Data Modeling and Metadata
Online Education Certification Enterprise Solutions
Metadata Management Fundamentals Logical Data Modeling DW and BI Data Modeling Data Profiling Data Quality Assessment Conceptual Data Modeling The Data Model Scorecard Data Parsing, Matching, and De-Duplication Best Practices in Data Resource Management Curating and Cataloging Data Introduction to Graph Databases
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TABLE OF CONTENTS
OVERVIEW 2 CURRICULA AT-A-GLANCE 3 CERTIFICATION PROGRAMS 4
ENTERPRISE SOLUTIONS 5-6 COURSE DESCRIPTIONS 7-17 OUR INSTRUCTORS 18-19
OUR CUSTOMERS 20 CONTACT US 21 PRICE LIST 22 ABOUT ELEARNINGCURVE 22
I learned so much out of the courses that I wanted to continue my learning even beyond the basic requirement of my current job.
-- Jagmeet Singh, CIMP Ex - Data Modeling & Metadata Management, Data Quality, Data Governance, IM Foundations, MDM USA
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Full course descriptions begin on page 7.
Metadata Management Fundamentals The Data Model Scorecard
This 4-hour course is designed to provide the We often build data models quickly and with the foundational metadata knowledge needed by singular goal of database design. This 3-hour anyone who has data management roles and course presents Data Model Scorecard®, which responsibilities. provides the tools needed to measure and manage data model quality. Logical Data Modeling Data Parsing, Matching, and De-duplication This 4.5-hour course covers the concepts, notation, and steps needed to create and extend logical data To take advantage of the worldwide marketplace, models. The course goes beyond fundamentals, businesses need to manage data globally. This and describes numerous data modeling situations reality poses very specialized and unique kinds of and patterns. problems in data management. In this 3-hour course you will learn to identify and avoid the BI & DW Data Modeling pitfalls of global information.
This 4-hour course includes a mix of data modeling Best Practices in Data Resource concepts, best practices, applications and practical Management examples that will help you build effective data warehouse and business intelligence applications. The data is one of the four critical resources in an organization, along with the financial resource, real Data Profiling property, and the human resource. This 4.5-hour course provides an in-depth analysis of the impact Data profiling is the process of analyzing actual of data disparity on the organization, and outlines data and understanding its true structure and best practices for data resource management. meaning. It is one of the most common and important activities in information management. Curating and Cataloging Date This 5-hour course teaches all practical skills necessary to succeed in a data profiling initiative. This 3-hour online training course will explore how curating and cataloging work together to meet the Data Quality Assessment data needs of business and data analysts, to provide self-service data to complement self- This 6-hour course gives comprehensive treatment service analytics, and to realize the promise of to the process and challenges of data quality democratizing data analytics. assessment. It starts with systematic treatment of various data quality rules and proceeds to building Introduction to Graph Databases aggregated data quality scorecard. This 4-hour online training course will provide an Conceptual Data Modeling overview of property graph database technology and teach the student how to translate business In this 3.5-hour course you will learn the secrets of requirements to a property graph database design successful conceptual data modeling through an that can be implemented on any modern property effective mix of presentation and exercises. You will graph database. gain valuable insights into the job and responsibilities of the data modeler.
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CIMP: Demonstrate Mastery. Achieve Success.
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Course Outline Metadata Management Fundamentals
Deriving value from data depends extensively on o Views of Data understanding the data and sharing knowledge among o Projects Flow everyone who works with data. Sharing data knowledge o Describing the Data Meaning is the core purpose of metadata. Just as you need o Describing the Data Constraints financial data to manage financial resources, you need o Describing the Data Relationships metadata to manage data resources. In today’s data- o Describing the Data driven world, the importance of managing data is certainly on par with that of managing finances. o Metadata This online training course is designed to provide the o Metadata Management Processes foundational metadata knowledge needed by anyone o Using Metadata who has data management roles and responsibilities. It o Metadata Tools and Technologies covers metadata basics such as the types and purposes of metadata, and explores core metadata disciplines of data modeling, data profiling, and data cataloging. o Data Modeling Defined Metadata roles in data governance, stewardship, o The Data Modeling Process security, quality, and analysis are explained. o Supplemental Models & Additional E-R
Concepts : o Dimensional Data Modeling The scope and complexities of metadata management The roles of data models as metadata and the basics of data modeling o What is Data Profiling? The role of data profiling in metadata o Myth and Reality of Data Profiling management and the basics of data profiling o Profiling Techniques methods o Profiling Challenges The roles of data catalogs in metadata o Role of Profiling management and the fundamentals of data o People and Technology curation and data cataloging Metadata dependencies of business processes, IT projects, data governance, data quality, o Data Curation business intelligence, self-service data, business o Data Cataloging analytics, and data science o Metadata and the Catalog : Anyone with data management roles and responsibilities Data stewards and data governance o The Metadata Muddle practitioners and participants o Data Science and Metadata Data curators and data catalog administrators o Data Provenance and Data Lineage Data and database analysts and designers o Ontology and Taxonomy Data quality professionals and practitioners Aspiring data modelers who need to start with the basics Anyone with a role in information management who needs to understand data or help others to understand data
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Logical Data Modeling Course Outline
Logical Data Modeling also known as Entity/Relationship (E-R) Modeling is a key method for o Introduction to Data Modeling getting a handle on the data requirements of an o The Entity Relationship Model organization. Logical data models provide a database o Introduction to LDM Methodology independent solution to data requirements which then can be driven forward to become effective database ) designs. o Determine Scope and Purpose o Define Business Subject Areas This online training course covers the concepts and o Identify Business Functions notation of logical data modeling and shows the steps o Identify Data Requirements needed to create and extend logical data models.
Many exercises and examples are included to enhance learning. o Modeling Entities o Modeling Relationships In addition, the course goes beyond these fundamentals. The situation where a new data model must be created from scratch is one of many o Modeling Attributes situations, so this course shows how to handle other o Modeling Keys situations such as: building from industry or canonical data models; extending legacy data models; and extending software package data models. In addition, o Rationalizing the Model data model patterns will be introduced such as: history o Data Modeling Situations and audit modeling, multi-business unit modeling, o Data Modeling Deliverables codes and reference data, and user defined attributes. o In Conclusion Understanding these situations and patterns, is critical to success in data modeling.
How to create, extend and apply logical data models How to use data modeling to meet business and performance requirements How to lead your team through the data modeling process How to avoid data modeling traps, problems, and time wasters How to make databases more robust through data modeling How to effectively communicate data models and database designs to others
Anyone who will be using or creating data models. Business Analysts and Architects Database Administrators and Analysts Data Administrators & Data Modelers Information Technology Managers, Project Managers Application Development Project Team Members
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BI & DW Data Modeling Course Outline
A well designed data model is the cornerstone to building business intelligence and data warehouse o Data Modeling Overview applications that provide significant business value. o Entity-Relationship Modeling Overview o Normalization Effective data modeling results in transforming data into an enterprise information asset that is consistent, comprehensive and current. Data is transformed from o What is Dimensional Modeling? operational or source systems into a data warehouse o Facts and often data marts or OLAP cubes for analysis. This o Dimensions course provides the fundamental techniques to o Schemas designing the data warehouse, data marts or cubes o Entity-Relationship vs. Dimensional that enable business intelligence reporting and Modeling analytics. o Purpose of Dimensional Modeling o Fact Tables This online training course discusses the two logical o Dimensional Modeling Vocabulary data modeling approaches of Entity-Relationship (ER) and dimensional modeling. ER modeling is used to establish the baseline data model while dimensional o Hierarchies modeling is the cornerstone to Business Intelligence o Slowly Changing Dimensions (BI) and Data Warehousing (DW) applications. These o Rapidly Changing Dimensions modeling techniques have expanded and matured as o Casual Dimensions best practices have emerged from years of experience o Multi-Valued Dimensions in data modeling in enterprises of all sizes and o Snow flaking industries. These techniques improve the business o Junk Dimensions value of the data, enhance project productivity and o Value Brand Reporting reduce the time to develop applications. This course o Heterogeneous Products includes a mix of concepts, applications and practical o How Swappable Dimensions examples. o Too Few or Too Many Dimensions o Benefits of Dimensional Modeling : The basics of Entity-Relationship (ER) and
dimensional modeling the benefits and applicability of Dimensional Data Modeling how to create Dimensional Data Models for BI and DW applications how to learn more about Data Modeling
: Beginning Data Modelers Business Analysts and Architects Database Administrators and Analyst IT Managers, Project Managers Application Development Project Team Members People involved in design and maintenance of Data Warehousing and Business Intelligence applications People involved in data quality or data governance processes
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Data Profiling Course Outline
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Data profiling is the process of analyzing actual data and understanding its true structure and meaning. It is o What is Data Profiling? one of the most common and important activities in o Myth and Reality of Data Profiling information management. Data profiling is the first o Profiling Techniques critical step in many major IT initiatives, including o Profiling Challenges implementing a data warehouse, building an MDM hub, o Role of Profiling populating metadata repository, as well as operational o People and Technology data migration and integration. It is also the key ingredient to successful data quality management. o Introduction While proliferation of commercial tools made data o Basic Counts profiling accessible for most information management o Value Frequency Charts professionals, successful profiling projects remain o Value Distribution Characteristics elusive. This is largely because the tools allow o Value Distribution gathering large volumes of information about data, but offer limited means and guidelines for analysis of that information. o Introduction o Timeline Profiling In this online training course you will learn all practical o Timestamp Pattern Profiling skills necessary to succeed in a data profiling initiative. o Multi-Dimensional Profiling o Event Dependency Profiling
The what, why, when, and how of data profiling Various data profiling techniques, from simple o Introduction column profiling to advanced profiling methods o Data Structures for State-Dependent Data for time-dependent and state-dependent data o Profiling Techniques How to efficiently gather data profiles How to analyze the data profiling information and ask the right questions about your data o Subject Profiling How to organize data profiling results o Relational Integrity Profiling How to perform dynamic data profiling and o Attribute Dependency Profiling identify changes in data structure and meaning o Dynamic Data Profiling
Data quality practitioners MDM practitioners Metadata management practitioners IT and business analysts involved in data management Those responsible for implementation and maintenance of various data management systems
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Data Quality Assessment Course Outline
More and more companies initiate data quality programs and form data stewardship groups every o Why Assess Data Quality year. The starting point for any such program must be o Business Value of Data Quality data quality assessment. Yet in absence of a Assessment comprehensive methodology, measuring data quality o Types of Data Errors remains an elusive concept. It proves to be easier to o Data Quality Assessment Approaches produce hundreds or thousands of data error reports o How Rule-Driven Approach Works than to make any sense of them. o Project Planning o Project Steps This online training course gives comprehensive treatment to the process and practical challenges of data quality assessment. It starts with systematic o Attribute Domain Constraints treatment of various data quality rules and proceeds to o Relational Integrity Constraints the results analysis and building aggregated data o Complex Data Relationships quality scorecard. Special attention is paid to the architecture and functionality of the data quality metadata warehouse. o Historical Data Overview o Timeline Constraints o Value Pattern Rules The what, why, when, and how of data quality o Rules for Event Histories assessment o Rules for State-Dependent Objects How to identify and use data quality rules for assessment How to ensure completeness of data quality assessment o Discovering Data Quality Rules How to construct and use a data quality o Implementing Data Quality Rules scorecard o Building Rule Catalog How to collect, manage, maintain, warehouse o Building Error Catalog and use data quality metadata o Fine-Tuning Data Quality Rules
Data quality practitioners o School Report Card Example Data stewards o A First Look at DQ Scorecard IT and business analysts and everyone else o Defining Aggregate Scores involved in data quality management o Score Tabulation
o Basic Scorecard Example o Recurrent Data Quality Assessment o Database and Enterprise-Wide DQ Scorecard
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Conceptual Data Modeling Course Outline
Conceptual Data Modeling using the UML standard is a key method for getting a handle on the data o Introduction to Conceptual Data Modeling requirements of an organization. Effective conceptual o UML - Unified Modeling Language data modeling results in maximum benefits from o Domain Modeling information assets by increasing shared use and o Class Modeling avoiding redundancy. Data that is relevant, timely, o Conceptual Data Modeling Basic consistent, and accessible has increased value to the Methodology organization.
This online training course teaches conceptual data modeling from A to Z and includes an effective mix of o Phase 1: Understanding the Business presentation and exercises. o Phase 2: Modeling Domains
Terminology, goals, and components of conceptual data modeling o Phase 3: Modeling Classes How to benefit from conceptual data modeling o Phase 4: Modeling Associations How to create conceptual data models, including Domain Models and Class Models
This course is geared towards: o Phase 5: Modeling Properties Business Analysts and Architects o Creating Deliverables Database Administrators and Analysts o In Conclusion Data Administrators and Data Modelers
Information Technology Managers, Project Managers Application Development Project Team Members
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The Data Model Scorecard Course Outline
A frequently overlooked aspect of data quality management is that of data model quality. We often o Why Measure Data Model Quality build data models quickly, in the midst of a o Traditional Review Methods development project, and with the singular goal of o Archer vs. Data Modeler database design. Yet the implications of those models o Enter the Scorecard are far-reaching and long-lasting. They affect the structure of implemented data, the ability to adapt to change, understanding of and communication about o Category 1 - Model Type data, definition of data quality rules, and much more. In o Category 2 - Correctness many ways, high-quality data begins with high-quality o Category 3 - Completeness data models. o Category 4 - Structure o Category 5 - Abstraction This online training course presents Steve Hoberman's o Category 6 - Standards Data Model Scorecard®, which provides the tools o Category 7 - Readability needed to measure and manage data model quality. o Category 8 - Definitions o Category 9 - Consistency o Category 10 - Data The importance of having an objective measure of data model quality The categories that make up the scorecard o Introducing the Scorecard into your including correctness, completeness, structural Organization soundness, flexibility, standards, and model o Scorecard Challenges consistency o Scorecard Tips How to apply the scorecard to different types of o Applying the Scorecard models Techniques to strengthen data models, including model reviews, model substitutes (screens, prototypes, sentences, spreadsheets and reports), and the use of automated tools to enforce modeling best practices and standards How to introduce the scorecard into a development methodology and your company culture
: Data Modelers Data Analysts Data Architects Data Stewards Database Administrators
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Data Parsing, Matching and De- : duplication Master data management professionals Data quality professionals Information architects Developers of data warehousing systems Business professionals who work with global data Data parsing, standardization, matching, and de- duplication are the cornerstones of successful Master Data Management (MDM). They are also critical parts Course Outline of successful data quality programs, and are key steps in building data warehouses as well as any data integration and consolidation initiatives. You could say that today few organizations can function effectively without implementing data parsing and matching processes often in many data domains. o Parsing and Standardization This need is further magnified if your company has o Introduction to Data Matching gone global and plans to create databases that o Data Matching Techniques combine name- and address-related data from all o Data Matching Destinations corners of the world. Managing global information o Evaluating Data Matching Tools effectively takes specialist knowledge and the ability to show consideration for the differences that exist throughout the world. Worldwide there are more than o External Data Sources 10,000 languages, 130 address formats, 36 personal o Syndicated Customer Data and hundreds of business name formats. All of these o Syndicated Product Data variables are further complicated by the need to o Using the Web respect national and regional cultures. Failure to o consider formats, styles, and cultures has huge impact on quality of data and quality of business relationships. o Introduction to Global Information o Global Data: What You Need to Know This online training course is aimed at data quality and o Variations by Country and Region master data management (MDM) professionals as well o Cultural and Legal Impacts as those responsible to work with global information. o Characters and Diacritics The field is broad and the details are many. The purpose of this course is to provide a broad and in- depth review of data parsing, standardization, matching, and de-duplication techniques, as well as o Data Profiling extensive overview of specific problems and solutions o Consistent Data Structures when dealing with global data. o Preparing Global Data for Effective Use
Data parsing, standardization, matching, and de-duplication techniques How to find and use external reference data How data parsing and matching contribute to improving data quality, MDM, and data warehousing Which data domains, entities and data elements may benefit from data parsing and matching Challenges of global data and ways to overcome these challenges
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Best Practices in Data Resource Course Outline Management
Data is one of the four critical resources in an o Current Situation organization, equivalent with the financial resource, o Halting Data Disparity real property, and the human resource. Yet most organizations fail to manage the data with the same priority, discipline, and attention that is applied to the o Data Names other critical resource. The time for disciplined o Data Definitions management of the data resource is long overdue. o Data Structures o Data Integrity Rules Most public and private sector organizations face many o Data Documentation challenges with burgeoning quantities of disparate data. These disparate data are not well understood, have high redundancy, are not consistent, have low o Data Orientation quality, and fail to adequately support the o Data Availability organization’s business information demand. The only o Data Responsibility way to resolve this situation is to thoroughly o Data Vision understand how and why disparate data are created, o Data Recognition and how those problems can be resolved.
This online training course begins with common definitions of data disparity and its impact on the organization, and proceeds to describe 10 sets of bad habits and good practices related to the architecture and governance components of data resource management.
How to define and identify disparate data. How to identify the impact of disparate data on the business. How to define, identify, and manage data resource quality. The common problems with the architecture and governance of the data resource. The best practices to solve these architecture and governance problems.
Anyone who has responsibility for the architecture or governance of the data resource. Data resource quality practitioners at all levels. Business executives and managers who struggle with the business impacts of poor quality data. IT managers who are challenged to deliver reliable and trusted data to support the business information demand. Data and information system architects who need to break the cycle of disparate data creation.
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Curating and Cataloging Data Course Outline
As the world of data management grows and changes, the roles and participants in data ecosystems must o Governance and Self-Service adapt. With the convergence of several influences – o How We Got Here big data, self-service analytics, and self-service data o Why Self-Service Data? preparation – we need to actively manage the inventory of self-service data. Data curation is both a data inventory management process and a data o Data Curation governance activity. The data curator is responsible to o Why Data Curation? oversee a collection of data assets and make it o The Data Curator available to and findable by anyone who needs data. o Data Lifecycles and Curation Cataloging maintains the collection of metadata that is o Curating Big Data necessary to support browsing, searching, evaluating, o Getting Started with Data Curation accessing, and securing datasets.
This 3-hour online training course will explore how o Definitions curating and cataloging work together to meet the data o Why Data Cataloging? needs of business and data analysts, to provide self- o Metadata and the Catalog service data to complement self-service analytics, and o Data Catalog Tools to realize the promise of democratizing data analytics. o Getting Started with Data Cataloging
How to define and identify disparate data. he concepts, responsibilities, and skills of data curation o The Business Case for a Data Catalog The role of the data curator in data governance o Kinds of Data Catalogs and the differences between a data curator and o Evaluation Criteria a data steward The needs of data seekers and the ways that curating and cataloging help to meet o Information, Technology, and Data The purpose, content, and uses of a data o The Enterprise Data Marketplace catalog o EDM Architecture The state of data cataloging tools and o EDM Data Services technology o Data as a Service Guidelines for getting started with data curating and cataloging
Business and IT leaders struggling with the paradoxes of modern data management Analytics and BI designers and developers who are dependent on fresh and relevant data for every analytics use case Data management professionals at all levels from architects to engineers Data governance professionals – especially data stewards who need to adapt to the changing world of modern data management
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Introduction to Graph Databases Course Outline
Entity Relationship modeling and relational databases have dominated the IT scene since the '80s, becoming the de facto standard approach for data persistence. o Graph Theory However, the ubiquitous relational database has waned o Anatomy of a Node with the advent of NoSQL and big data technologies. o Anatomy of a Relationship Today’s data architect must master a new database o Properties and Paths technology – graph database – that has emerged with a solid set of use cases based on mathematical graph theory and graph algorithms.
This online course will provide an overview of property o Graph Database graph database technology and teach the student how o Graph Analytics to translate business requirements to a property graph o Semantic Web Graph Database database design that can be implemented on any modern property graph database.
o Graph Data Modeling Overview o Agile Graph Data Modeling The fundamental concepts and practices of o Graph Data Modeling Process Business Intelligence o Working with Users The fundamental concepts and practices of Business Analytics Concepts and principles of Predictive Analytics Concepts and principles of Data Mining o Representing Things Concepts and principles of Data Science o Describing Things The roles of data analytics in a data-driven o Categorizing Things Part 1 & 2 organization o Entity Definition Best/Worst Practices
Data Architects that need to understand how o Representing Connections graph database fits into the overall persistence o Naming Relationships architecture o Relationship Direction DBA’s and Data modelers expanding into graph o Describing Relationships database o Relationship Best/Worst Practices Data Science and Data analysts interacting with graph databases Big Data Managers and decision makers o Complex Object Modeling o Resolving Hypergraphs Part 1 & 2 o Complex Objects o Data Structures-Linked Lists and Trees o Managing Slowly Changing Dimensions
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OUR INSTRUCTORS
Mike Brackett David Haertzen Mike Brackett has been in the data management David Haertzen is chief instructor for First Place, field for over 40 years, during which he is webmaster of Infogoal.com, and has over 20 developed many concepts and techniques for years of experience in the Information designing applications and managing data Technology field. Working with a wide range of resources. He is the originator of the common businesses and government agencies has given data architecture concept, the data resource David insights into the practical application of framework, the data naming taxonomy, the five- data modeling in many environments. He has tier five-schema concept, the data rule concept, developed models covering subject areas such the BI value chain, the data resource data as: human resources, assets, customers, concept, and the architecture-driven data model employees, products, services, organizations, concept, and new techniques for understanding locations, orders, inventory, and processes. and integrating disparate data.
Steve Hoberman Kathy Hunter Steve Hoberman is a trainer, consultant, and Kathy always says she has data in her blood. writer in the field of data modeling. Steve is a Joining Harte-Hanks in 2002, she built an columnist and frequent contributor to industry information management practice and, with her publications. He is the author of several data highly skilled team, was responsible for modeling books including Data Modeling Made instituting their highly successful Global Data Simple, Data Modeler’s Workbench, and Data Management solution set. From information Modeling for the Business. Steve is an innovator quality and data governance to providing global in data modeling and the inventor of the Data data solutions and guidance she attained a Model Scorecard®, which has quickly become reputation for expert knowledge and successful the standard for data model quality delivery in global information management t. .
William McKnight Arkady Maydanchik William is president of McKnight Consulting For more than 20 years, Arkady Maydanchik has Group. He is a Southwest Entrepreneur of the been a recognized leader and innovator in the Year Finalist, a frequent best practices judge, has fields of data quality and information integration. authored hundreds of articles and white papers As a practitioner, author and educator he has and given hundreds of international keynotes and been involved in some of the most challenging public seminars. William is a former Information projects industry has seen. These projects were Technology Vice President of a Fortune 50 often the result of major corporate mergers and company, a former engineer of DB2 at IBM and the need to consolidate and integrate databases holds an MBA from Santa Clara University. of enormous variety and complexity.
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Rick Sherman Henrik Sørensen Rick is the founder of Athena IT Solutions, a Henrik Liliendahl Sørensen has over 30 years of Boston area business intelligence and data experience in working with Master Data warehousing consulting firm that provides Management and Data Quality and is a charter solutions for customers of all sizes and member of the International Association of industries. His hands-on experience includes a Information and Data Quality. Currently Henrik wide range of data integration tools. Rick also works with Master Data Management at Tata teaches data warehousing, data integration Consulting Services and as Practice Manager at and business intelligence for a masters’ Omikron Data Quality besides writing on a well degree program at Northeastern University’s trafficked blog about data quality, master data graduate school of engineering. management and the art of data matching.
John Singer Dave Wells John Singer has 4 decades of experience in a Dave Wells is a consultant, teacher, and variety of data architecture–related roles. practitioner in the field of information management. John’s past accomplishments include He brings to every endeavor a unique and implementing metadata management balanced perspective about the relationships of solutions, data modeling processes and business and technology. This perspective governance, master data management ─refined through a career of more than thirty-five solutions, and an ITIL-based CMDB combining years that encompassed both business and architecture, business, and IT metadata in a technical roles─ helps to align business and comprehensive solution. John is currently information technology in the most effective ways. focusing on property graph technologies and is Dave is a frequent contributor to trade publications the Founder and CEO of NodeEra Software, and is a co-author of the book BI Strategy: How to makers of the NodeEra family of Property Create and Document. He also speaks at a variety Graph Data Modeling tools for Neo4j. of industry events.
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WHAT OUR CUSTOMERS ARE SAYING…
The courses are well laid out, build on each other, and are rich in practical content and advice.
-- Steve Lutter, CIMP Data Quality, DM and Metadata, IM Foundations, Business Intelligence, Data Governance, MDM, United States
It is evident that a thorough and considerable effort has gone into the preparation of this program.
-- Alfredo Parga O’Sullivan, CIMP Ex Data Quality, Ireland
The ability to take the courses at my own pace and at a time suitable for me was of great help.
-- Geeta Jegamathi, CIMP Data Quality, India
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CONTACT US
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DATA MODELING & METADATA COURSE PRICING
Packages
CIMP Data Modeling & Metadata Package……………. $1,895.00 CIMP Ex Data Modeling & Metadata Package………… $2,795.00 CIMP All Course Access License $4,995.00 CIMP All Course Access License w/ Exam Package $5,990.00
Individual Courses
Metadata Management Fundamentals…………………. $385.00 Conceptual Data Modeling………………………………. $335.00 Logical Data Modeling…………………………………….. $430.00 DW and BI Data Modeling……………………………….. $420.00 The Data Model Scorecard………………………………. $285.00 Data Profiling………………………………………………. $520.00 Data Parsing, Matching & De-duplication $435.00 Data Quality Assessment ………………………………... $510.00 Best Practices in Data Resource Management……….. $305.00 Curating and Cataloging Data……………………………. $290.00 Introduction to Graph Databases………………………… $435.00
Exams
Exam for each course…...... $80.00
Enterprise Discounts
We offer discounts to Enterprise customers who purchase in bulk. Please contact usAbout for more eLearningCurve information. eLearningCurve offers comprehensive online education programs in various disciplines of information management. With eLearningCurve, you can take the courses you need when you need them from any place at any time. Study at your own pace, listen to the material many times, and test your knowledge through online exams to ensure maximum information comprehension and retention. eLearningCurve also offers two robust certification programs: CIMP & CDS. Certified Information Management Professional (CIMP) builds upon education to certify knowledge and understanding of information management. Certified Data Steward (CDS) is a role-based certification designed for the fast growing data stewardship profession.
Finally, eLearningCurve’s Enterprise Program is a flexible, scalable, cost- effective solution for teams and enterprises.
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