DAFF-M01 Data Analysis for Fraud Using Spreadsheets - Module 01 Data Quality (PREVIEW)

This is a PREVIEW of the below course. For access to the full course, click here


Course Overview

This module introduces students to data quality assessment methodology which is required prior to analyzing any dataset. Definitions, examples, and exercises are given to provide students with an understanding of the steps performed to ensure quality of the data is optimal for analysis. 

Learning Objectives

Students will learn:

  • the importance of data quality and the key definitions of data quality dimensions;
  • how to obtain access to the required dataset; (partial in PREVIEW)
  • how to document the work they perform; (partial in PREVIEW)
  • how to assess the dataset for: 
    • completeness; 
    • uniqueness;
    • timeliness; (disabled in the PREVIEW)
    • validity; (disabled in the PREVIEW)
    • accuracy; and (disabled in the PREVIEW)
    • consistency. (disabled in the PREVIEW)

Features

  • 24/7 access to courses through your Internet browser;
  • Save time and quickly earn CPE credits with instant access; 
  • Qualified assessment (quiz) grading after each lesson; 
  • The flexibility to start or stop a course and pick-up right where you left off; 
  • A printable CPE certificate at the end of the course upon successful completion; and
  • No additional shipping fees.

Qualified Instructor

Alexis C. Bell, MS, CFE, PI


Level

Basic and Introductory -  Program knowledge level most beneficial to students new to a skill or an attribute. These individuals are often at the staff or entry level in organizations, although such programs may also benefit a seasoned professional with limited exposure to the area.


Pre-Requisites Required

Experience: None

Advance Preparation: This course uses spreadsheets such as MS Excel or Google Sheets. Students will be required to use a spreadsheet software of their choosing during the exercise portion of the lesson. Therefore, prior to starting the course, students will need to be able to access a spreadsheet software during certain lessons in order to be able to complete the course. 


CPE Hours

The recommended CPE credit for this course is 5.25 CPE hours in the below indicated fields of study.

NOTE - This is a PREVIEW of the course. No CPE credits will be awarded for the preview. To receive the above number of CPE hours, you must complete the full course here

Credit Hour

One credit hour is equal to fifty minutes of participation in a program of learning.

Claiming CPE Credit

The receiving organization where you submit the certificate of completion has final authority on the acceptance of the individual courses for CPE credit. To be eligible for CPE credit, you must complete each lesson in the course, successfully pass the final exam with at least an 80% score, and submit the ending evaluation within one year of purchase date. You may only claim CPE credit for a course once.


Fields of Study

This course may be submitted for consideration for continuing professional education (CPE) credit under the following fields of study:

We Want to Know

Have you submitted this course to an organization and the CPE credit was accepted by another organization? We want to know the organization's name, website, and field of study. Please email us with the details at [email protected]


Instructional Method

This is an online self-study, computer-assisted learning course. 


Acknowledgements

  • “MS Excel®” is a registered trademark of Microsoft Corporation. 
  • “Google SheetsTM” is a web-based spreadsheet program with a registered trademark of Google. https://docs.google.com/spreadsheets/


Course Syllabus

Introduction to Data Quality

Students will learn the importance of data quality and the key definitions of data quality dimensions.

Ensure Access

Students will learn how to obtain access to the required dataset.

Documentation of Assessment

Students will learn how to document the work they perform.

Data Quality Assessment for Completeness

Students will learn how to assess the dataset for completeness.

Data Quality Assessment for Uniqueness

Students will learn how to assess the dataset for uniqueness.

Data Quality Assessment for Timeliness

Students will learn how to assess the dataset for timeliness.

Data Quality Assessment for Validity

Students will learn how to assess the dataset for validity.

Data Quality Assessment for Accuracy

Students will learn how to assess the dataset for accuracy.

Data Quality Assessment for Consistency

Students will learn how to assess the dataset for consistency.

Course Final Exam, Evaluation & CPE Certification

Students must complete the final exam and pass with a minimum score of 80%. Afterwards, they are provided an opportunity to evaluate the course and then receive their CPE certificate.
This is a great course, very well thought out and designed with consistency in the learning objectives and actual layout of the course format. It provides an initial format for the student to develop assessment tools supported by well documented process steps which can be followed by users. — Christine Dever Homack, Principal, Accountabilities Consulting Services, LLC
What stood out to me was the usefulness of the videos. They allowed me to go through the lessons step by step in order to preform the tasks. I was very impressed by instructors use of Excel. It became confusing to me based upon my limited experience with it, but it was great to see it in action. I thought the course was well presented. The instructional videos were awesome and it gave me a real sense of the actual work involved. — Shawn Ganley, Detective, Rahway Police Department

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