Introduction to Business Data Analytics (CBDA)

About This Course

Introduction to Business Data Analytics (CBDA) is a comprehensive preparatory course that is specially designed to help you pass the IIBA - CBDA certification exam. This course focuses on the technical skills required to effectively perform business data analytics, its objectives, and its applications in various industries. Gain hands-on experience with data collection, cleaning, analysis, interpretation, and visualization techniques. After the completion of this ‘Introduction to Business Analytics’ course, you’ll have a clear understanding of the importance of data-driven decision making at the strategic level and develop a data strategy for your organization.

Skills You’ll Get

• Techniques for data cleaning and preparation (handling missing values, outliers, and more)
• Summarize and understand data characteristics for Exploratory Data Analysis (EDA)
• Conversion of raw data into a suitable format for analysis
• Using statistical methods to analyze data, including descriptive statistics, hypothesis testing, and correlation analysis
• Using visualization techniques to create various types of charts and graphs to communicate data insights
• Using different visualization methods to create stories that convey complex information in a clear and understandable manner
• Create conceptual, logical, and physical data models to represent the structure and relationships of data elements
• Ability to apply analytical techniques such as decision trees and regression analysis
• Find optimal solutions to complex problems, such as resource allocation or scheduling with mathematical techniques
• Implement policies and procedures to ensure data quality, security, and compliance with regulations
• Awareness of data warehousing and data marts to perform centralized data storage and analysis
• Execute ETL (Extract, Transfer, Load) process for extracting data from various sources, transforming it into a suitable format, and loading it 
• Apply data mining techniques and algorithms to find patterns and trends in large datasets
• Using Machine Learning (ML) Algorithms to build predictive learning models

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