1167 Model courses delivered Live 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 3 Days 18 CPD hours This course is intended for This course is geared for experienced Scala developers who are new to the world of machine learning and are eager to expand their skillset. Professionals such as data engineers, data scientists, and software engineers who want to harness the power of machine learning in their Scala-based projects will greatly benefit from attending. Additionally, team leads and technical managers who oversee Scala development projects and want to integrate machine learning capabilities into their workflows can gain valuable insights from this course Overview Working in a hands-on learning environment led by our expert instructor you'll: Grasp the fundamentals of machine learning and its various categories, empowering you to make informed decisions about which techniques to apply in different situations. Master the use of Scala-specific tools and libraries, such as Breeze, Saddle, and DeepLearning.scala, allowing you to efficiently process, analyze, and visualize data for machine learning projects. Develop a strong understanding of supervised and unsupervised learning algorithms, enabling you to confidently choose the right approach for your data and effectively build predictive models Gain hands-on experience with neural networks and deep learning, equipping you with the know-how to create advanced applications in areas like natural language processing and image recognition. Explore the world of generative AI and learn how to utilize GPT-Scala for creative text generation tasks, broadening your skill set and making you a more versatile developer. Conquer the realm of scalable machine learning with Scala, learning the secrets to tackling large-scale data processing and analysis challenges with ease. Sharpen your skills in model evaluation, validation, and optimization, ensuring that your machine learning models perform reliably and effectively in any situation. Machine Learning Essentials for Scala Developers is a three-day course designed to provide a solid introduction to the world of machine learning using the Scala language. Throughout the hands-on course, you?ll explore a range of machine learning algorithms and techniques, from supervised and unsupervised learning to neural networks and deep learning, all specifically crafted for Scala developers. Our expert trainer will guide you through real-world, focused hands-on labs designed to help you apply the knowledge you gain in real-world scenarios, giving you the confidence to tackle machine learning challenges in your own projects. You'll dive into innovative tools and libraries such as Breeze, Saddle, DeepLearning.scala, GPT-Scala (and Generative AI with Scala), and TensorFlow-Scala. These cutting-edge resources will enable you to build and deploy machine learning models for a wide range of projects, including data analysis, natural language processing, image recognition and more. Upon completing this course, you'll have the skills required to tackle complex projects and confidently develop intelligent applications. You?ll be able to drive business outcomes, optimize processes, and contribute to innovative projects that leverage the power of data-driven insights and predictions. Introduction to Machine Learning and Scala Learning Outcome: Understand the fundamentals of machine learning and Scala's role in this domain. What is Machine Learning? Machine Learning with Scala: Advantages and Use Cases Supervised Learning in Scala Learn the basics of supervised learning and how to apply it using Scala. Supervised Learning: Regression and Classification Linear Regression in Scala Logistic Regression in Scala Unsupervised Learning in Scala Understand unsupervised learning and how to apply it using Scala. Unsupervised Learning:Clustering and Dimensionality Reduction K-means Clustering in Scala Principal Component Analysis in Scala Neural Networks and Deep Learning in Scala Learning Outcome: Learn the basics of neural networks and deep learning with a focus on implementing them in Scala. Introduction to Neural Networks Feedforward Neural Networks in Scala Deep Learning and Convolutional Neural Networks Introduction to Generative AI and GPT in Scala Gain a basic understanding of generative AI and GPT, and how to utilize GPT-Scala for natural language tasks. Generative AI: Overview and Use Cases Introduction to GPT (Generative Pre-trained Transformer) GPT-Scala: A Library for GPT in Scala Reinforcement Learning in Scala Understand the basics of reinforcement learning and its implementation in Scala. Introduction to Reinforcement Learning Q-learning and Value Iteration Reinforcement Learning with Scala Time Series Analysis using Scala Learn time series analysis techniques and how to apply them in Scala. Introduction to Time Series Analysis Autoregressive Integrated Moving Average (ARIMA) Models Time Series Analysis in Scala Natural Language Processing (NLP) with Scala Gain an understanding of natural language processing techniques and their application in Scala. Introduction to NLP: Techniques and Applications Text Processing and Feature Extraction NLP Libraries and Tools for Scala Image Processing and Computer Vision with Scala Learn image processing techniques and computer vision concepts with a focus on implementing them in Scala. Introduction to Image Processing and Computer Vision Feature Extraction and Image Classification Image Processing Libraries for Scala Model Evaluation and Validation Understand the importance of model evaluation and validation, and how to apply these concepts using Scala. Model Evaluation Metrics Cross-Validation Techniques Model Selection and Tuning in Scala Scalable Machine Learning with Scala Learn how to handle large-scale machine learning problems using Scala. Challenges of Large-Scale Machine Learning Data Partitioning and Parallelization Distributed Machine Learning with Scala Machine Learning Deployment and Production Understand the process of deploying machine learning models into production using Scala. Deployment Challenges and Best Practices Model Serialization and Deserialization Monitoring and Updating Models in Production Ensemble Learning Techniques in Scala Discover ensemble learning techniques and their implementation in Scala. Introduction to Ensemble Learning Bagging and Boosting Techniques Implementing Ensemble Models in Scala Feature Engineering for Machine Learning in Scala Learn advanced feature engineering techniques to improve machine learning model performance in Scala. Importance of Feature Engineering in Machine Learning Feature Scaling and Normalization Techniques Handling Missing Data and Categorical Features Advanced Optimization Techniques for Machine Learning Understand advanced optimization techniques for machine learning models and their application in Scala. Gradient Descent and Variants Regularization Techniques (L1 and L2) Hyperparameter Tuning Strategies

Gain a deep understanding of Production Sharing Contracts (PSC) and related agreements through our expert-led course. Enroll now and excel in your field with EnergyEdge.

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 5 Days 30 CPD hours This course is intended for This course is designed for the Dynamics 365 Supply Chain Management Functional Consultant Expert or those whose role includes implementing and configuring advanced features of Dynamics 365 Supply Chain Management. Overview Objectives: Create products as type Item and Service. Set up product unit conversions. Set up transfer orders Set up default order settings. Create product masters with predefined variants. Create and set up category hierarchies. Create product attributes. Create bill of materials using BOM designer Discrete manufacturing concepts Production order statuses Production stages Bill of materials (BOM) Resources Routes and operations Configure commodity pricing in Supply Chain Management. Configure product compliance in Supply Chain Management. Configure commodity pricing in Supply Chain Management. Configure product compliance in Supply Chain Management. Configure process manufacturing. Create and configure catch weight items. Configure approved vendors. Understand the features in engineering change management. Learn how the end-to-end engineering change management process works. Set up engineering organizations. Work with product versioning. Create dimension groups by using the version dimensions. Create product life cycle states. Use engineering categories. Set up engineering change severities and severity rules. Set up product owners. Use a workflow with engineering change management. Create an engineering change request. Learn about business impacts. Create an engineering change order. Important terminology to help you understand the concepts and processes that are associated with product configuration. Product configuration end-to-end scenario. Different areas that span the product configuration process. The product configuration model. How to build a product configuration model. Configure the costing sheet. Perform BOM calculation and analyze costs by using the costing sheet. IoT intelligence and insights in Supply Chain Management This course is designed to build your in Dynamics 365 Supply Chain Management application knowledge. This course will cover the most important features and functionalities needed by Dynamics 365 Supply Chain Management functional consultant including: The product information and how to configure, create, and manage your product and inventory. Supply chain management configuration and processing. The transportation management features, and the warehouse management features. Asset Management functionalities. Master planning configuration and processing. Sales and procurement processes. Create products and product masters in Dynamics 365 Supply Chain Management Product information workspace Concept of a product Create products Set up unit conversions Batch disposition codes Default order settings Define products as not stocked Create product masters with variants Gain productivity by using the Variant suggestions page Create and set up category hierarchies and attributes Set up item pricing Create bill of materials in Dynamics 365 Supply Chain Management Work with the BOM designer BOM and formula versions BOM line types BOM levels Get started with discrete manufacturing in Dynamics 365 Supply Chain Management Discrete manufacturing life cycle Master planning and planned production orders Bills of materials Working with BOM and item configurations Create a bill of materials Production orders Work with commodity pricing and compliance in Dynamics 365 Supply Chain Management Configure commodity pricing Configure product compliance Configure process manufacturing in Dynamics 365 Supply Chain Management Process manufacturing item types Production type setup Set up packaging and batch attributes Shelf life related setup Item model group, product compliance, and approved vendors Catch weight Transaction adjustments Catch weight item handling policy Catch weight tags Configure approved vendors Get started with Engineering Change Management for Dynamics 365 Supply Chain Management Overview of features End-to-end walkthrough of features Set up versioned products in Dynamics 365 Supply Chain Management Create an engineering organization Create nomenclature for product versions Create product version number rules Create product dimension groups by using the version dimension Create product life cycle states Engineering attributes Product readiness policies Product release policies Create engineering categories Configure engineering change management for Dynamics 365 Supply Chain Management Set up engineering change severities Set up severity rule sets Set up product owners Engineering workflows Request and follow up with product changes in Dynamics 365 Supply Chain Management Engineering change requests Engineering change orders up with product changes in Dynamics 365 Supply Chain Management Get started with product configuration in Dynamics 365 Supply Chain Management Elements of a product configuration model Expression constraints and table constraints in product configuration models Validate and test a product configuration model Finalize a model for configuration Set up a product configuration model Calculations for product configuration models Solver strategy for product configuration Reuse product configurations Release a product configuration model Customize a product configuration model Work with the costing sheet in Dynamics 365 Supply Chain Management Costing versions Cost groups Calculation groups Costing sheets BOM calculations BOM Measurements BOM Reports Configure formulas for process manufacturing in Dynamics 365 Supply Chain Management Formulas, formula lines, and formula versions Formula features Approve and activate formulas and formula versions Use step consumption Coproducts By-products Burden allocation Planning items Get started with production control in Dynamics 365 Supply Chain Management Core concepts in production control Understand unified manufacturing Manufacturing principles Overview of the production process and production life cycle Batch orders Discrete manufacturing Process manufacturing Lean manufacturing Configure production control for unified manufacturing Capacity planning Integration between the General ledger and the Production control modules IoT intelligence and insights Describe the value proposition for mixed-reality Guides for production floor workers Use warehouse management for manufacturing in Dynamics 365 Supply Chain Management Production input location Production output location Staging and order picking Release BOM and formula lines to the warehouse Cross-docking Planned cross docking Visibility into material exceptions Work with manufacturing execution in Dynamics 365 Supply Chain Management Understand the manufacturing executions Identify roles in manufacturing execution Planning consideration for manufacturing execution Configure manufacturing execution Setup time and attendance for manufacturing execution processes Control production with manufacturing execution Create and process report as finished journals with co-products and by-products Calculate and approve raw time registrations Report as finished from the job card device Get started with Asset Management for Dynamics 365 Supply Chain Management Asset Management terminology and concepts Functional locations and assets Assets and work orders Install the Asset Management mobile workspace Use the Asset Management mobile workspace Asset Management integration capabilities Integrate Asset Management with Dynamics 365 Guides Schedule work orders in Asset Management for Dynamics 365 Supply Chain Management Configure workers for work order scheduling Schedule and dispatch work orders Calculate capacity load on scheduled work orders Configure and perform the procure-to-purchase process in Dynamics 365 Supply Chain Management Procurement scenario Overview of the procure-to-purchase process Set up procurement categories Use procurement catalogs Create a purchase requisition Create and process a request for quotation Create purchase orders Vendor categories and catalogs Evaluate a vendor and put a vendor on hold Use purchasing policies Configure activity-based subcontracting and production flow costing in Dynamics 365 Supply Chain Management Subcontracting capabilities Subcontracting a vendor Subcontracting service Transfer activities as subcontracting activities Subcontracting as an alternate resource Cost accounting of subcontracted services Subcontracting cost flow Backflush costing Products and material in Work in Progress Standard cost Costing lean manufacturing Calculation of standard cost Calculate unused quantities Calculation of production variances to standard cost Ledger entries created from processing a production flow Configure and use agreements in Dynamics 365 Supply Chain Management Work with trade agreements Create sales agreements Create purchase agreements Configure trade allowance management Configure brokerage contract management Configure royalty contract management Configure vendor rebates Rebate management module Work with capa Additional course details: Nexus Humans MB-335T00: Microsoft Dynamics 365 Supply Chain Management, Expert 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 MB-335T00: Microsoft Dynamics 365 Supply Chain Management, Expert 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 2 Days 12 CPD hours This course is intended for Executives, Project Managers, Business Analysts, Business and IT stakeholders working with analysts, Quality and process engineers, technicians, corrective action coordinators or managers; supervisors, team leaders, and process operators; anyone who wants to improve their ability to solve recurring problems. Overview At the completion of this course, you should be able to: Identify the different types of tools and techniques available Apply change management successfully Review what to look for when applying business case thinking to Root Cause Analysis Develop a process to systematically approach problems Business success is dependent on effective resolution of the problems that present themselves every day. Often the same or similar problems continue causing repeated losses in time or money and your staff become experts at fixing rather than preventing the problems. Learn to find and fix root causes and develop corrective actions that will effectively eliminate or control these problems. Section 1: RCA Foundation Concepts and Objectives Section Learning Objectives Discuss Definitions ? IT Perspective Discuss What is a problem and why do they exist? What is Root Cause? RCA Benefits and Approaches Event and Casual Analysis Event and Causal Analysis: Exercise 1c Worksheet RCA Tools for each approach Section Summary and Conclusions Section 2: Enhance use of RCA tools Why use a Particular Method Tool: Change Analysis Change Analysis Examples Tool: How to Resolve Conflict Tool: 5 Why?s Example Learning Management Problem Tool: Cause and Effect Tool: Fault Tree Analysis Why do we use Fault Tree Analysis? How does it work? Fault Tree Diagram Symbols Example #1 of FTA: Car Hits Object Tool: Failure Modes and Effects Analysis (FMEA) Example: Failure Modes and Effects Analysis Tool: Design / Application Review Section 3: Problem Resolution and Prevention Section Objectives The Secret of Solving Problems: -A Note about Statistical Control -A Note about Fire Fighting Technique: Business Process Mapping Example: IGOE Technique: Lean Six Sigma and DMAIC Lean Six Sigma Benefits Importance of Understanding the Business Process The Business Process Mandate Technique: Process Modeling Graphical Notation Standard (BPMN): -What is Business Process Modeling Notation (BPMN)? -Benefits of BPMN -Basic Components of BPMN Technique: Business Process Maturity Model Five Levels of Maturity Section 4: Capability Improvement for RCA Steps in Disciplined Problem Solving RCA as a RCA Process Key RCA Role Considerations Sustainable RCA Improvement Organizational Units Process Area Goals, Practices Specific and General Practices Specific Practice Examples Software Maturity Survey SWOT Analysis Worksheet Recognize the importance of the Change Management component in your RCA implementation Using the ADKAR Model to Communicate Change Review ADKAR© Model ? -Awareness of the need for change -Desire to participate and support the change -Knowledge on how to change -Ability to implement required skills and behaviors -Reinforcement to sustain the change The ADKAR Model: Reinforcement Section 5: Course Summary and Conclusions Plan the Proposal and Business Case Example: 1 Page Business Case Resource Guide Questions

Duration 5 Days 30 CPD hours This course is intended for The primary audience for this course are Application Consultants, Business Process Architects, Business Process Owners/Team Leads/Power Users, Data Consultants/Managers, and Solution Architects. Overview Students will set up and deliver their own master data from SAP ERP to SAP SCM (APO), and make any necessary master data enhancements to ensure proper planning results in APO.Students will complete the modeling of their supply chain by creating APO master data that is necessary to activate a fully functional Supply Chain in APO. In this course, students learn how to set up and configure the standard interface between the SAP ERP system and SAP SCM with focus on SAP APO. Integration for Supply Chain Modeling Integrating SAP ERP and SAP SCM Configuring an Integration Model Using Monitoring and Error-Processing Tools Setting Up Incremental Data Transfers for Master Data Changes Organizing Integration Models Performing Routine Operations with Background Processing Supply Chain Locations Managing Locations Integrating Plant Data Integrating MRP Areas Managing Transportation Zones Integrating Customers as Locations Integrating Vendors as Locations Integrating Factory Calendars and Time Streams Supply Chain Products Integrating Products Maintaining Product Data External Procurements Relationships Integrating Purchasing Information Records Integrating Scheduling Agreements Network Modeling Creating Means of Transportation Creating Transportation Lanes Supply Chain Resources Integrating Production Resources Creating Supply Chain Management (SCM)-Specific Resources Integrating Capacity Variants Integrating Setup Groups and Matrices Manufacturing Process Modeling Preparing Integration of Master Data Mapping Bill of Materials (BOM) Fields Mapping the Routings Fields Integrating Production Master Data Transferring a Master Recipe to a Production Process Model (PPM) Transferring Characteristics and Classes Quota Arrangement Creating Quota Arrangements Supply Chain Modeling Creating an SAP liveCache Model Creating a Version in SAP SCM Using the Supply Chain Engineer (SCE) Transactional Data Integration Integrating Transactional Data Supply Chain Subcontracting Preparing Master Data for Subcontracting

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 3 Days 18 CPD hours This course is intended for Developers Administrators Overview Understand why Blockchain is needed and where Explore the major components of Blockchain Learn about Hyperledger Fabric v1.1 and the structure of the Hyperledger Architecture Lean the features of the Fabric model including chaincode, SDKs, Ledger, Security and Membership Services Perform comprehensive labs on writing chaincode Explore the architecture of Hyperledger Fabric v1.1 Understand and perform in depth labs on Bootstrapping the Network Gain a detailed understanding of the benefits, components and architecture of Hyperledger Composer Learn Hyperledger Explorer and Hyperledger Composer Playground Perform comprehensive labs to integrate/develop an application with Hyperledger Fabric running a smart contract Build applications on Hyperledger Fabric v1.1 This instructor-led Hyperledger training course is designed for developers and administrators who want to take a comprehensive deep dive on Hyperledger Fabric and Hyperledger Composer. This Hyperledger training course has several comprehensive labs, giving you real world experience.In 3 days, you will learn the need for blockchain applications, where blockchain is used, and about Hyperledger Fabric, the open source framework for developing blockchain applications and solutions with a modular architecture. Introduction to Blockchain Introduction to Blockchain What is Blockchain Types of network Public network Permissioned network Private network Need for Blockchain Components of Blockchain Consensus Provenance Immutability Finality Where can Blockchain be used Example on Blockchain How Blockchain Works How Blockchain Works Structure of Blockchain Block Hash Blockchain Distributed Lifecycle of Blockchain Smart Contract Consensus Algorithm Proof of Work Proof of Stake Practical Byzantine Fault Tolerance Actors of Blockchain Blockchain developer Blockchain operator Blockchain regulator Blockchain user Membership service provider Building A Small Blockchain Application Introduction to Hyperledger Fabric v1.1 Introduction to Hyperledger What is Hyperledger Why Hyperledger Where can Hyperledger be used Hyperledger Architecture Membership Blockchain Transaction Chaincode Hyperledger Fabric Features of Hyperledger Fabric Installation of prerequisite Getting Started With Fabric Model The Fabric Model Features of Fabric Model Chaincode SDKs Ledger Privacy through channels Security and Membership services Assets Consensus Components of Fabric Model Peer Orderer Certificate Authority Building your network Chaincode Chaincode Chaincode API How to write a Chaincode Lab Work Architecture of Hyperledger Fabric v1.1 Architecture of Hyperledger Fabric Transaction Ledger Nodes Peer Endorser Ordering Nodes Channels Certificate Authority Transaction Flow Lab Work Bootstrapping Bootstrapping the Network Introduction Lab Work Task 1 - Generate the crypto material for the various participants. Task 2 - Generate the genesis block for the Orderer node and start ordering service (solo node). Task 3 - Generated the configuration transaction block to create a new channel. Task 4 - Sign the configuration block and create the new channel. Task 5 - Make peers of all the organizations join the channel that we created in Task 4 Introdcution to Hyperledger Explorer Introduction To Hyperledger Explorer Block Details Peer List Chaincode List Transaction Details Installation of Hyperledger Explorer Starting the Explorer App Introduction to Hyperledger Composer Introduction Components of Hyperledger Composer Benefits of Hyperledger Composer Key Concepts Hyperledger Composer Solution Installation Hyperledger Composer Playground Hyperledger Composer Playground Introduction Playground Overview Lab Work Additional course details: Nexus Humans Hyperledger Training - Developing on Hyperledger Fabric 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 Hyperledger Training - Developing on Hyperledger Fabric 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 3 Days 18 CPD hours This course is intended for Data Science for Marketing Analytics is designed for developers and marketing analysts looking to use new, more sophisticated tools in their marketing analytics efforts. It'll help if you have prior experience of coding in Python and knowledge of high school level mathematics. Some experience with databases, Excel, statistics, or Tableau is useful but not necessary. Overview By the end of this course, you will be able to build your own marketing reporting and interactive dashboard solutions. The course starts by teaching you how to use Python libraries, such as pandas and Matplotlib, to read data from Python, manipulate it, and create plots, using both categorical and continuous variables. Then, you'll learn how to segment a population into groups and use different clustering techniques to evaluate customer segmentation.As you make your way through the course, you'll explore ways to evaluate and select the best segmentation approach, and go on to create a linear regression model on customer value data to predict lifetime value. In the concluding sections, you'll gain an understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification algorithms. Finally, you'll apply these techniques to create a churn model for modeling customer product choices. Data Preparation and Cleaning Data Models and Structured Data pandas Data Manipulation Data Exploration and Visualization Identifying the Right Attributes Generating Targeted Insights Visualizing Data Unsupervised Learning: Customer Segmentation Customer Segmentation Methods Similarity and Data Standardization k-means Clustering Choosing the Best Segmentation Approach Choosing the Number of Clusters Different Methods of Clustering Evaluating Clustering Predicting Customer Revenue Using Linear Regression Understanding Regression Feature Engineering for Regression Performing and Interpreting Linear Regression Other Regression Techniques and Tools for Evaluation Evaluating the Accuracy of a Regression Model Using Regularization for Feature Selection Tree-Based Regression Models Supervised Learning: Predicting Customer Churn Classification Problems Understanding Logistic Regression Creating a Data Science Pipeline Fine-Tuning Classification Algorithms Support Vector Machine Decision Trees Random Forest Preprocessing Data for Machine Learning Models Model Evaluation Performance Metrics Modeling Customer Choice Understanding Multiclass Classification Class Imbalanced Data Additional course details: Nexus Humans Data Science for Marketing Analytics 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 Data Science for Marketing Analytics 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.