50 Data Quality courses

My popular 'Getting Started in Data Governance' course is making it's way across the pond!

Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning

Data-Informed Decision Making in Projects: On-Demand Project management professionals constantly need to make project decisions that could be decisive for the outcome of their projects but often do not have sufficient information available to confidently make decisions. As a result, projects are increasingly falling short of delivering on their promises, requiring, more than ever, a data-informed approach to decision-making in the area of project delivery and management. The rapid growth of data comes with various challenges though, which consequently needs consideration of various critical factors for a successful implementation of a data-informed decision-making process in organizations and projects. What You Will Learn At the end of this program, you will be able to: Describe and understand the relevant methods and techniques to identify, acquire, and analyze relevant data points for decision making in projects Articulate analytical questions to focus on the real problems Identify potential shortfalls and gaps in project decision-making and apply actions to mitigate them Introduction to Data-Informed Decision Making The different types of decisions in projects Data-informed decision-making framework Shortcomings with traditional decision-making models Understanding the value of data for project delivery Issues in project management and how data can help solve them The DIKW Pyramid (Data, information, knowledge, wisdom) Types of data in projects Applying Data Analytics Understanding Data Analytics Levels of Data Analytics Data-Informed vs. Data-Driven Challenges and How to Address Them Project data availability and collection Data quality Behavioral blockers and bias Skills and Techniques Data literacy and data fluency Communicating for informed decision-making Monitoring and evaluating project decisions Implementing Data-Informed Decision Making Decision-making strategy and governance Project data culture Continuously improving decision quality Future Outlook for Decision-Making in Projects Data and AI Digital Decisioning

About this Virtual Instructor Led Training (VILT) The energy industry has started its journey to be more data centric by embracing the industry 4.0 concept. As a result, data management - which was considered until recently as a back-office service to support geoscience, reservoir management, engineering, production and maintenance - is now given the spotlight! To become an active stakeholder in this important transition in E&P data management, it is necessary to understand the new technical opportunities offered by the Cloud, Artificial Intelligence and how data governance can pave the way towards more reliable and resilient processes within E&P domain. Several key questions that need to be addressed: Why place more focus on data assets? Is data management just about serving geoscientists or engineers with fresh data? What is the value of data management in the E&P sector for decision making? How to convince the data consumers that the data we provide is reliable? Is the data architecture of my organization appropriate and sustainable? The purpose of this 5 half-day Virtual Instructor Led Training (VILT) course is to present the data challenges facing the energy organizations today and see how they practically solve them. The backbone of this course is based on the DAMA Book of Knowledge for Data Management. The main data management activities are described in sequence with a particular focus on recent technological developments. Training Objectives Upon completion of this VILT course, the participants will be able to: Understand why the data asset is now considered as a main asset by energy organizations Appreciate the importance of data governance and become an active stakeholder of it Understand the architecture and implementation of data structure in their professional environment Get familiarized with the more important data management activities such as data security and data quality Integrate their subsurface and surface engineering skills with the data managements concepts This VILT course is unique on several points: All notions are explained by some short presentations. For each of them, a set of video, exercises, quizzes will be provided to help develop an engaging experience between the trainer and the participants A pre-course questionnaire to help the trainer focus on the participants' needs and learning objectives A detailed reference manual A lexicon of terms for data-management Limited class size to encourage the interactivity Target Audience This VILT course is intended for: Junior/new data managers Geoscientists Reservoir engineers Producers Maintenance specialists Construction specialists Human resources Legal Course Level Basic or Foundation Training Methods The VILT course will be delivered online in 5 half-days consisting 4 hours per day, with 2 breaks of 10 minutes per day. Course Duration: 5 half-day sessions, 4 hours per session (20 hours in total). Trainer Your expert course leader is a geologist by education who has dedicated his career to subsurface data management services. In 2016, he initiated a tech startup dedicated to Data Management using Artificial Intelligence (AI) tools. He is heavily involved in developing business plans, pricing strategies, partnerships, marketing and SEO, and is the co-author of several Machine Learning publications. He also delivers training on Data Management and Data Science to students and professionals. Based in France, he was formerly Vice President, Sales & Marketing at CGG where he was in charge of the Data Management Services strategy, Sales Manager at Spie O&G Services where he initiated the Geoscience technical assistance activities and Product Manager of interactive seismic inversion software design and marketing at Paradigm.       POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information about post training coaching support and fees applicable for this. Accreditions And Affliations

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Duration 4 Days 24 CPD hours This course is intended for Data Analysts responsible for data quality using QualityStageData Quality ArchitectsData Cleansing Developers Overview List the common data quality contaminantsDescribe each of the following processes: Investigation, Standardization, Match. & SurvivorshipDescribe QualityStage architectureDescribe QualityStage clients and their functionsImport metadataBuild and run DataStage/QualityStage jobs, review resultsBuild Investigate jobsUse Character Discrete, Concatenate, and Word Investigations to analyze data fieldsDescribe the Standardize stageIdentify Rule SetsBuild jobs using the Standardize stageInterpret standardization resultsInvestigate unhandled data and patternsBuild a QualityStage job to identify matching recordsApply multiple Match passes to increase efficiencyInterpret and improve match resultsBuild a QualityStage Survive job that will consolidate matched records into a single master recordBuild a single job to match data using a Two-Source match This course teaches how to build QualityStage parallel jobs that investigate, standardize, match, and consolidate data records. Students will gain experience by building an application that combines customer data from three source systems. Data Quality Issues Listing the common data quality contaminants Describing data quality processes QualityStage Overview Describing QualityStage architecture Describing QualityStage clients and their functions Developing with QualityStage Importing metadata Building DataStage/QualityStage Jobs Running jobs Reviewing results Investigate Building Investigate jobs Using Character Discrete, Concatenate, and Word Investigations to analyze data fields Reviewing results Standardize Describing the Standardize stage Identifying Rule Sets Building jobs using the Standardize stage Interpreting standardize results Investigating unhandled data and patterns Match Building a QualityStage job to identify matching records Applying multiple Match passes to increase efficiency Interpreting and improving Match results Survive Building a QualityStage survive job that will consolidate matched records into a single master record Two-Source Match Building a QualityStage job to match data using a reference match Additional course details: Nexus Humans KM213 IBM InfoSphere QualityStage Essentials v11.5 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 KM213 IBM InfoSphere QualityStage Essentials v11.5 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.

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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

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