Power BI Data Specialist
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Data Visualization with Python
Data Visualization with Python $100.00

Introduction to Business Data Specialist (CBDA)

$100.00

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

  • Free pre-assessment and first 2 lessons
  • 8+ Interactive Lessons | 115+ Exercises
  • Accessible on mobile and tablet too
  • Certificate of completion
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Description

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