Earners of this certificate drive successful analytics practices by defining clear business outcomes to ensure efforts align with organizational strategic direction. Badge earners manipulate and work with structured and unstructured data, profile data, and apply cleansing techniques to maintain data quality throughout the data-life cycle. These candidates apply ETL (extract, transform, load) techniques to data models and use sophisticated tools for managing an ongoing data practice.
This certificate is Part 3 of the Data Analyst Certificates Bundle – a comprehensive five-part program that provides training and practical guidance on the topic of data analytics.
The Data Analytics Modeling Certificate will expand your ability to work with structured and unstructured data to drive a successful analytics practice. To start, you will learn to define clear business outcomes for your analytics practice to ensure your efforts align with your organization's strategic direction and create value. Next, you will learn data profiling and data cleansing techniques to maintain data quality throughout the data life cycle. You'll practice ETL (extract, transform, load) techniques and work with different data models and analytics tools. Finally, you will learn how to institute sophisticated tools for managing an ongoing enterprise data practice, including tools for data warehousing, managing the data life cycle, and working with structured and unstructured data.
Note: It is recommended that you complete the Application of Data Analysis Essentials Certificate, or ensure you have equivalent knowledge and skills, before starting this certificate course.
Learning Labs: This is an interactive learning program that includes bonus simulated learning labs with step-by-step guidance that will expose you to the tools needed to implement an analytics practice in a practical way and equip you to deploy those tools as needed within your organization. You will practice using various technologies for preparing, analyzing and managing datasets in the real world. *Time spent on learning labs does not award CPE and completing learning labs is not a requirement for earning the certificate
Accounting and finance professionals, especially those interested in learning and applying data analysis techniques to help their organizations make informed, data-driven business decisions.Other information
|Format:||Course - Online|
|Access:||This is a digital product. You will have access to the content for a year after purchase date.|
You have twelve (12) months from the date of purchase to complete the entire program.
- The certificate course subscription validity period is for 12 Months respectively.
- Students will receive an activation directly from AICPA within 3 working days
All students will get a digital badge upon completion. A digital badge helps you gain recognition for earning your certificate by allowing you to easily display and share your achievement. Prospective clients and employers will be able to quickly verify your competencies and skills. The digital badge also provides greater credibility and visually declares your commitment to quality. According to a recent LinkedIn study, profiles with certifications and badges receive six times the number of profile views.
- Identify opportunities, processes, and necessary data for solving analytical problems.
- Apply data profiling and data cleansing techniques to available data.
- Use data preparation and enrichment tools.
- Use ETL (extract, transform, load) tools.
- Compare data warehousing techniques.
- Use data warehousing and data management tools.
- Align the outcomes of your data analytics practice with your organization's strategic direction and create value
- Defining value and tying analytics to value-driven business cases
- Understanding the characteristics of data and how they can be leveraged to gather insights from information
- Identifying project constructs for data analytics
- Identifying different types of data with which analysts will be expected to interact
- Profiling data for accurate analysis initiatives
- Understanding tool capabilities for working with data
- Cleansing data with appropriate tools to increases analytics accuracy
- Managing data quality and integrity
- Extracting, transforming, and loading data
- Implementing a data warehouse
- Managing the data life cycle
- Creating and using different types of data models
- Tools for working with both structured and unstructured data