61 Ecosystem courses in Liverpool delivered Live Online

Duration 3 Days 18 CPD hours This course is intended for This intermediate-level hands-on course is geared for experienced Administrators, Analysts, Architects, Data Scientists, Database Administrators and Implementers Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. Working in a hands-on learning environment led by our Oracle Certified expert facilitator, students will learn how to: Administer ODI resources and setup security with ODI Apply ODI Topology concepts for data integration Describe ODI Model concepts Describe architecture of Oracle Data Integrator Design ODI Mappings, Procedures, Packages, and Load Plans to perform ELT data transformations Explore, audit data, and enforce data quality with ODI Implement Changed Data Capture with ODI Oracle Data Integrator is a comprehensive data integration platform that covers all data integration requirements from high-volume, high-performance batch loads, to event-driven integration processes and SOA-enabled data services. Oracle Data Integrator's Extract, Load, Transform (E-LT) architecture leverages disparate RDBMS engines to process and transform the data - the approach that optimizes performance, scalability and lowers overall solution costs. Throughout this course participants will explore how to centralize data across databases, performing integration, designing ODI Mappings, and setting up ODI security. In addition, Oracle Data Integrator can interact with the various tools of the Hadoop ecosystem, allowing administrators and data scientists to farm out map-reduce operations from established relational databases to Hadoop. They can also read back into the relational world the results of complex Big Data analysis for further processing. Working in a hands-on learning environment led by our Oracle Certified expert facilitator, students will learn how to: Administer ODI resources and setup security with ODI Apply ODI Topology concepts for data integration Describe ODI Model concepts Describe architecture of Oracle Data Integrator Design ODI Mappings, Procedures, Packages, and Load Plans to perform ELT data transformations Explore, audit data, and enforce data quality with ODI Implement Changed Data Capture with ODI Introduction to Integration and Administration Oracle Data Integrator: Introduction Oracle Data Integrator Repositories Administering ODI Repositories Create and connect to the Master Repository Export and import the Master Repository Create, connect, and set a password to the Work Repository ODI Topology Concepts ODI Topology: Overview Data Servers and Physical Schemas Defining Topology Agents in Topology Planning a Topology Describing the Physical and Logical Architecture Topology Navigator Creating Physical Architecture Creating Logical Architecture Setting Up a New ODI Project ODI Projects Using Folders Understanding Knowledge Modules Exporting and Importing Objects Using Markers Oracle Data Integrator Model Concepts Understanding the Relational Model Understanding Reverse-Engineering Creating Models Organizing ODI Models and Creating ODI Datastores Organizing Models Creating Datastores Constraints in ODI Creating Keys and References Creating Conditions Exploring Your Data Constructing Business Rules ODI Mapping Concepts ODI Mappings Expressions, Join, Filter, Lookup, Sets, and Others Behind the Rules Staging Area and Execution Location Understanding Knowledge Modules Mappings: Overview Designing Mappings Multiple Sources and Joins Filtering Data Overview of the Flow in ODI Mapping Selecting a Staging Area Configuring Expressions Execution Location Selecting a Knowledge Module Mappings: Monitoring and Troubleshooting Monitoring Mappings Working with Errors Designing Mappings: Advanced Topics 1 Working with Business Rules Using Variables Datasets and Sets Using Sequences Designing Mappings: Advanced Topics 2 Partitioning Configuring Reusable Mappings Using User Functions Substitution Methods Modifying Knowledge Modules Using ODI Procedures Procedures: Overview Creating a Blank Procedure Adding Commands Adding Options Running a Procedure Using ODI Packages Packages: Overview Executing a Package Review of Package Steps Model, Submodel, and Datastore Steps Variable Steps Controlling the Execution Path Step-by-Step Debugger Starting a Debug Session New Functions Menu Bar Icons Managing ODI Scenarios Scenarios Managing Scenarios Preparing for Deployment Using Load Plans What are load plans? Load plan editor Load plan step sequence Defining restart behavior Enforcing Data Quality with ODI Data Quality Business Rules for Data Quality Enforcing Data Quality with ODI Working with Changed Data Capture CDC with ODI CDC implementations with ODI CDC implementation techniques Journalizing Results of CDC Advanced ODI Administration Setting Up ODI Security Managing ODI Reports ODI Integration with Java

Duration 4 Days 24 CPD hours This course is intended for This course is geared for experienced skilled Java developers, software developers, data scientists, machine learning experts or others who wish to transtion their coding skills to Scala, learning how to code in Scala and apply it in a practical way. This is not a basic class. Overview Working in a hands-on learning environment led by our expert instructor you'll: Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills. Discover the power of functional programming and learn techniques that will make your code more efficient,maintainable, and enjoyable to write. Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library. Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance. Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process. Explore the fascinating world of generative AI and GPT technologies, and learn how to integrate them into your projects,adding a touch of innovation and intelligence to your Scala solutions. If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. Discover the power of Scala programming in our comprehensive, hands-on technical training course designed specifically for experienced object-oriented (OO) developers. Scala is a versatile programming language that combines the best of both OO and functional programming paradigms, making it ideal for a wide range of projects, from web applications to big data processing and machine learning. By mastering Scala, you'll be able to develop more efficient, scalable, and maintainable applications. Fast Track to Scala Programming for OO / Java Developers is a four day hands-on course covers the core principles of Scala, functional programming, web application development, database connectivity, concurrency programming, testing, and interoperability between Scala and Java. Additionally, you'll explore cutting-edge generative AI and GPT technologies, learning how to integrate them into your Scala applications for intelligent suggestions or automation. Throughout the course you?ll explore the latest tools and best practices in the Scala ecosystem, gaining valuable knowledge and experience that can be directly applied to your day-to-day work. With 50% of the course content dedicated to hands-on labs, you'll gain practical experience applying the concepts you've learned across various projects, such as building functional web applications, connecting to databases, designing modular components, and implementing concurrency. Upon completing the course, you'll have a solid understanding of the language and its features, empowering you to confidently apply your new skills in data science and machine learning projects. You'll exit well-prepared to create efficient, scalable, and maintainable Scala applications, regardless of the complexity of your projects. Introduction to Scala Scala features and benefits Comparing Scala with Java and other OO languages Installing Scala and setting up the development environment Object-Oriented Programming in Scala Classes and objects Traits, mixins, and inheritance Companion objects and factories Encapsulation and polymorphism Functional Programming Basics Pure functions and referential transparency Higher-order functions and currying Immutability and persistent data structures Pattern matching and recursion Having Fun with Functional Data Structures Lists, sets, and maps in Scala Folding and reducing operations Stream processing and lazy evaluation For-comprehensions Building Web Applications in Functional Style Introduction to Play Framework Functional web routing and request handling JSON handling with Play-JSON Middleware and functional composition Connecting to a Database Introduction to Slick library Database configuration and setup Querying and updating with Slick Transactions and error handling Building Scalable and Extensible Components Modular architecture and design patterns Dependency injection with MacWire Type classes and type-level programming Implicit parameters and conversions Concurrency Programming & Akka Introduction to Akka framework and Actor model Actor systems and message passing Futures and Promises Supervision and fault tolerance Building Confidence with Testing Introduction to ScalaTest and ScalaCheck Unit testing and property-based testing Test-driven development in Scala Mocking and integration testing Interoperability between Scala and Java Calling Java code from Scala Using Java libraries in Scala projects Converting Java collections to Scala collections Writing Scala code that can be called from Java Using Generative AI and GPT Technologies in Scala Programming Overview of GPT and generative AI Integrating GPT with Scala applications Use cases and practical examples

Duration 3 Days 18 CPD hours This course is intended for This course is geared for Python experienced attendees who wish to learn and use basic machine learning algorithms and concepts. Students should have skills at least equivalent to the Python for Data Science courses we offer. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to Understand the main concepts and principles of predictive analytics Use the Python data analytics ecosystem to implement end-to-end predictive analytics projects Explore advanced predictive modeling algorithms w with an emphasis on theory with intuitive explanations Learn to deploy a predictive model's results as an interactive application Learn about the stages involved in producing complete predictive analytics solutions Understand how to define a problem, propose a solution, and prepare a dataset Use visualizations to explore relationships and gain insights into the dataset Learn to build regression and classification models using scikit-learn Use Keras to build powerful neural network models that produce accurate predictions Learn to serve a model's predictions as a web application Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This course provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. Hands-on Predictive Analytics with Python is a three-day, hands-on course that guides students through a step-by-step approach to defining problems and identifying relevant data. Students will learn how to perform data preparation, explore and visualize relationships, as well as build models, tune, evaluate, and deploy models. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seabor, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. The Predictive Analytics Process Technical requirements What is predictive analytics? Reviewing important concepts of predictive analytics The predictive analytics process A quick tour of Python's data science stack Problem Understanding and Data Preparation Technical requirements Understanding the business problem and proposing a solution Practical project ? diamond prices Practical project ? credit card default Dataset Understanding ? Exploratory Data Analysis Technical requirements What is EDA? Univariate EDA Bivariate EDA Introduction to graphical multivariate EDA Predicting Numerical Values with Machine Learning Technical requirements Introduction to ML Practical considerations before modeling MLR Lasso regression KNN Training versus testing error Predicting Categories with Machine Learning Technical requirements Classification tasks Credit card default dataset Logistic regression Classification trees Random forests Training versus testing error Multiclass classification Naive Bayes classifiers Introducing Neural Nets for Predictive Analytics Technical requirements Introducing neural network models Introducing TensorFlow and Keras Regressing with neural networks Classification with neural networks The dark art of training neural networks Model Evaluation Technical requirements Evaluation of regression models Evaluation for classification models The k-fold cross-validation Model Tuning and Improving Performance Technical requirements Hyperparameter tuning Improving performance Implementing a Model with Dash Technical requirements Model communication and/or deployment phase Introducing Dash Implementing a predictive model as a web application Additional course details: Nexus Humans Hands-on Predicitive Analytics with Python (TTPS4879) 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 Hands-on Predicitive Analytics with Python (TTPS4879) 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.

EFQM Certified Model Foundation Course The EFQM Foundation course will help you to benchmark and improve the performance of every organisation by using the new EFQM Model and RADAR diagnostic tool, version 2025! This is a two-day online course, delivered via a mix of work-rooms, presentations, videos, and one-to-one support. This course is credited as part of the admission to the EFQM Assessor, Performance Improvement Practitioner or Excellence Practitioner courses. Who is the EFQM Certified Model Foundation course for? This is the essential course for anyone who wants to find out about the EFQM Model and RADAR and how these tools can benefit an organisation. This course is suitable for anyone who wants to understand the new EFQM Model and how it can be used to make their organisation more effective. Whilst this training is effective as a stand-alone course, it is also a pre-requisite for anyone considering one of the EFQM qualification routes as a way of progressing their management development and career. At the end of the course, you will be able to: - demonstrate how the EFQM Model could benefit your organisation and how it could be used to overcome current and future challenges - explain how the Model is structured and how the different elements apply to your organisation - start applying the RADAR as both a diagnostic tool - to identify strengths and areas for improvement – and a performance improvement methodology - conduct a high-level self-assessment of your organisation - understand the opportunities provided by EFQM data and insights - gain an insight into the alternative uses of the EFQM Model The EFQM Foundation Course Training Programme Following the welcome and introduction, this course comprises of 9 modules: Module 0: Welcome & course objectives Module 1: Why we need a model to face & master complexity? Why the EFQM Model? Module 2: Introduction to the EFQM Model Module 3: The Model (part 1): Direction Module 4:The Model (part 2): Execution Module 5: The Model (part 3): Results Module 6: RADAR Module 7: Assessment Module 8: Data & Insights Module 9: Next steps Delivery The course is delivered through a virtual trainer led live class Cost £800 + VAT If you are not yet a member but are already thinking about joining CforC, you can find more information on how to become a member and the benefits by clicking here.

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 2 Days 12 CPD hours This course is intended for This class assumes some prior experience with Git, plus basic coding or programming knowledge. Overview This course is approximately 50% hands-on, combining expert lecture, real-world demonstrations and group discussions with machine-based practical labs and exercises. 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 led by our expert team, students will explore: Getting Started with Collaboration Understanding the GitHub Flow Branching with Git Local Git Configuration Working Locally with Git Collaborating on Your Code Merging Pull Requests Viewing Local Project History Streaming Your Workflow with Aliases Workflow Review Project: GitHub Games Resolving Merge Conflicts Working with Multiple Conflicts Searching for Events in Your Code Reverting Commits Helpful Git Commands Viewing Local Changes Creating a New Local Repository Fixing Commit Mistakes Rewriting History with Git Reset Merge Strategies: Rebase This is a fast-paced hands-on course that provides you with a solid overview of Git and GitHub, the web-based version control repository hosting service. While the examples in this class are related to computer code, GitHub can be used for other content. It offers the complete distributed version control and source code management (SCM) functionality of Git as well as adding its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for every project. Getting Started with The GitHub Ecosystem What is Git? Exploring a GitHub Repository Using GitHub Issues Activity: Creating A GitHub Issue Using Markdown Understanding the GitHub Flow The Essential GitHub Workflow Branching with Git Branching Defined Activity: Creating a Branch with GitHub Introduction Class Diagram Interaction Diagrams Sequence Diagrams Communication Diagrams State Machine Diagrams Activity Diagram Implementation Diagrams Local Git Configuration Checking your Git version Git Configuration Levels Viewing your configurations Configuring your username and email Configuring autocrif Working Locally with Git Creating a Local copy of the repo Our favorite Git command: git status Using Branches locally Switching branches Activity: Creating a New File The Two Stage Commit Collaborating on Your Code Collaboration Pushing your changes to GitHub Activity: Creating a Pull Request Exploring a Pull Request Activity: Code Review Merging Pull Requests Merge Explained Merging Your Pull Request Updating Your Local Repository Cleaning Up the Unneeded Branches Viewing Local Project History Using Git Log Streaming Your Workflow with Aliases Creating Custom Aliases Workflow Review Project: GitHub Games User Accounts vs. Organization Accounts Introduction to GitHub Pages What is a Fork? Creating a Fork Workflow Review: Updating the README.md Resolving Merge Conflicts Local Merge Conflicts Working with Multiple Conflicts Remote Merge Conflicts Exploring Searching for Events in Your Code What is GitHub? What is Git bisect? Finding the bug in your project Reverting Commits How Commits are made Safe operations Reverting Commits Helpful Git Commands Moving and Renaming Files with Git Staging Hunks of Changes Viewing Local Changes Comparing changes with the Repository Creating a New Local Repository Initializing a new local repository Fixing Commit Mistakes Revising your last commit Rewriting History with Git Reset Understanding reset Reset Modes Reset Soft Reset Mixed Reset Hard Does gone really mean gone? Getting it Back You just want that one commit Oops, I didn?t mean to reset Merge Strategies: Rebase About Git rebase Understanding Git Merge Strategies Creating a Linear History Additional course details: Nexus Humans Introduction to GITHub for Developers (TTDV7551) 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 Introduction to GITHub for Developers (TTDV7551) 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 4 Days 24 CPD hours This course is intended for This class is intended for experienced developers who are responsible for managing big data transformations including: Extracting, loading, transforming, cleaning, and validating data. Designing pipelines and architectures for data processing. Creating and maintaining machine learning and statistical models. Querying datasets, visualizing query results and creating reports Overview Design and build data processing systems on Google Cloud Platform. Leverage unstructured data using Spark and ML APIs on Cloud Dataproc. Process batch and streaming data by implementing autoscaling data pipelines on Cloud Dataflow. Derive business insights from extremely large datasets using Google BigQuery. Train, evaluate and predict using machine learning models using TensorFlow and Cloud ML. Enable instant insights from streaming data Get hands-on experience with designing and building data processing systems on Google Cloud. This course uses lectures, demos, and hand-on labs to show you how to design data processing systems, build end-to-end data pipelines, analyze data, and implement machine learning. This course covers structured, unstructured, and streaming data. Introduction to Data Engineering Explore the role of a data engineer. Analyze data engineering challenges. Intro to BigQuery. Data Lakes and Data Warehouses. Demo: Federated Queries with BigQuery. Transactional Databases vs Data Warehouses. Website Demo: Finding PII in your dataset with DLP API. Partner effectively with other data teams. Manage data access and governance. Build production-ready pipelines. Review GCP customer case study. Lab: Analyzing Data with BigQuery. Building a Data Lake Introduction to Data Lakes. Data Storage and ETL options on GCP. Building a Data Lake using Cloud Storage. Optional Demo: Optimizing cost with Google Cloud Storage classes and Cloud Functions. Securing Cloud Storage. Storing All Sorts of Data Types. Video Demo: Running federated queries on Parquet and ORC files in BigQuery. Cloud SQL as a relational Data Lake. Lab: Loading Taxi Data into Cloud SQL. Building a Data Warehouse The modern data warehouse. Intro to BigQuery. Demo: Query TB+ of data in seconds. Getting Started. Loading Data. Video Demo: Querying Cloud SQL from BigQuery. Lab: Loading Data into BigQuery. Exploring Schemas. Demo: Exploring BigQuery Public Datasets with SQL using INFORMATION_SCHEMA. Schema Design. Nested and Repeated Fields. Demo: Nested and repeated fields in BigQuery. Lab: Working with JSON and Array data in BigQuery. Optimizing with Partitioning and Clustering. Demo: Partitioned and Clustered Tables in BigQuery. Preview: Transforming Batch and Streaming Data. Introduction to Building Batch Data Pipelines EL, ELT, ETL. Quality considerations. How to carry out operations in BigQuery. Demo: ELT to improve data quality in BigQuery. Shortcomings. ETL to solve data quality issues. Executing Spark on Cloud Dataproc The Hadoop ecosystem. Running Hadoop on Cloud Dataproc. GCS instead of HDFS. Optimizing Dataproc. Lab: Running Apache Spark jobs on Cloud Dataproc. Serverless Data Processing with Cloud Dataflow Cloud Dataflow. Why customers value Dataflow. Dataflow Pipelines. Lab: A Simple Dataflow Pipeline (Python/Java). Lab: MapReduce in Dataflow (Python/Java). Lab: Side Inputs (Python/Java). Dataflow Templates. Dataflow SQL. Manage Data Pipelines with Cloud Data Fusion and Cloud Composer Building Batch Data Pipelines visually with Cloud Data Fusion. Components. UI Overview. Building a Pipeline. Exploring Data using Wrangler. Lab: Building and executing a pipeline graph in Cloud Data Fusion. Orchestrating work between GCP services with Cloud Composer. Apache Airflow Environment. DAGs and Operators. Workflow Scheduling. Optional Long Demo: Event-triggered Loading of data with Cloud Composer, Cloud Functions, Cloud Storage, and BigQuery. Monitoring and Logging. Lab: An Introduction to Cloud Composer. Introduction to Processing Streaming Data Processing Streaming Data. Serverless Messaging with Cloud Pub/Sub Cloud Pub/Sub. Lab: Publish Streaming Data into Pub/Sub. Cloud Dataflow Streaming Features Cloud Dataflow Streaming Features. Lab: Streaming Data Pipelines. High-Throughput BigQuery and Bigtable Streaming Features BigQuery Streaming Features. Lab: Streaming Analytics and Dashboards. Cloud Bigtable. Lab: Streaming Data Pipelines into Bigtable. Advanced BigQuery Functionality and Performance Analytic Window Functions. Using With Clauses. GIS Functions. Demo: Mapping Fastest Growing Zip Codes with BigQuery GeoViz. Performance Considerations. Lab: Optimizing your BigQuery Queries for Performance. Optional Lab: Creating Date-Partitioned Tables in BigQuery. Introduction to Analytics and AI What is AI?. From Ad-hoc Data Analysis to Data Driven Decisions. Options for ML models on GCP. Prebuilt ML model APIs for Unstructured Data Unstructured Data is Hard. ML APIs for Enriching Data. Lab: Using the Natural Language API to Classify Unstructured Text. Big Data Analytics with Cloud AI Platform Notebooks What's a Notebook. BigQuery Magic and Ties to Pandas. Lab: BigQuery in Jupyter Labs on AI Platform. Production ML Pipelines with Kubeflow Ways to do ML on GCP. Kubeflow. AI Hub. Lab: Running AI models on Kubeflow. Custom Model building with SQL in BigQuery ML BigQuery ML for Quick Model Building. Demo: Train a model with BigQuery ML to predict NYC taxi fares. Supported Models. Lab Option 1: Predict Bike Trip Duration with a Regression Model in BQML. Lab Option 2: Movie Recommendations in BigQuery ML. Custom Model building with Cloud AutoML Why Auto ML? Auto ML Vision. Auto ML NLP. Auto ML Tables.

Duration 4 Days 24 CPD hours This course is intended for This course is geared for experienced skilled Java developers, software developers, data scientists, machine learning experts or others who wish to transtion their coding skills to Scala, learning how to code in Scala and apply it in a practical way. This is not a basic class. Overview Working in a hands-on learning environment led by our expert instructor you'll: Get comfortable with Scala's core principles and unique features, helping you navigate the language confidently and boosting your programming skills. Discover the power of functional programming and learn techniques that will make your code more efficient, maintainable, and enjoyable to write. Become proficient in creating dynamic web applications using the Play Framework, and easily connect to databases with the user-friendly Slick library. Master concurrency programming with Akka, empowering you to build scalable and fault-tolerant applications that excel in performance. Enhance your testing skills using ScalaTest and ScalaCheck, ensuring the reliability and quality of your Scala applications, while having fun in the process. Explore the fascinating world of generative AI and GPT technologies, and learn how to integrate them into your projects, adding a touch of innovation and intelligence to your Scala solutions. If your team requires different topics, additional skills or a custom approach, our team will collaborate with you to adjust the course to focus on your specific learning objectives and goals. Discover the power of Scala programming in our comprehensive, hands-on technical training course designed specifically for experienced object-oriented (OO) developers. Scala is a versatile programming language that combines the best of both OO and functional programming paradigms, making it ideal for a wide range of projects, from web applications to big data processing and machine learning. By mastering Scala, you'll be able to develop more efficient, scalable, and maintainable applications. Fast Track to Scala Programming for OO / Java Developers is a four day hands-on course covers the core principles of Scala, functional programming, web application development, database connectivity, concurrency programming, testing, and interoperability between Scala and Java. Additionally, you'll explore cutting-edge generative AI and GPT technologies, learning how to integrate them into your Scala applications for intelligent suggestions or automation. Throughout the course you?ll explore the latest tools and best practices in the Scala ecosystem, gaining valuable knowledge and experience that can be directly applied to your day-to-day work. With 50% of the course content dedicated to hands-on labs, you'll gain practical experience applying the concepts you've learned across various projects, such as building functional web applications, connecting to databases, designing modular components, and implementing concurrency. Upon completing the course, you'll have a solid understanding of the language and its features, empowering you to confidently apply your new skills in data science and machine learning projects. You'll exit well-prepared to create efficient, scalable, and maintainable Scala applications, regardless of the complexity of your projects. Introduction to Scala Scala features and benefits Comparing Scala with Java and other OO languages Installing Scala and setting up the development environment Object-Oriented Programming in Scala Classes and objects Traits, mixins, and inheritance Companion objects and factories Encapsulation and polymorphism Functional Programming Basics Pure functions and referential transparency Higher-order functions and currying Immutability and persistent data structures Pattern matching and recursion Having Fun with Functional Data Structures Lists, sets, and maps in Scala Folding and reducing operations Stream processing and lazy evaluation For-comprehensions Building Web Applications in Functional Style Introduction to Play Framework Functional web routing and request handling JSON handling with Play-JSON Middleware and functional composition Connecting to a Database Introduction to Slick library Database configuration and setup Querying and updating with Slick Transactions and error handling Building Scalable and Extensible Components Modular architecture and design patterns Dependency injection with MacWire Type classes and type-level programming Implicit parameters and conversions Concurrency Programming & Akka Introduction to Akka framework and Actor model Actor systems and message passing Futures and Promises Supervision and fault tolerance Building Confidence with Testing Introduction to ScalaTest and ScalaCheck Unit testing and property-based testing Test-driven development in Scala Mocking and integration testing Interoperability between Scala and Java Calling Java code from Scala Using Java libraries in Scala projects Converting Java collections to Scala collections Writing Scala code that can be called from Java Using Generative AI and GPT Technologies in Scala Programming Overview of GPT and generative AI Integrating GPT with Scala applications Use cases and practical examples Additional course details: Nexus Humans Fast Track to Scala Programming Essentials for OO / Java Developers (TTSCL2104) 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 Fast Track to Scala Programming Essentials for OO / Java Developers (TTSCL2104) 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.

“You don't take a photograph. You make it" - Ansel Adams Photography For Parents - Editing In Lightroom Next course starts 11 March 2024 Editing is an essential skill for anyone with even just a passing interest in photography. Much as we try, we can't always get everything right in camera and editing tools help us bring our vision to life, help correct problems and allow our images to look beautifully polished.  We teach how to do all these things in Adobe Lightroom because it's a powerful, intuitive and affordable tool used by Pros around the world for that very reason. KEY COURSE INFO: Course type: Instructor-led Course duration: 4 weeks + prep module Course format: Online course with interactive edit-along live classes, feedback on your learning and support group WHAT YOU LEARN: From being able to correct issues such as over/under exposure, issues with colour or graininess to bringing your vision to life, creating mood and tone to your images, knowing how to use editing software is a must. But good and purposeful editing goes way beyond just slapping on a preset or a filter - in our course we teach you all about the tools - not just what all of them do but how, and we teach you how to know what to use and when to achieve certain effects.everything you need to allow you to style your photos, confidently use the software and fix common problems. editing to correct common photo problems creating good editing workflow using powerful precision tools editing to achieve beautiful skin tones using light and colour creatively editing to bring out a mood of a feel THIS IS THE COURSE FOR YOU IF : Love taking photos but want them to look more polished - with colours that pop and light that's perfectly balanced You want to be able to fix common issues such as under- or overexposure, white balance or colour casts so that you never need to bin an image just because it wasn't right straight out of camera You want to get more creative with light and colour to make the images look just like the moment felt. Bring on the moody edits or sun soaked edits or light kissed skin tones or creative black and white conversions You want to confidently use the editing techniques so you can bring your vision to life every time, without guesswork You want to be able to prepare your images for print so that your walls can beam with pride at the beauty that you captured Next Editing in Lightroom course starts 11 March 2024  4 core course modules plus prep module Support Facebook group Weekly edit-along live webinar Practice files to learn on Feedback on your images HERE IS WHAT YOU WILL LEARN in this course :  Our Photo Editing in Lightroom course guides you through the photo editing process in Adobe Lightroom from start to finish – from setting it up correctly, organising your collections and creating a workflow that works for you, through learning the capabilities of all the editing tools - such as adjusting exposure, colours, light or sharpening the images; all the way to printing and creating photo books. We’ll show you how to create specific artistic effects ( high impact Black and white, vintage, light and airy, dark and moody etc ), and how to fix common photography problems ( grainy images, underexposed images, wrong colours etc). At the end of the course you will be able to confidently manage your photo collection with Lightroom, achieve desired artistic effects and make your photos truly stand out. Getting around in Lightroom and Lightroom Classic - shooting in full manual - but with clarity and purpose Correcting common problems - whether your photo started too dark or too bright, your child's face was in a shade, the white balance and colours were off, the grain was heavy, the light looked dull - you can fix it all within a couple of clicks in Lightroom Using Lightroom's super powerful intelligent masking tools to quickly and efficiently edit specific parts of your image - from brightening up the eyes to rescuing overblown skies Enhancing and adding beautiful light - light makes images stand out, but it can be hard capturing the atmosphere it creates in camera at times. Lightroom is where you can bring light to life and make your images feel how what you saw in real life Knowing how to shoot in camera so you can get the most in editing - photographers will often purposefully under or overexpose images in camera so that they have more chance of balancing the image just they way they want in Lightroom. INSIDE THE MODULES - aka WHAT DO YOU LEARN? Module 0: Getting set up in Lightroom This module is released a week before the course starts to give you a chance to fully familiarise yourself with the layout, structure and the best way of working in Lightroom Classic, including guidance of creating a smooth selection and culling process that helps you see and keep the best of your images, without getting bogged down with 100s near duplicates. Module 1: Getting Started In Lightroom - Global Edits In this module we introduce you to Lightroom's most powerful tools - the global edits. We help you understand how to tweak your images to achieve the effects you're after quickly - from adjusting over and under exposure, to correcting white balance in a few different ways, to having a play with my favourite Lightroom tool - the tone curve. Major Takeaways: At the end of the module you will have gained full control of lightroom key global tools and will be able to use them to correct any global - as in affecting the whole image - issues with your images. This includes perfecting your exposure, balancing your colour and white balance, sharpening and creating clarity and more. Module 2: Targeted Edits - Masks!  Lightroom allows us to apply edits to selected parts of the image through a range of its very powerful, intelligent and manual masks. This means you can now effortlessly select your subject from the photo to adjust them separately with just one click, you can pick only the sky, the subject's eyes or only colour green or any other element and treat them differently to the rest.  Major Takeaways: You will be able to use the full range of masks in lightroom and apply your edits exactly where they are needed, and nowhere else. This includes working with with the new AI powered masks which can detect a person's individual features and allow you to selectively edit them in a few simple steps, as well as those more manual tools, that allow you to literally paint with light and colour on your photographs. Module 3: Creative Editing in Lightroom  This is a brand new module in our course where we take all the tools introduced in Modules 1 and 2 and actually use them to achieve different effects on our photos. Wer show you how to be guided by light when editing, how to achieve a light and airy or a moody edit on your images, how to add light and how to convert to black and white, as well as how to edit skin in Lightroom! This module is all play!  Major Takeaways: You will learn how to create yoru own editing process - from knowing where to start and to understanding what elements of your photograph may need or benefit from adjusting and knowing how you can do so to achieve different outcomes. You will also learn how to edit your skin tones so they look delightful - no matter what your subject's complexion.  Module 4: After the edits We close the course with a module centered around all the things you might want to do after the edits - from preparation for print, creating photo books, but also sharing your images online and syncing them between different machines and adobe workplaces. PLUS there is a little Photoshop intro so you get a glimpse at how it's different and what it can do.    Major Takeaways: You will learn how to connect all of your Adobe ecosystem and sync and share your images across multiple devices. You will learn how to prepare your images for print so they look their very best and how to create photobooks. Next course starts 11 March 2024 Absolutely, without a doubt book Editing. I was very unsure at first. My interest in photography is to capture those memories so it felt like it was 'cheating' to edit. I can honestly say it's like magic.To finish off the pictures you love and just give you complete control over the exposure, colours to make them perfect, to rescue the disasters etc. I learnt so much in those few weeks and it made the biggest difference to my photos.  Kerry White Editing in LIGHTROOM Book your space now: COURSE STARTS : 11 March 2024 ( 4 weeks + 1 prep week)COURSE COST : £179 ( payment plans available ) What’s included: 4 weekly modules with step by step illustrated tutorials and videos + prep module weekly edit along class online class ( also recorded) - on Wednesday evenings feedback and advice on your edits private support group PDF workbook and downloadable videos Practice images to learn on Join our next group to really bring life to your images and make them look sleep, polished and full of light COURSE PREREQUISITES: Lightroom or Lightroom Classic CC software installed on your computer or tablet. RECOMENDED : Lightroom Photography Plan 25GB – a monthly subscription at approx £9pm – including Lightroom CC, Lightroom Classic CC and Photoshop. This plan can also be occasionally purchased at a lower price via Amazon or Groupon if you purchase a year’s worth of subscription in one go ( average savings – around 2-4 months worth depending on the deal) Please note that Adobe also has a Lightroom Plan with 1TB of storage – we do not recommend this plan as it restricts you to one version of software only. The increased Cloud storage can always be purchased as an add on or your plan can be switched from Photographer plan to CC only at a later stage if you prefer.

Duration 5 Days 30 CPD hours This course is intended for This course is for all Oracle Professionals. Specifically Database Administrators, Web Server Administrators, System Administrators, CIOs and other IT Management Professionals. Overview Upon successful completion of this course, students will be able to understand configuration for installation, configuration of a host system, using Oracle Restart framework, troubleshooting tips, how the database fits with other systems, internals of the database, database failures and other topics. In this course, students will focus on configuration on supported systems. Also how the database fits in with other systems. Oracle Database Architercture Overview The Database Instance Database Memory Structures Database Process Structures Database Storage Architecture Oracle Clusterware Configuring the Host for Standalone Installlation Host Configuration Overview Choosing a Database Host Choosing an Operating System Proprietary Unix vs Open-Source Linux Making the OS Selection Prepare an Oracle LINUX Installation Perform Oracle LINUX Installation Configure the LINUX Host Step 1 - Confirm General System Requirements Step 2 - Confirm The Operating System Platform Step 3 - Confirm LINUX Package Requirements Step 4 - Confirm Network Configuration Configure Host for Virtualization VM Technologies Configure Oracle Linux for Virtualization Step 1 - Download Linux Updates Step 2 - Check Linux Kernel Step 3 - Download Latest yum Configuration File Step 4 - Enable Oracle Linux Add-ons Step 5 - Install VirtualBox Oracle Optimal Flexible Architecture OFA Goals ORACLE_BASE ORACLE_HOME Database Files Multiple ORACLE_HOMEs Databse Software Upgrades Mutliple Oracle Software Installations GRID Infrastructure Configuration Configure the Linux Installation About ASM Devices Configure Linux Devices for ASM Partioning a Device Oracle ASMLib Configue MS Windows Devices GRID Infrastructure Installation Perform the Installation Download Unpack the Installation Package Launch the Installation Session The Installation Dialog Verify the Installation Operating System Confirmation Oracle Utilities Setup SQL*Plus Confirmation Using EM Cloud Control Troubleshooting Problems DATABASE Installation Configuraion Configure a Linux Installation System Groups & Users Configure Kernel Parameters Create the Physical Directories Configure MS Windows Installation Prerequisite Checks & Fixup Utility DATABASE Installation ABOUT THE INSTALLATION The Installation Tools About The Installation Dialog The Installation Session Log PERFORM INSTALLATION ON LINUX Download Unpack The Installation Files Set Environment Variables Launch The Installation SERVER CLASS INSTALLATION DIALOG More About The Global Database Name About Enterprise Manager Cloud Control Secure The New Database PERFORM INSTALLATION ON WINDOWS Launch The Installation POST---INSTALLATION CONFIGURATION Required Environment Variables Defining The Environment Variables About SSH VERIFY THE INSTALLATION SQL*PlusConfirmation Operating System Confirmation Firewall Configuration Accessing EM Database Express Creating Databses Using DBCA About DBCA DBCA Templates Are You Sure? DEFAULT CONFIGURATION DATABASE ADVANCED MODE DATABASE File Location Variables Database Vault and Label Security CONFIGURE AN EXISTING DATABASE DELETE A DATABASE MANAGE TEMPLATES MANUALLY CREATE A DATABASE The CREATE DATABASE Command Using Oracle Restart WHAT IS ORACLE RESTART? What Oracle Restart Does Is Oracle Restart Deprecated? Registering With Oracle Restart More About srvctl Terminal Session Configuration COMPONENT STATUS USING SRVCTL Database Status Grid Infrastructure Status Oracle Home Status CONFIGURATION USING SRVCTL Examining A Database Configuration Examining The Listener Configuration Examining The ASM Configuration Manual Registration ListenerConfiguration Modification Database ConfigurationModification STARTUP/SHUTDOWN USING SRVCTL Why Use srvctl? MANAGING ORACLE RESTART Obtaining Oracle Restart Status Start/Stop Oracle Restart Preparing for a Database Upgrade What is a Database Upgrade? Database Upgrade Methods Real World Database Upgrade Develop A Database Upgrade Plan ABOUT THE DATABASE VERSION Direct Upgrade The COMPATIBLE Database Parameter PRE-UPGRADE PREPARATIONS Oracle Warehouse Builder Oracle Label Security (OLS) Oracle Database Vault Locating The Older Database Installations THE PRE-UPGRADE INFORMATION TOOL Pre-Upgrade Information Tool Files Run The Pre---Upgrade Information Tool Examining The Pre---Upgrade Information Tool Results Upgrade to Oracle Databse 12c Launch DBUA Database Upgrade Dialog Post-Upgrade Tasks Post-Upgrade Checklist Enable Unified Auditing Migrate to Unified Auditing Database Parameter Changes Enable New Features Source Destination Specific Post-Upgrade Tasks Oracle Architecture: The Systems Infrastructure About Enterprise Architectures The Relational Database Legacy Computing Models The Multi-Tiered Computing Model Scaling Up Cloud-Based Deployment ORACLE INFRASTRUCTURE ECOSYSTEM USING ORACLE ENTERPRISE MANAGER More About EM Using EM Database Express Using EM Cloud Control Oracle Architecture: The Database Host The Database Server Stack PROCESSOR LAYER CPU Resources Memory Resources I/O & STORAGE PROCESSING OS LAYER PROCESSING MODES DATABASE SERVER VIRTUALIZATION STORAGE VIRTUALIZATION ORACLE DATABASE SERVER STACK ORACLE ENGINEERED SYSTEMS Oracle Exadata Database Platform Exalogic Cloud Machine Exalytics BI Machine Oracle Architecture: Princilples & Technology Concepts Grid Computing Principles Why Grid Computing? What Is Grid Computing? PARALLELIZATION PRINCIPLES Hardware Parallelization Grid Computing Devices Clustered Database Servers CLOUD COMPUTING PRINCIPLES Multi-Tenancy Oracle Architecture: The RDBMS Installation & the Database Instance The Database Server Software Database Versions & Releases Database Editions Using PRODUCT_COMPONENT_VERSION View The Core Database Components Using V$VERSION View Understanding The Database Version Number The COMPATIBLE Database Parameter DATABASE INSTANCE ELEMENTS Individual Elements Of A Database Instance Physical Database Elements An Operational Database installation DATABASE INSTANCE CONFIGURATIONS Single Instance Parameter Files & Instance Configuration MAX_STRING_SIZE Parameter Example Independent Instances Clustered Instances The Database Instance In A Multi-tenant Configuration RECONFIGURING A DATABASE INSTANCE Static Vs. Dynamic Parameters Dynamic Parameter Setting Parameter Setting Scope Parameter Setting Level Setting Upgrade Related Parameters DATABASE COMPONENTS Advanced Data Functionality Components Security Components High-Performance Components Administration Components Database F