Day Program - CBA 350
Schedule: Three Days, Monday - Wednesday, 8:30 a.m. to 4 p.m.
Schedule: Online, Anytime
The 21st century belongs to those who can think and act based on sound business intelligence. Organizations need to make business decisions based on more than feelings or gut reactions to events – regardless of the field. Consumer product companies, insurance companies, banks, governments, and even sports teams are utilizing analytics to improve their bottom line and assure their long term success.
This course focuses on business analytics as a process for transforming data sourcing/management and data integration into meaningful business intelligence.
This course directly supports the BIA segment of the (CBIP) Certification Exam (CBIP Certified Business Intelligence Professional Exam).
Who Should Attend
Business executives, owners, and managers seeking an improved understanding of business intelligence and business analytics practices. It is also designed for business analysts or process managers, business or technical systems analysts, requirements engineers, product managers, product owners, enterprise analysts, business architects, management consultant/change agents, or a practitioner in a related discipline such as project management, software development, and quality assurance or interaction design.
This course will teach you how to transform data to meaningful business intelligence in order to make sound business decisions. You will learn and understand the relationships between business process performance, integration, and business performance metrics. You will learn how to set up a business metrics dashboard to examine and understand the relationship between business intelligence and business analytics. As important, you will learn how to best interpret what the data represents by extracting the meaningless data to avoid misinterpretation of “real” data required to make better decisions.
Our program also includes the study of predictive analytics. Predictive models and analysis are typically used to forecast future probabilities. We introduce and discuss a number of techniques, including data mining, statistical modeling, and machine learning to help analysts make future business forecasts.