Data Audit Methodology for Analytics Practitioners

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Michiko I. Wolcott

1:25:56

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1. Introduction

1. 1-1. What is data quality.mp4

07:49

2. 1-2. What is data management.mp4

09:46

2. Overview of the Data Audit Methodology

1. 2-1. Methodology Overview.mp4

08:10

2. 2-2. The Audit Plan.mp4

07:32

3. The Data Audit Methodology in Detail

1. Introduction to the Checklist.mp4

05:22

2. Day 1.mp4

04:54

3. Day 2.mp4

13:51

4. Day 3.mp4

09:28

5. Days 4-7.mp4

11:44

6. Finalizing the Audit.mp4

06:18

4. Final Comments

1. Final Comments.mp4

01:02

Description

Data quality concepts and best practices for ensuring the quality of data used for analytics development projects.

What You'll 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 is this 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 Need to Know?

While the participants are expected to have basic proficiency in scripting/programming for conceptual reasons, no specific scripting language is assumed.

More details

Description
If you are an analytics practitioner, be it in statistics, data science, machine learning, and so on, youâ€™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.
Who this course is for:
Current and aspiring technical professionals in data, including analytics, statistics, data science, business intelligence, data engineering, machine learning, and artificial intelligence, among others.

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Data Analysis

Michiko I. Wolcott

Instructor's Courses

Michiko is currently the managing partner and principal consultant of Msight Analytics, a management consulting firm specializing in data and analytics. Backed by 20 years of experience in analytical project execution and delivery, she has helped organizations of all sizes in the development of enterprise capability and effectiveness in data and analytics.She has led multi-national analytical consulting practices, with clients and colleagues from across the globe in financial services, media and communications, retail, healthcare, life sciences, public sector, as well as humanitarian response and disaster management, and has spoken at many industry conferences and forums.Her prior responsibilities include serving as the Lead Data Scientist at North Highland and leading the international analytics practice at Equifax as the Vice President of International Analytics. Michiko holds a Master of Science degree in Statistics from Florida State University among other degrees from Florida State University and the Peabody Conservatory of the Johns Hopkins University.

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