The top 14 Data Quality courses you need to take

1. Total Data Quality

This specialization aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality.\n\nThis specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis...

2. Measuring Total Data Quality

By the end of this second course in the Total Data Quality Specialization, learners will be able to: 1. Learn various metrics for evaluating Total Data Quality (TDQ) at each stage of the TDQ framework. 2. Create a quality concept map that tracks relevant aspects of TDQ from a particular application or data source. 3. Think through relative trade-offs between quality aspects, relative costs and practical constraints imposed by a particular project or study. 4. Identify relevant software and related tools for computing the various metrics. 5. Understand metrics that can be computed for both designed and found/organic data. 6. Apply the metrics to real data and interpret their resulting values from a TDQ perspective. This specialization as a whole aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis...

3. Data Quality Fundamentals

4.2

(557)

Data quality is not necessarily data that is devoid of errors. Incorrect data is only one part of the data quality equation. Managing data quality is a never ending process. Even if a company gets all the pieces in place to handle today's data quality problems, there will be new and different challenges tomorrow. That's because business processes, customer expectations, source systems, and business rules all change continuously. To ensure high quality data, companies need to gain broad commitment to data quality management principles and develop processes and programs that reduce data defects over time. Much like any other important endeavor, success in data quality depends on having the right people in the right jobs. This course helps you understand key concepts, principles and terminology related to data quality and other areas in data management...

4. The Total Data Quality Framework

By the end of this first course in the Total Data Quality specialization, learners will be able to: 1. Identify the essential differences between designed and gathered data and summarize the key dimensions of the Total Data Quality (TDQ) Framework; 2. Define the three measurement dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; 3. Define the three representation dimensions of the Total Data Quality framework, and describe potential threats to data quality along each of these dimensions for both gathered and designed data; and 4. Describe why data analysis defines an important dimension of the Total Data Quality framework, and summarize potential threats to the overall quality of an analysis plan for designed and/or gathered data. This specialization as a whole aims to explore the Total Data Quality framework in depth and provide learners with more information about the detailed evaluation of total data quality that needs to happen prior to data analysis. The goal is for learners to incorporate evaluations of data quality into their process as a critical component for all projects. We sincerely hope to disseminate knowledge about total data quality to all learners, such as data scientists and quantitative analysts, who have not had sufficient training in the initial steps of the data science process that focus on data collection and evaluation of data quality. We feel that extensive knowledge of data science techniques and statistical analysis procedures will not help a quantitative research study if the data collected/gathered are not of sufficiently high quality. This specialization will focus on the essential first steps in any type of scientific investigation using data: either generating or gathering data, understanding where the data come from, evaluating the quality of the data, and taking steps to maximize the quality of the data prior to performing any kind of statistical analysis or applying data science techniques to answer research questions. Given this focus, there will be little material on the analysis of data, which is covered in myriad existing Coursera specializations. The primary focus of this specialization will be on understanding and maximizing data quality prior to analysis...

5. Data Science Methods for Quality Improvement

Data analysis skills are widely sought by employers, both nationally and internationally. This specialization is ideal for anyone interested in data analysis for improving quality and processes in business and industry. The skills taught in this specialization have been used extensively to improve business performance, quality, and reliability.\n\nBy completing this specialization, you will improve your ability to analyze data and interpret results as well as gain new skills, such as using RStudio and RMarkdown. Whether you are looking for a job in data analytics, operations, or just want to be able to do more with data, this specialization is a great way to get started in the field.\n\nLearners are encouraged to complete this specialization in the order the courses are presented.\n\nThis specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder...

6. GIS Data Formats, Design and Quality

In this course, the second in the Geographic Information Systems (GIS) Specialization, you will go in-depth with common data types (such as raster and vector data), structures, quality and storage during four week-long modules: Week 1: Learn about data models and formats, including a full understanding of vector data and raster concepts. You will also learn about the implications of a data’s scale and how to load layers from web services. Week 2: Create a vector data model by using vector attribute tables, writing query strings, defining queries, and adding and calculating fields. You'll also learn how to create new data through the process of digitizing and you'll use the built-in Editor tools in ArcGIS. Week 3: Learn about common data storage mechanisms within GIS, including geodatabases and shapefiles. Learn how to choose between them for your projects and how to optimize them for speed and size. You'll also work with rasters for the first time, using digital elevation models and creating slope and distance analysis products. Week 4: Explore datasets and assess them for quality and uncertainty. You will also learn how to bring your maps and data to the Internet and create web maps quickly with ArcGIS Online. Take GIS Data Formats, Design and Quality as a standalone course or as part of the Geographic Information Systems (GIS) Specialization. You should have equivalent experience to completing the first course in this specialization, Fundamentals of GIS, before taking this course. By completing the second class in the Specialization you will gain the skills needed to succeed in the full program...

7. Informatica Data Quality Developer Fundamentals

3.9

(126)

Informatica Data Quality (IDQ) Developer is a beginner level course created to give you the foundation for Informatica Developer tool. This course is an extension to the Informatica Data Quality Analyst course (hosted on Udemy by Inf Sid) and covers the steps of UNIX/Windows installation service configurations for both the server and the clientbasics of data quality transformationstransformation languageexpressions, relational data objectsflat filesparametersworkflowsobject migrationdeployment and basic of Command Line Interface (CLI) etcThis course assumes that you have little knowledge of Informatica tools and relational databases but that is not mandatory. The course is  meant for beginner level students and remains at a very basic level through out and offers step-by-step guidance with a business use case. NOTE: Purchasing this course does not entitle you for free software from Informatica. Students should procure the working VM or a valid license key to practice the exercises...

8. Clinical Trials Data Management and Quality Assurance

In this course, you’ll learn to collect and care for the data gathered during your trial and how to prevent mistakes and errors through quality assurance practices. Clinical trials generate an enormous amount of data, so you and your team must plan carefully by choosing the right collection instruments, systems, and measures to protect the integrity of your trial data. You’ll learn how to assemble, clean, and de-identify your datasets. Finally, you’ll learn to find and correct deficiencies through performance monitoring, manage treatment interventions, and implement quality assurance protocols...

9. Improving data quality in data analytics & machine learning

4.7

(182)

All of our decisions are based on data. Our sense organs gather data, our memories are data, and our gut-instincts are data. If you want to make good decisions, you need to have high-quality data. This course is about data quality: What it means, why it's important, and how you can increase the quality of your data. In this course, you will learn: High-level strategies for ensuring high data quality, including terminology, data documentation and management, and the different research phases in which you can check and increase data quality. Qualitative and quantitative methods for evaluating data quality, including visual inspection, error rates, and outliers. Python code is provided to see how to implement these visualizations and scoring methods using pandas, numpy, seaborn, and matplotlib. Specific data methods and algorithms for cleaning data and rejecting bad or unusual data. As above, Python code is provided to see how to implement these procedures using pandas, numpy, seaborn, and matplotlib. This course is for Data practitioners who want to understand both the high-level strategies and the low-level procedures for evaluating and improving data quality. Managers, clients, and collaborators who want to understand the importance of data quality, even if they are not working directly with data...

10. Informatica Data Quality Developer Specialist Certification

4.4

(71)

Informatica Data Quality Practice Tests The practice tests are created to measures your knowledge and competency as an Informatica Data Quality (IDQ) developer who will be a member of any kind of Data Quality projects/implementations. These practice tests cover a lot of ground on in-depth aspects of Data Quality processes such as Profiling, Standardization, Matching, Consolidation, Exception Management and various aspects of configuring the appropriate Data Quality transformations and build the transformations for complex real-time requirements. Additionally, all the other important areas like human tasks, debugging, Reference Data Management, Scorecards, Analyst activities, deployment activities, execute Data Quality mappings, integrating the mappings/rules/mapplets into Informatica Power Center. Is this an online video-based course?No. This is a practice test with questions and multiple-choice answers. Will it be an interactive session?Yes, you will be able to send a note to the trainer/instructor and communicate as well. If you are taking this course, there are options to see the questions asked by you and also by other participants. This will not only help you to get the questions clarified but also you will know the different perspectives of the subject based on the questions asked by other participants. Will I get any material apart from the questions and answers?We will support you with your questions, doubts, and help you with any related Informatica corporation documentation and other curated Data Quality specific products content to learn and understand the content/topics with more clarity. Do these tests cover any real-time scenarios / live project scenarios?Yes, all the content and questions in this course are from real-time project implementations. Everything will be practical and on real-time project implementation. Will this help me to clear the IDQ certification?We encourage you to learn all the topics and prepare for success. All the questions and answers provided here have helped multiple other participants to clear the interviews and IDQ certification without any help. We do help you with any clarifications required. Please note, these questions are not copy and paste from the actual certification test, these questions are prepared based on the test topics covered in the certification testPlease note, launch the practice test using chrome browser. In other browsers, the question and answers may not fit the window and you may have to scroll the page up and down to see the questions and answers. Sample QuestionsQuestion 1. A customer file has 5 million records and 500 records are duplicates, the customer data has to be analyzed based on CustomerName, DateofBirth and SSN. Filter Exact Match option is enabled in Match transformation, how the mapping performance will be impacted?A) Enabling Filter Exact Match option helps improve performance by removing duplicate records out of the matching process. However, there is additional logic (internally added) to remove the duplicate records that adds little overhead to internal mapping logic, hence its not recommended to enable Filter Exact Match option for this scenario. B) It is recommended to use the Filter Exact Match option and allow the 500 duplicate records go through the matching process, as every row of data goes through the matching process irrespective of selecting Filter Exact Match option. C) Filter Exact Match option can only be used in Dual Source matching, and in this scenario, the option is disabled by default. D) It is recommended to group the similar records and send the grouped data to the Match Process instead of using Filter Exact Match option, similar records will be created on the same cluster, which will be (similar records) consolidated and removed by Consolidator transformation. Correct Answer: A. For this scenario, it's not recommended to enable the Filter Exact Match option. Filter Exact Match can be configured in clustered output match type only. When Filter Exact Match is enabled it Improves the performance of the Match Transformation by eliminating the identical records from the matching process. The identical records are directly written to the clustered output. If you have a source dataset with a large number of identical records enabling Filter Exact Match will improve performance by removing identical records out of the matching process. Based on the source data and the number of exact matches in the dataset, it has to be checked if Match Mapping can be executed with or without enabling the Filter Exact Match option. If the data set has a lot of exact matches, there is a lot of overhead to process these exact matches, and Match mapping can be very resource-intensive, to Improve the Match performance in this scenario we can enable the Filter Exact Match Option. Question 2. Using Data Quality for Excel, 80 records should be processed as one batch, select all the correct answers. A) Set Maximum Occurrences to unbounded to allow the web service to process multiple records in one batch. B) In the DQ for excel settings, increase the Batch size to 80C) In the Data Integration Service, under the Application tab, select the web service which is configured in DQ for excel and update the Maximum Concurrent Requests to 80D) Users cannot configure the batch size, the records will be processed as per the default batch size of 1 record. Updating the Maximum Concurrent Requests to 80 in the web services 80 records can be processed in parallel. Correct Answer: A, B DQ for excel helps non-Informatica users to use the data quality rules created in IDQ and validate data in excel. DQ for excel helps users to reuse the data quality rules and perform data validation from their PC and check how the source data will get transformed into the target data objects. In IDQ, developer has created a mapplet that will standardize data based on the reference data and parse address or customer names. Developers can save the mapplet as web service and give the web service ( after deploying the web service to application). Users can use the DQ for excel and create a new service(Add) using the web service url and validate the data in excel. Users can create a batch of 100 records and validate the data using DQ for Excel. The web service Maximum Occurrences should be configured to unbounded to allow the web service to process multiple records in one batch. Question 3. Data has to be extracted from various sources and compared against the master data for each load, the existing process is delaying the load process due to the source data comparison with master data for every load. The customer is insisting to follow the same load approach and asked your recommendation. Select the best approachA) Use Identity Match with Persistent RecordID which uses Universal ID which allows users to store matching keys to make the subsequent matching process efficient. It will significantly improve the performance when source data is matched against master data regularly and the speed of the match operation is critical B) Standardize the data and perform dual-source matching, as data is standardized by removing the noise words, and validating against the reference tables, the match process will be efficient and load time will be considerably less as compared to performing the match without standardizationC) Reading the data from two pipelines and performing a dual-source is not recommended, when time is a constraint. Use Union transformation to combined the data from both the sources and perform single source Field matching and in consolidation transformation use row-based strategy with modal exact option to prioritize the data from the master source. D) All the other approaches mentioned will not reduce the load window, as the match process is a complex process. Instead of using match transformation use Comparison transformation to compare strings between source data and master data and based on the score generated to create an ETL process to load as per your requirement. Correct Answer: AUse Identity Match with Persistent RecordID which uses Universal ID which allows users to store matching keys to make the subsequent matching process efficient. It will significantly improve the performance when source data is matched against master data regularly and the speed of the match operation is critical...

11. Data Quality Masterclass - The Complete Course

4.5

(1,220)

Learn quickly with my Data Quality Management course that covers the latest best practices from the Data IndustryThe course is structured in such a way that makes it easy even for absolute beginners to get started! This course will give you a deep understanding of the Data Quality Management discipline by using hands-on, contextual examples designed to showcase why Data Quality is important and how how to use Data Quality principles to manage the data in your organization. In this Data Quality course you will learn:· What is Data Quality· What is Data Quality Management· Why is Data Quality Important and how it affects your business· What are the different Data Quality Dimensions· What are Data Quality Rules· Profiling· Data Parsing· Data Standartization· Identity resolution· Record linkage· Data cleansing· Data enhancement· What is the Data Quality Process· What are the different Data Quality Roles· Data Quality Tools and their importance· Data Quality Best practicesand much, much more! Enroll today and enjoy: Lifetime access to the course6 hours of high quality, up to date video lecturesPractical Data Quality course with step by step instructions on how to implement the different techniquesThanks again for checking out my course and I look forward to seeing you in the classroom!...

12. Data Warehouse ETL Testing & Data Quality Management A-Z

4.4

(2,977)

Learn the essentials of ETL Data Warehouse Testing and Data Quality Management through this step-by-step tutorial. This course takes you through the basics of ETL testing, frequently used Data Quality queries, reporting and monitoring. In this tutorial we will learn how to build database views for Data Quality monitoring and build Data Quality visualizations and reports!.. Learn to build data quality dashboards from scratch!.. Learn some of the most common mistakes made when performing ETL/ELT tests.... Forget about manual ad-hoc ETL testing, learn more about automated ETL and data quality reportsThe course contains training materials, where you can practice, apply your knowledge and build an app from scratch. The training materials are provided in an Excel file that you can download to your computer. Each module also ends with a short quiz and there is a final quiz at the end of the course. After completion of this course, you will receive a certificate of completion. Good luck and hope you enjoy the course. Pre-requisites: Basic knowledge of SQL Some experience with Visualization tools would be helpful, but not required Basic setup of database (PostgreSQL, Oracle) and visualization tool (Qliksense) is recommendedCourse content: The course consists of the following modules: IntroductionWhat is ETL/ELT Testing and Data Quality Management?Build database views for Data Quality MonitoringBuild dashboards for ReportingExercisesFinal QuizWho should follow this course?Students that want to learn the basics of ETL/ELT testing and Data Quality ManagementBusiness Analysts and Data Analysts that would like to learn more about ETL/ELT testing, frequently used queries and practical examplesSoftware Engineers that would like to build an automated solution for ETL/ELT testing using database views/dashboardsData Stewards and Managers considering to apply data quality standards within their organization...

13. The Complete Data Quality and Digital Transformation Course

4

(91)

In light of the accelerating AI revolution across industries in the past years, it has never been more relevant than it is now post the global pandemic that you should improve your digital literacy and upskill yourself with data analytics skillsets. This course features the latest addition of an organisation structure - Chief Data Office which enables an organisation to become data and insights driven, no matter it's in a centralised, hybrid or de-centralised format. You'll be able to understand how each of the Chief Data Office function works and roles and responsibilities underpinned each pillar which covers the key digital concepts you need to know. There is a focus on the end-to-end data quality management lifecycle and best practices in this course which are critical to achieving the vision set out in the data strategy and laying the foundations for advanced analytics use cases such as Artificial Intelligence, Machine Learning, Blockchain, Robotic Automation etc. You will also be able to check your understanding about the key concepts in the exercises and there are rich reading materials for you to better assimilate these concepts. At the end of the course, you'll be able to grasp an all-round understanding about below concepts: Digital TransformationChief Data OfficerChief Data OfficeCentralised Chief Data Office Organisation StructureData StrategyData MonetisationData GovernanceData StewardshipData QualityData ArchitectureData Lifecycle ManagementOperations IntelligenceAdvanced Analytics and Data ScienceData Quality Objectives6 Data Quality Dimensions and ExamplesRoles and Responsibilities of Data Owners and Data Stewards (Data Governance)Data Quality Management PrinciplesData Quality Management Process CycleData DomainISO 8000Data ProfilingData Profiling Technologies (Informatica, Oracle, SAP and IBM)MetadataDifferences Between Technical and Business MetadataBusiness Validation RulesData Quality Scorecard (with Informatica example)Tolerance LevelRoot Cause AnalysisData CleansingData Quality Issue Management (with a downloadable issue management log template)After you complete this course, you will receive a certificate of completion. So how does this sound to you? I look forward to welcoming you in my course. Cheers, Bing...