2987 Software Development courses in Cardiff delivered Online

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. Data Exploration and Cleaning Python and the Anaconda Package Management System Different Types of Data Science Problems Loading the Case Study Data with Jupyter and pandas Data Quality Assurance and Exploration Exploring the Financial History Features in the Dataset Activity 1: Exploring Remaining Financial Features in the Dataset Introduction to Scikit-Learn and Model Evaluation Introduction Model Performance Metrics for Binary Classification Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve Details of Logistic Regression and Feature Exploration Introduction Examining the Relationships between Features and the Response Univariate Feature Selection: What It Does and Doesn't Do Building Cloud-Native Applications Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients The Bias-Variance Trade-off Introduction Estimating the Coefficients and Intercepts of Logistic Regression Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters Activity 4: Cross-Validation and Feature Engineering with the Case Study Data Decision Trees and Random Forests Introduction Decision trees Random Forests: Ensembles of Decision Trees Activity 5: Cross-Validation Grid Search with Random Forest Imputation of Missing Data, Financial Analysis, and Delivery to Client Introduction Review of Modeling Results Dealing with Missing Data: Imputation Strategies Activity 6: Deriving Financial Insights Final Thoughts on Delivering the Predictive Model to the Client

Duration 4 Days 24 CPD hours This course is intended for The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview Overview of data science and machine learning at scale Overview of the Hadoop ecosystem Working with HDFS data and Hive tables using Hue Introduction to Cloudera Data Science Workbench Overview of Apache Spark 2 Reading and writing data Inspecting data quality Cleansing and transforming data Summarizing and grouping data Combining, splitting, and reshaping data Exploring data Configuring, monitoring, and troubleshooting Spark applications Overview of machine learning in Spark MLlib Extracting, transforming, and selecting features Building and evaluating regression models Building and evaluating classification models Building and evaluating clustering models Cross-validating models and tuning hyperparameters Building machine learning pipelines Deploying machine learning models Spark, Spark SQL, and Spark MLlib PySpark and sparklyr Cloudera Data Science Workbench (CDSW) Hue This workshop covers data science and machine learning workflows at scale using Apache Spark 2 and other key components of the Hadoop ecosystem. The workshop emphasizes the use of data science and machine learning methods to address real-world business challenges. Using scenarios and datasets from a fictional technology company, students discover insights to support critical business decisions and develop data products to transform the business. The material is presented through a sequence of brief lectures, interactive demonstrations, extensive hands-on exercises, and discussions. The Apache Spark demonstrations and exercises are conducted in Python (with PySpark) and R (with sparklyr) using the Cloudera Data Science Workbench (CDSW) environment. The workshop is designed for data scientists who currently use Python or R to work with smaller datasets on a single machine and who need to scale up their analyses and machine learning models to large datasets on distributed clusters. Data engineers and developers with some knowledge of data science and machine learning may also find this workshop useful. Overview of data science and machine learning at scaleOverview of the Hadoop ecosystemWorking with HDFS data and Hive tables using HueIntroduction to Cloudera Data Science WorkbenchOverview of Apache Spark 2Reading and writing dataInspecting data qualityCleansing and transforming dataSummarizing and grouping dataCombining, splitting, and reshaping dataExploring dataConfiguring, monitoring, and troubleshooting Spark applicationsOverview of machine learning in Spark MLlibExtracting, transforming, and selecting featuresBuilding and evauating regression modelsBuilding and evaluating classification modelsBuilding and evaluating clustering modelsCross-validating models and tuning hyperparametersBuilding machine learning pipelinesDeploying machine learning models Additional course details: Nexus Humans Cloudera Data Scientist Training 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 Cloudera Data Scientist Training 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.

Duration 5 Days 30 CPD hours This course is intended for This course is designed for business professionals who leverage data to address business issues. The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming. However, there is not necessarily a single organizational role that this course targets. A prospective student might be a programmer looking to expand their knowledge of how to guide business decisions by collecting, wrangling, analyzing, and manipulating data through code; or a data analyst with a background in applied math and statistics who wants to take their skills to the next level; or any number of other data-driven situations. Ultimately, the target student is someone who wants to learn how to more effectively extract insights from their work and leverage that insight in addressing business issues, thereby bringing greater value to the business. Overview In this course, you will learn to: Use data science principles to address business issues. Apply the extract, transform, and load (ETL) process to prepare datasets. Use multiple techniques to analyze data and extract valuable insights. Design a machine learning approach to address business issues. Train, tune, and evaluate classification models. Train, tune, and evaluate regression and forecasting models. Train, tune, and evaluate clustering models. Finalize a data science project by presenting models to an audience, putting models into production, and monitoring model performance. For a business to thrive in our data-driven world, it must treat data as one of its most important assets. Data is crucial for understanding where the business is and where it's headed. Not only can data reveal insights, it can also inform?by guiding decisions and influencing day-to-day operations. This calls for a robust workforce of professionals who can analyze, understand, manipulate, and present data within an effective and repeatable process framework. In other words, the business world needs data science practitioners. This course will enable you to bring value to the business by putting data science concepts into practice Addressing Business Issues with Data Science Topic A: Initiate a Data Science Project Topic B: Formulate a Data Science Problem Extracting, Transforming, and Loading Data Topic A: Extract Data Topic B: Transform Data Topic C: Load Data Analyzing Data Topic A: Examine Data Topic B: Explore the Underlying Distribution of Data Topic C: Use Visualizations to Analyze Data Topic D: Preprocess Data Designing a Machine Learning Approach Topic A: Identify Machine Learning Concepts Topic B: Test a Hypothesis Developing Classification Models Topic A: Train and Tune Classification Models Topic B: Evaluate Classification Models Developing Regression Models Topic A: Train and Tune Regression Models Topic B: Evaluate Regression Models Developing Clustering Models Topic A: Train and Tune Clustering Models Topic B: Evaluate Clustering Models Finalizing a Data Science Project Topic A: Communicate Results to Stakeholders Topic B: Demonstrate Models in a Web App Topic C: Implement and Test Production Pipelines

Duration 3 Days 18 CPD hours This course is intended for This is an introductory level React development course for web developers. Overview Our engaging instructors and mentors are highly experienced practitioners who bring years of current 'on-the-job' experience into every classroom. Working in a hands-on learning environment, guided by our expert team, attendees will learn about and explore: A basic and advanced understanding of React components An advanced, in-depth knowledge of how React works A complete understanding of using Redux How to build, validate, and populate interactive forms How to use inline styles for perfect looking components How to test React components How to build and use components How to get control of your build process Introduction to React | React Basics is a three-day hands-on course designed to get students quickly up and running with Core React skills. Geared for more experienced web developers new to React, this course provides students with the core knowledge and hands-on skills they require to build reliable, powerful React apps.Throughought the course students will explore React fundamentals with a progressive, example-driven approach. You?ll create your first apps, learn how to write components, start handling user interaction, and manage rich forms. We end the first part by exploring the inner workings of Create React App (Facebook?s tool for running React apps), and building a multi-page app that uses client-side routing.Every project in this course was built using Create React App. Create React App is based on Webpack, a tool which helps process and bundle our various JavaScript, CSS, HTML, and image files. We explore Create React App in-depth in the module ?Using Webpack with Create React App.?Students will build Single Page Applications (SPA), create robust routing with error handling, and both class and functional reusable components.The lab project will also include the use of form validation.NOTE: This is a foundational course that explores how to build your first React application. Students who want a deeper dive, withmore intermediate level topics such as Redux, REST, Unit Testing and more might consider the TT4195 Mastering React five-daysuperset of this class as an alternative. ES6 Primer Prefer const and let over var Arrow functions Modules Object.assign() Template literals The spread operator and Rest parameters Enhanced object literals Default arguments Destructuring assignments Your First React Web Application Setting up your development environment JavaScript ES6 /ES7 What?s a component? Building The App Making The App data-driven Your app?s first interaction JSX and the Virtual DOM React Uses a Virtual DOM Why Not Modify the Actual DOM? What is a Virtual DOM? Virtual DOM Pieces ReactElement JSX

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake architectures. Module 1: Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Module 2: Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Module 3: Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Module 4: Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Module 5: Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Module 6: Architecture and course review Post course knowledge check Architecture review Course review Additional course details: Nexus Humans Building Data Lakes on AWS 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 Building Data Lakes on AWS 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.

Duration 1 Days 6 CPD hours In this hands on workshop for Agile Scrum Masters, Release Train Engineers and anyone serving as Jira Administrators, Jira experts will lead you through advanced configuration and customization settings in Jira, from installation through to customized screens, workflows, filters and reports. Jira Administration Adding and managing Users Administering and managing Groups Global Jira Settings Jira layout and interface customization User authentication and security Jira Customization Customization of screens and fields Customization of workflows Project and Board Administration Configuring and managing Projects Configuring and managing Boards Creating and managing Filters JQL Jira Integration Integrating Jira with Atlassian Tools Retrospectives and Documentation in Confluence Code management with Bitbucket Integration management with Bamboo Building a Dashboard with gadgets Jira Plug-ins and Marketplace

Duration 1 Days 6 CPD hours This course is intended for This course is intended for: Data platform engineers Solutions architects IT professionals Overview In this course, you will learn to: Apply data lake methodologies in planning and designing a data lake Articulate the components and services required for building an AWS data lake Secure a data lake with appropriate permission Ingest, store, and transform data in a data lake Query, analyze, and visualize data within a data lake In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake Introduction to data lakes Describe the value of data lakes Compare data lakes and data warehouses Describe the components of a data lake Recognize common architectures built on data lakes Data ingestion, cataloging, and preparation Describe the relationship between data lake storage and data ingestion Describe AWS Glue crawlers and how they are used to create a data catalog Identify data formatting, partitioning, and compression for efficient storage and query Lab 1: Set up a simple data lake Data processing and analytics Recognize how data processing applies to a data lake Use AWS Glue to process data within a data lake Describe how to use Amazon Athena to analyze data in a data lake Building a data lake with AWS Lake Formation Describe the features and benefits of AWS Lake Formation Use AWS Lake Formation to create a data lake Understand the AWS Lake Formation security model Lab 2: Build a data lake using AWS Lake Formation Additional Lake Formation configurations Automate AWS Lake Formation using blueprints and workflows Apply security and access controls to AWS Lake Formation Match records with AWS Lake Formation FindMatches Visualize data with Amazon QuickSight Lab 3: Automate data lake creation using AWS Lake Formation blueprints Lab 4: Data visualization using Amazon QuickSight Architecture and course review Post course knowledge check Architecture review Course review

Duration 5 Days 30 CPD hours This course is intended for This course is intended for IT Professionals who are already experienced in general Windows Server and Windows Client administration, and who want to learn more about using Windows PowerShell for administration. No prior experience with any version of Windows PowerShell, or any scripting language, is assumed. This course is also suitable for IT Professionals already experienced in server administration, including Exchange Server, SharePoint Server, SQL Server, System Center, and others. Overview After completing this course, students will be able to: Describe the functionality of Windows PowerShell and use it to run and find basic commands. Identify and run cmdlets for server administration. Work with Windows PowerShell pipeline. Describe the techniques Windows PowerShell pipeline uses. Use PSProviders and PSDrives to work with other forms of storage. Query system information by using WMI and CIM. Work with variables, arrays, and hash tables. Write basic scripts in Windows PowerShell. Write advanced scripts in Windows PowerShell. Administer remote computers. Use background jobs and scheduled jobs. Use advanced Windows PowerShell techniques. This course provides students with the fundamental knowledge and skills to use Windows PowerShell for administering and automating administration of Windows based servers. Getting Started with Windows PowerShell Overview and Background Understanding command syntax Finding commands Lab : Configuring Windows PowerShell Lab : Finding and Running Basic Commands Cmdlets for administration Active Directory administration cmdlets Network configuration cmdlets Other server administration cmdlets Lab : Windows Administration Working with the Windows PowerShell pipeline Understanding the Pipeline Selecting, Sorting, and Measuring Objects Filtering Objects Out of the Pipeline Enumerating Objects in the Pipeline Sending pipeline data as output Lab : Using the Pipeline Lab : Filtering Objects Lab : Enumerating Objects Lab : Sending output to a file Understanding How the Pipeline Works Passing the pipeline data Advanced considerations for pipeline data Lab : Working with Pipeline Parameter Binding Using PSProviders and PSDrives Using PSProviders Using PSDrives Lab : Using PSProviders and PSDrives Querying Management Information by Using WMI and CIM Understanding WMI and CIM Querying Data with WMI and CIM Making changes with WMI/CIM Lab : Working with WMI and CIM Working with variables, arrays, and hash tables Using variables Manipulating variables Manipulating arrays and hash tables Lab : Working with variables Basic scripting Introduction to scripting Scripting constructs Importing data from files Lab : Basic scripting Advanced scripting Accepting user input Overview of script documentation Troubleshooting and error handling Functions and modules Lab : Accepting data from users Lab : Implementing functions and modules Administering Remote Computers Using basic Windows PowerShell remoting Using advanced Windows PowerShell remoting techniques Using PSSessions Lab : Using basic remoting Lab : Using PSSessions Using Background Jobs and Scheduled Jobs Using Background Jobs Using Scheduled Jobs Lab : Using Background Jobs and Scheduled Jobs Using advanced Windows PowerShell techniques Creating profile scripts Using advanced techniques Lab : Practicing advanced techniques Lab : Practicing script development (optional)

Duration 5 Days 30 CPD hours This course is intended for This course is intended for IT Professionals who are already experienced in general Windows Server and Windows Client administration, and who want to learn more about using Windows PowerShell for administration. No prior experience with any version of Windows PowerShell, or any scripting language, is assumed. This course is also suitable for IT Professionals already experienced in server administration, including Exchange Server, SharePoint Server, SQL Server, System Center, and others. Overview After completing this course, students will be able to:Describe the functionality of Windows PowerShell and use it to run and find basic commands.Identify and run cmdlets for server administration.Work with Windows PowerShell pipeline.Describe the techniques Windows PowerShell pipeline uses.Use PSProviders and PSDrives to work with other forms of storage.Query system information by using WMI and CIM.Work with variables, arrays, and hash tables.Write basic scripts in Windows PowerShell.Write advanced scripts in Windows PowerShell.Administer remote computers.Use background jobs and scheduled jobs.Use advanced Windows PowerShell techniques. This course provides students with the fundamental knowledge and skills to use Windows PowerShell for administering and automating administration of Windows based servers. Getting Started with Windows PowerShell Overview and Background Understanding command syntax Finding commands Lab : Configuring Windows PowerShell Lab : Finding and Running Basic Commands Cmdlets for administration Active Directory administration cmdlets Network configuration cmdlets Other server administration cmdlets Lab : Windows Administration Working with the Windows PowerShell pipeline Understanding the Pipeline Selecting, Sorting, and Measuring Objects Filtering Objects Out of the Pipeline Enumerating Objects in the Pipeline Sending pipeline data as output Lab : Using the Pipeline Lab : Filtering Objects Lab : Enumerating Objects Lab : Sending output to a file Understanding How the Pipeline Works Passing the pipeline data Advanced considerations for pipeline data Lab : Working with Pipeline Parameter Binding Using PSProviders and PSDrives Using PSProviders Using PSDrives Lab : Using PSProviders and PSDrives Querying Management Information by Using WMI and CIM Understanding WMI and CIM Querying Data with WMI and CIM Making changes with WMI/CIM Lab : Working with WMI and CIM Working with variables, arrays, and hash tables Using variables Manipulating variables Manipulating arrays and hash tables Lab : Working with variables Basic scripting Introduction to scripting Scripting constructs Importing data from files Lab : Basic scripting Advanced scripting Accepting user input Overview of script documentation Troubleshooting and error handling Functions and modules Lab : Accepting data from users Lab : Implementing functions and modules Administering Remote Computers Using basic Windows PowerShell remoting Using advanced Windows PowerShell remoting techniques Using PSSessions Lab : Using basic remoting Lab : Using PSSessions Using Background Jobs and Scheduled Jobs Using Background Jobs Using Scheduled Jobs Lab : Using Background Jobs and Scheduled Jobs Using advanced Windows PowerShell techniques Creating profile scripts Using advanced techniques Lab : Practicing advanced techniques Lab : Practicing script development (optional)

Duration 4 Days 24 CPD hours This course is intended for This course is intended for: Developers Solutions Architects Data Engineers Anyone with little to no experience with ML and wants to learn about the ML pipeline using Amazon SageMaker Overview In this course, you will learn to: Select and justify the appropriate ML approach for a given business problem Use the ML pipeline to solve a specific business problem Train, evaluate, deploy, and tune an ML model using Amazon SageMaker Describe some of the best practices for designing scalable, cost-optimized, and secure ML pipelines in AWS Apply machine learning to a real-life business problem after the course is complete This course explores how to use the machine learning (ML) pipeline to solve a real business problem in a project-based learning environment. Students will learn about each phase of the pipeline from instructor presentations and demonstrations and then apply that knowledge to complete a project solving one of three business problems: fraud detection, recommendation engines, or flight delays. By the end of the course, students will have successfully built, trained, evaluated, tuned, and deployed an ML model using Amazon SageMaker that solves their selected business problem. Module 0: Introduction Pre-assessment Module 1: Introduction to Machine Learning and the ML Pipeline Overview of machine learning, including use cases, types of machine learning, and key concepts Overview of the ML pipeline Introduction to course projects and approach Module 2: Introduction to Amazon SageMaker Introduction to Amazon SageMaker Demo: Amazon SageMaker and Jupyter notebooks Hands-on: Amazon SageMaker and Jupyter notebooks Module 3: Problem Formulation Overview of problem formulation and deciding if ML is the right solution Converting a business problem into an ML problem Demo: Amazon SageMaker Ground Truth Hands-on: Amazon SageMaker Ground Truth Practice problem formulation Formulate problems for projects Module 4: Preprocessing Overview of data collection and integration, and techniques for data preprocessing and visualization Practice preprocessing Preprocess project data Class discussion about projects Module 5: Model Training Choosing the right algorithm Formatting and splitting your data for training Loss functions and gradient descent for improving your model Demo: Create a training job in Amazon SageMaker Module 6: Model Evaluation How to evaluate classification models How to evaluate regression models Practice model training and evaluation Train and evaluate project models Initial project presentations Module 7: Feature Engineering and Model Tuning Feature extraction, selection, creation, and transformation Hyperparameter tuning Demo: SageMaker hyperparameter optimization Practice feature engineering and model tuning Apply feature engineering and model tuning to projects Final project presentations Module 8: Deployment How to deploy, inference, and monitor your model on Amazon SageMaker Deploying ML at the edge Demo: Creating an Amazon SageMaker endpoint Post-assessment Course wrap-up Additional course details: Nexus Humans The Machine Learning Pipeline on AWS 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 The Machine Learning Pipeline on AWS 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.