Description

Course Details:

• The Data Analytics Modelling 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.

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.

Digital Badge:

• On completion of this certificate you will receive a digital badge to share with your online professional network, which is available as a downloadable PDF certificate suitable for printing and framing.

Key Topics:

• 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

Learning Outcomes:

• 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