IIBA

Introduction to Business Data Analytics (CBDA)

Data is the new King! Learn how to perform data analysis that affects crucial business decisions.

(IIBA-CBDA.AQ1) / ISBN : 978-1-64459-376-9
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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

 

Download Course Outline

1

Introduction to Business Data Analytics

  • What is Business Data Analytics?
  • The Business Data Analytics Cycle
  • Business Data Analytics Objectives
  • Business Analysis and Business Data Analytics
  • Applications of Data Analytics
2

Identify the Research Questions

  • Define Business Problem or Opportunity
  • Identify and Understand the Stakeholders
  • Assess Current State
  • Define Future State
  • Formulate Research Questions
  • Plan Business Data Analytics Approach
  • Techniques
3

Source Data

  • Plan Data Collection
  • Determine the Data Sets
  • Collect Data
  • Validate Data
  • Techniques
  • A Case Study for Source Data
4

Analyze Data

  • Develop Data Analysis Plan
  • Prepare Data
  • Explore Data
  • Perform Data Analysis
  • Assess the Analytics and System Approach Taken
  • Techniques
  • A Case Study for Analyze Data
5

Interpret and Report Results

  • Validate Understanding of Stakeholders
  • Plan Stakeholder Communication
  • Determine Communication Needs of Stakeholders
  • Derive Insights from Data
  • Document and Communicate Findings from Completed Analysis
  • Techniques
  • A Case Study for Interpret and Report Results
6

Use Results to Influence Business Decision-Making

  • Recommend Actions
  • Develop Implementation Plan
  • Manage Change
  • Techniques
  • A Case Study for Use Results to Influence Business Decision-Making
7

Guide Organizational-Level Strategy for Business Data Analytics

  • Organizational Strategy
  • Talent Strategy
  • Data Strategy
  • Techniques
  • Competencies
  • A Case Study for Guide Organization-Level Strategy for Business Data Analytics
A

Appendix A: Techniques

  • Business Simulation
  • Business Visualizations
  • Concept Modelling
  • Data Dictionary
  • Data Flow Diagrams
  • Data Mapping
  • Data Storytelling
  • Decision Modelling and Analysis
  • Descriptive and Inferential Statistics
  • Extract, Transform, and Load (ETL)
  • Exploratory Data Analysis
  • Hypothesis Formulation and Testing
  • Interface Analysis
  • Optimization
  • Problem Shaping and Reframing
  • Stakeholder List, Map, or Personas
  • Survey and Questionnaire
  • Technical Visualizations
  • The Big Idea
  • 3-Minute Story

Any questions?
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There’s a lot to learn about data analysis and its correlation to business intelligence. Read this section to find out more about the course highlights.

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It is a certification program conducted by the International Institute of Business Analysis (IIBA) to validate your skills for performing data analytics, making data-driven decisions, and contributing effectively to organizational success. It is a valuable asset for those working in data analytics, business intelligence, and related fields.

There are no formal prerequisites for this exam. However, it is recommended that you have a minimum 21 months of business analysis experience within the past four years.

This comprehensive course content focuses on these areas including: business analysis fundamentals, data analysis techniques, data modeling and management, business intelligence and analytics, and data governance & ethics.

It is an industry-recognized, global certification that can significantly boost your career and contribute to your success in the field of business data analytics. It can open new job opportunities in the regional as well as global markets with a higher compensation.

The average annual salary of a CBDA certified analyst is around $85,000 annually.

The exam fee is $250 for members and $400 for non members.

There are 75 questions in total. The exam format is multiple-choice, and scenario-based questions.

The exam duration is 120 minutes.

Yes, it is an ideal course for job seekers as well as business professionals wanting to learn data analytics and make data-driven decisions.

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