MLlib Introduction to Graph Processing Overview of graph processing Use cases and applications of graph processing Graph representations and operations Introduction to Spark GraphX Overview of GraphX Creating and transforming graphs Graph algorithms in GraphX Big Data Innovation! Using GPT and Generative AI Technologies with Spark and Scala Overview of generative AI technologies Integrating GPT with Spark and Scala Practical applications and use cases Bonus Topics / Time Permitting Introduction to Spark NLP Overview of Spark NLP Preprocessing text data Text classification and sentiment analysis Putting It All Together Work on a capstone project that integrates multiple aspects of the course, including data processing, machine learning, graph processing, and generative AI technologies.

Duration 4 Days 24 CPD hours This course is intended for Software engineers concerned with building, managing and deploying AI solutions that leverage Azure AI Services, Azure AI Search, and Azure OpenAI. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and generative AI solutions on Azure. AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage?Azure AI Services,?Azure AI Search, and?Azure OpenAI. The course will use C# or Python as the programming language. Prerequisites Before attending this course, students must have: Knowledge of Microsoft Azure and ability to navigate the Azure portal Knowledge of either C# or Python Familiarity with JSON and REST programming semantics Recommended course prerequisites AI-900T00: Microsoft Azure AI Fundamentals course 1 - Prepare to develop AI solutions on Azure Define artificial intelligence Understand AI-related terms Understand considerations for AI Engineers Understand considerations for responsible AI Understand capabilities of Azure Machine Learning Understand capabilities of Azure AI Services Understand capabilities of the Azure Bot Service Understand capabilities of Azure Cognitive Search 2 - Create and consume Azure AI services Provision an Azure AI services resource Identify endpoints and keys Use a REST API Use an SDK 3 - Secure Azure AI services Consider authentication Implement network security 4 - Monitor Azure AI services Monitor cost Create alerts View metrics Manage diagnostic logging 5 - Deploy Azure AI services in containers Understand containers Use Azure AI services containers 6 - Analyze images Provision an Azure AI Vision resource Analyze an image Generate a smart-cropped thumbnail 7 - Classify images Provision Azure resources for Azure AI Custom Vision Understand image classification Train an image classifier 8 - Detect, analyze, and recognize faces Identify options for face detection analysis and identification Understand considerations for face analysis Detect faces with the Azure AI Vision service Understand capabilities of the face service Compare and match detected faces Implement facial recognition 9 - Read Text in images and documents with the Azure AI Vision Service Explore Azure AI Vision options for reading text Use the Read API 10 - Analyze video Understand Azure Video Indexer capabilities Extract custom insights Use Video Analyzer widgets and APIs 11 - Analyze text with Azure AI Language Provision an Azure AI Language resource Detect language Extract key phrases Analyze sentiment Extract entities Extract linked entities 12 - Build a question answering solution Understand question answering Compare question answering to Azure AI Language understanding Create a knowledge base Implement multi-turn conversation Test and publish a knowledge base Use a knowledge base Improve question answering performance 13 - Build a conversational language understanding model Understand prebuilt capabilities of the Azure AI Language service Understand resources for building a conversational language understanding model Define intents, utterances, and entities Use patterns to differentiate similar utterances Use pre-built entity components Train, test, publish, and review a conversational language understanding model 14 - Create a custom text classification solution Understand types of classification projects Understand how to build text classification projects 15 - Create a custom named entity extraction solution Understand custom named entity recognition Label your data Train and evaluate your model 16 - Translate text with Azure AI Translator service Provision an Azure AI Translator resource Specify translation options Define custom translations 17 - Create speech-enabled apps with Azure AI services Provision an Azure resource for speech Use the Azure AI Speech to Text API Use the text to speech API Configure audio format and voices Use Speech Synthesis Markup Language 18 - Translate speech with the Azure AI Speech service Provision an Azure resource for speech translation Translate speech to text Synthesize translations 19 - Create an Azure AI Search solution Manage capacity Understand search components Understand the indexing process Search an index Apply filtering and sorting Enhance the index 20 - Create a custom skill for Azure AI Search Create a custom skill Add a custom skill to a skillset 21 - Create a knowledge store with Azure AI Search Define projections Define a knowledge store 22 - Plan an Azure AI Document Intelligence solution Understand AI Document Intelligence Plan Azure AI Document Intelligence resources Choose a model type 23 - Use prebuilt Azure AI Document Intelligence models Understand prebuilt models Use the General Document, Read, and Layout models Use financial, ID, and tax models 24 - Extract data from forms with Azure Document Intelligence What is Azure Document Intelligence? Get started with Azure Document Intelligence Train custom models Use Azure Document Intelligence models Use the Azure Document Intelligence Studio 25 - Get started with Azure OpenAI Service Access Azure OpenAI Service Use Azure OpenAI Studio Explore types of generative AI models Deploy generative AI models Use prompts to get completions from models Test models in Azure OpenAI Studio's playgrounds 26 - Build natural language solutions with Azure OpenAI Service Integrate Azure OpenAI into your app Use Azure OpenAI REST API Use Azure OpenAI SDK 27 - Apply prompt engineering with Azure OpenAI Service Understand prompt engineering Write more effective prompts Provide context to improve accuracy 28 - Generate code with Azure OpenAI Service Construct code from natural language Complete code and assist the development process Fix bugs and improve your code 29 - Generate images with Azure OpenAI Service What is DALL-E? Explore DALL-E in Azure OpenAI Studio Use the Azure OpenAI REST API to consume DALL-E models 30 - Use your own data with Azure OpenAI Service Understand how to use your own data Add your own data source Chat with your model using your own data 31 - Fundamentals of Responsible Generative AI Plan a responsible generative AI solution Identify potential harms Measure potential harms Mitigate potential harms Operate a responsible generative AI solution

Duration 5 Days 30 CPD hours This course is intended for This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems. A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language. Overview In this course, you will use R to perform common data science tasks.You will: Set up an R development environment and execute simple code. Perform operations on atomic data types in R, including characters, numbers, and logicals. Perform operations on data structures in R, including vectors, lists, and data frames. Write conditional statements and loops. Structure code for reuse with functions and packages. Manage data by loading and saving datasets, manipulating data frames, and more. Analyze data through exploratory analysis, statistical analysis, and more. Create and format data visualizations using base R and ggplot2. Create simple statistical models from data. In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable. This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business. Lesson 1: Setting Up R and Executing Simple Code Topic A: Set Up the R Development Environment Topic B: Write R Statements Lesson 2: Processing Atomic Data Types Topic A: Process Characters Topic B: Process Numbers Topic C: Process Logicals Lesson 3: Processing Data Structures Topic A: Process Vectors Topic B: Process Factors Topic C: Process Data Frames Topic D: Subset Data Structures Lesson 4: Writing Conditional Statements and Loops Topic A: Write Conditional Statements Topic B: Write Loops Lesson 5: Structuring Code for Reuse Topic A: Define and Call Functions Topic B: Apply Loop Functions Topic C: Manage R Packages Lesson 6: Managing Data in R Topic A: Load Data Topic B: Save Data Topic C: Manipulate Data Frames Using Base R Topic D: Manipulate Data Frames Using dplyr Topic E: Handle Dates and Times Lesson 7: Analyzing Data in R Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Identify Missing Values Lesson 8: Visualizing Data in R Topic A: Plot Data Using Base R Functions Topic B: Plot Data Using ggplot2 Topic C: Format Plots in ggplot2 Topic D: Create Combination Plots Lesson 9: Modeling Data in R Topic A: Create Statistical Models in R Topic B: Create Machine Learning Models in R

Duration 2 Days 12 CPD hours This course is intended for This introduction to Spring development course requires that incoming students possess solid Java programming skills and practical hands-on Java experience. This class is geared for experienced Java developers who are new to Spring, who wish to understand how and when to use Spring in Java and JEE applications. Overview Working in a hands-on learning environment, led by our expert practitioner, students will: Explain the issues associated with complex frameworks such as JEE and how Spring addresses those issues Understand the relationships between Spring and JEE, AOP, IOC and JDBC. Write applications that take advantage of the Spring container and the declarative nature of assembling simple components into applications. Understand how to configure the Spring Boot framework Understand and work on integrating persistence into a Spring application Explain Spring's support for transactions and caching Work with Spring Boot to facilitate Spring setup and configuration Apply Aspect Oriented Programming (AOP) to Spring applications Become familiar with the conditionally loading of bean definitions and Application Contexts Understand how to leverage the power of Spring Boot Use Spring Boot to create and work with JPA repositories Introduction to Spring Boot | Spring Boot Quick Start is a hands-on Spring training course geared for experienced Java developers who need to understand what the Spring Boot is in terms of today's systems and architectures, and how to use Spring in conjunction with other technologies and frameworks. This leading-edge course provides added coverage of Spring's Aspect-Oriented Programming and the use of Spring Boot. Students will gain hands-on experience working with Spring, using Maven for project and dependancy management, and, optionally, a test-driven approach (using JUnit) to the labs in the course. The Spring framework is an application framework that provides a lightweight container that supports the creation of simple-to-complex components in a non-invasive fashion. Spring's flexibility and transparency is congruent and supportive of incremental development and testing. The framework's structure supports the layering of functionality such as persistence, transactions, view-oriented frameworks, and enterprise systems and capabilities. This course targets Spring Boot 2 , which includes full support for Java SE 11 and Java EE 8. Spring supports the use of lambda expressions and method references in many of its APIs. The Spring Framework Understand the value of Spring Explore Dependency Injection (DI) and Inversion of Control (IoC) Introduce different ways of configuring collaborators Spring as an Object Factory Initializing the Spring IoC Container Configuring Spring Managed Beans Introduce Java-based configuration The @Configuration and @Bean annotations Define bean dependencies Bootstrapping Java Config Context Injection in Configuration classes Using context Profiles Conditionally loading beans and configurations Bean Life-Cycle Methods Defining Bean dependencies Introduce Spring annotations for defining dependencies Explore the @Autowired annotation Stereotype Annotations Qualifying injection points Lifecycle annotations Using properties in Java based configuration The @Value annotation Using the Candidate Components Index Introduction to Spring Boot Introduce the basics of Spring Boot Explain auto-configuration Introduce the Spring Initializr application Bootstrapping a Spring Boot application Working with Spring Boot Provide an overview of Spring Boot Introduce starter dependencies Introduce auto-configuration @Enable... annotations Conditional configuration Spring Boot Externalized Configuration Bootstrapping Spring Boot Introduction to Aspect Oriented Programming Aspect Oriented Programming Cross Cutting Concerns Spring AOP Spring AOP in a Nutshell @AspectJ support Spring AOP advice types AspectJ pointcut designators Spring Boot Actuator Understand Spring Boot Actuators Work with predefined Actuator endpoints Enabling Actuator endpoints Securing the Actuator Developing in Spring Boot Introduce Spring Boot Devtools Enable the ConditionEvaluationReport Debugging Spring Boot applications Thymeleaf Provide a quick overview of Thymeleaf Introduce Thymeleaf templates Create and run a Spring Thymeleaf MVC application Additional course details: Nexus Humans Spring Boot Quick Start | Core Spring, Spring AOP, Spring Boot 2.0 and More (TT3322) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Spring Boot Quick Start | Core Spring, Spring AOP, Spring Boot 2.0 and More (TT3322) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Game design training face to face training customised and bespoke.

Duration 3 Days 18 CPD hours This course is intended for This course is primarily designed for students who want to gain the skills necessary to use VBA to automate tasks in Excel such as collecting data from external sources, cleaning, and manipulating data. The target student may also want to learn how to create custom worksheet functions to streamline worksheet formulas and make complex worksheets easier to support, maintain, and understand. Overview In this course, you will develop and deploy VBA modules to solve business problems. You will: Identify general components of VBA and their appropriate use in solving business solutions. Record VBA macros to automate repetitive tasks. Use reference tools built into Excel to get help on VBA programming language and objects used in the Excel VBA environment. Write VBA code to create a custom worksheet function. Eliminate, avoid, or handle errors in VBA code, and optimize its performance. Control how and when macros run. Develop UserForm objects to create custom dialog boxes and windows. Use VBA to read and write data from local files and cloud services. Use VBA to clean and transform data. Run programs and commands outside of Excel and share VBA projects with other users VBA (Visual Basic for Applications) enables you to enhance and extend the capabilities of Microsoft© Excel© and other applications in the Microsoft© Office application suite. You can use VBA to perform tasks that would be difficult or impossible to do using only worksheet functions, and you can automate a wide range of tasks involving the collection, processing, analysis, and visualization of data. This course will give you a good foundation for understanding, creating, and using VBA in your own Excel workbooks, show you how to work with data across different applications, and how to package the macros and functions you create so you can back them up, move them to other computers, and share them with other users Prerequisites To ensure your success in this course, you should be an experienced Excel user who is comfortable creating and working with Excel workbooks, including tasks such as entering worksheet formulas, using absolute and relative addressing, formatting cells, and creating pivot tables and charts. This level of skill could be acquired by taking the Microsoft Excel for Office 365? (Desktop or Online) courses, Parts 1, 2, and 3 1 - Using VBA to Solve Business Problems Topic A: Use Macros to Automate Tasks in Excel Topic B: Identify Components of Macro-Enabled Workbooks Topic C: Configure the Excel VBA Environment 2 - Automating Repetitive Tasks Topic A: Use the Macro Recorder to Create a VBA Macro Topic B: Record a Macro with Relative Addressing Topic C: Delete Macros and Modules Topic D: Identify Strategies for Using the Macro Recorder 3 - Getting Help on VBA Topic A: Use VBA Help Topic B: Use the Object Browser to Discover Objects You Can Use in VBA Topic C: Use the Immediate Window to Explore Object Properties and Methods 4 - Creating Custom Worksheet Functions Topic A: Create a Custom Function Topic B: Make Decisions in Code Topic C: Work with Variables Topic D: Perform Repetitive Tasks 5 - Improving Your VBA Code Topic A: Debug VBA Errors Topic B: Deal with Errors Topic C: Improve Macro Performance 6 - Controlling How and When Macros Run Topic A: Prompt the User for Information Topic B: Configure Macros to Run Automatically 7 - Developing Custom Forms Topic A: Display a Custom Dialog Box Topic B: Program Form Events 8 - Using VBA to Work with Files Topic A: Use VBA to Get File and Directory Structure Topic B: Use VBA to Read Text Files Topic C: Use VBA to Write Text Files 9 - Using VBA to Clean and Transform Data Topic A: Automate Power Query Topic B: Transform Data Using VBA and Workbook Functions Topic C: Use Regular Expressions Topic D: Manage Errors in Data 10 - Extending the Programming Environment Beyond the Workbook Topic A: Run Other Programs and Commands Topic B: Share Your VBA Projects