Data Audit Methodology for Analytics Practitioners
Data quality concepts and best practices for ensuring the quality of data used for analytics development projects.
Description
If you are an analytics practitioner, be it in statistics, data science, machine learning, and so on, you’ve experienced frustration with data quality, not just once, but multiple times. If you are an aspiring analytics practitioner, know that you will experience this frustration. There is not a single analytics practitioner in the world who can escape from dealing with data problems.
We find so many problems with the quality of the data. However, our non-analytics clients and colleagues often expect us to be able to resolve data quality issues just because we work with data as professionals. This difference in expectations often leads to conflict with little resolution.
Although sometimes we are lucky, we often find the data problems well into the analysis process, causing project delays or even project cancellations. This worsens the friction and impacts our working relationships with clients and colleagues.
This course prepares you with a practical methodology to address this ever-challenging topic with what we can do as analytics practitioners with things we can and should be responsible for, as competent professionals.
This course is intended for current and aspiring technical professionals in data, including analytics, statistics, data science, business intelligence, data engineering, machine learning, and artificial intelligence, among others.
What You Will Learn!
- A framework for data quality, rooted in global data management standards.
- What data quality means for analytics practitioners and how that differs from analytical design.
- The various sources of data defects in an analytics project.
- A methodology for effective audit and understanding of data collected/extracted, to reduce project delays caused by data quality issues.
Who Should Attend!
- Current and aspiring technical professionals in data, including analytics, statistics, data science, business intelligence, data engineering, machine learning, and artificial intelligence, among others.