Data Quality Fundamentals

Data Quality Fundamentals, available at $54.99, has an average rating of 4.4, with 35 lectures, based on 588 reviews, and has 2429 subscribers.

You will learn about Determine data quality requirements by studying business functions, gathering information, evaluating output requirements and formats. Profile select data sets to ensure quality and develop the data visualizations necessary to both manage and communicate data quality. Coordinate business efforts to deliver data that is fit for use for use in critical processes, analysis and reports. Collaborate with business application team to document information architecture requirements as needed Serve as a subject matter expert and perform data quality related functions for urgent, high visibility, high profile, and strategic projects while meeting challenging deadlines. This course is ideal for individuals who are Data Scientists or Solution Architects or Big Data Developers/Administrator or Data Quality Consultants or Data Analysts or Data Stewards or Project Managers or ETL Developers or ETL Testers It is particularly useful for Data Scientists or Solution Architects or Big Data Developers/Administrator or Data Quality Consultants or Data Analysts or Data Stewards or Project Managers or ETL Developers or ETL Testers.

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Summary

Title: Data Quality Fundamentals

Price: $54.99

Average Rating: 4.4

Number of Lectures: 35

Number of Published Lectures: 35

Number of Curriculum Items: 35

Number of Published Curriculum Objects: 35

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

Determine data quality requirements by studying business functions, gathering information, evaluating output requirements and formats.
Profile select data sets to ensure quality and develop the data visualizations necessary to both manage and communicate data quality.
Coordinate business efforts to deliver data that is fit for use for use in critical processes, analysis and reports.
Collaborate with business application team to document information architecture requirements as needed
Serve as a subject matter expert and perform data quality related functions for urgent, high visibility, high profile, and strategic projects while meeting challenging deadlines.

Who Should Attend

Data Scientists
Solution Architects
Big Data Developers/Administrator
Data Quality Consultants
Data Analysts
Data Stewards
Project Managers
ETL Developers
ETL Testers

Target Audiences

Data Scientists
Solution Architects
Big Data Developers/Administrator
Data Quality Consultants
Data Analysts
Data Stewards
Project Managers
ETL Developers
ETL Testers

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.�

Course Curriculum

Chapter 1: Data Quality

Lecture 1: What is Data Quality?

Lecture 2: Example of Data Quality

Lecture 3: Can we achieve 100 % Data Quality?

Lecture 4: What can be done to achieve 100% Data Quality?

Lecture 5: How can we measure Data Quality?

Chapter 2: Data Quality Dimensions

Lecture 1: What are Data Quality Dimensions?

Lecture 2: Consistency Data Quality Dimension

Lecture 3: Completeness Data Quality Dimension

Lecture 4: Timeliness Data Quality Dimension

Lecture 5: Uniqueness Data Quality Dimension

Lecture 6: Validity Data Quality Dimension

Lecture 7: Accuracy Data Quality Dimension

Lecture 8: Example of Data Quality Dimension

Chapter 3: Data Quality Vs Data Governance

Lecture 1: Data Quality Vs Data Governance

Chapter 4: Data Life Cycle

Lecture 1: Introduction to the End to End Data Life Cycle with a case study

Lecture 2: Data Maintenance

Lecture 3: Data Derivation

Lecture 4: Data Usage

Lecture 5: Data Publication

Lecture 6: Data Archival

Lecture 7: Data Purging

Chapter 5: Data Quality Life Cycle

Lecture 1: Data Quality Life Cycle

Chapter 6: Data Profiling

Lecture 1: What is Data Profiling?

Lecture 2: Commonly used data types during Data Profiling

Lecture 3: Data Profiling Vs Data Mining

Lecture 4: What are the different types of Data Profiling?

Chapter 7: Business Expectations and Impacts of Low Data Quality

Lecture 1: Business Expectations on Data Quality

Lecture 2: Impacts and Costs of Low Data Quality – Part 1

Lecture 3: Impacts and Costs of Low Data Quality – Part 2

Lecture 4: How to correct the existing errors in the Data Warehouse?

Lecture 5: How does the Enhance, Transform and Calculate phase or the ETL phase help?

Lecture 6: Data Standardization

Lecture 7: Complete and Corrected Data

Lecture 8: Match and Consolidate the Data

Chapter 8: Data Quality Roles

Lecture 1: Different Data Quality Roles in an Enterprise

Instructors

Sid Inf
Data/ETL Architect

Rating Distribution

1 stars: 12 votes
2 stars: 18 votes
3 stars: 105 votes
4 stars: 239 votes
5 stars: 214 votes

Frequently Asked Questions

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