4186 | On Demand | Intermediate | Self-Study
Data Analytics Modeling Certificate
Friday, May 1, 2020 - Friday, April 30, 2021
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.
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.
Accounting and finance professionals, especially those interested in learning and applying data analysis techniques to help their organizations make informed, data-driven business decisions.
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