Principles of Data Analytics

Principles of Data Analytics is a professional certification aimed at validating foundational data analytics skills for individuals looking to enter the field of data analytics. It is designed for professionals who are responsible for developing and promoting data-driven business decision-making.

Key Components:

1. Data Mining: Techniques and tools used to extract information from large datasets.
2. Data Manipulation: Methods for cleaning, transforming, and preparing data for analysis.
3. Data Visualization: Skills in creating and interpreting graphical representations of data.
4. Data Governance: Understanding of data policies, privacy regulations, and ethical considerations.
5. Basic Statistical Methods: Application of statistical techniques to analyze data and generate insights.

Exam Details:

• Format: Multiple-choice and performance-based questions.
• Duration: 90 minutes.
• Questions: Up to 90 questions.
• Passing Score: 675 on a scale of 100-900.

Target Audience:

• Data Analysts
• Business Analysts
• Reporting Analysts
• Any professional looking to transition into a data-focused role

Benefits:

• Skill Validation: Confirms the ability to handle data responsibly and effectively.
• Career Advancement: Enhances job prospects and potential for career growth in data-related fields.
• Industry Recognition: Acknowledged by employers as a credible certification in data analytics.

Topics Covered:

1. Data Concepts and Environments: Understanding different types of data, data structures, and the environments in which data is stored and processed.
2. Data Mining and Analysis: Techniques for exploring and analyzing data to uncover patterns and insights.
3. Visualization: Creating and interpreting charts, graphs, and dashboards to present data findings.
4. Data Governance, Quality, and Controls: Ensuring data integrity, compliance with regulations, and establishing controls for data accuracy and security.

Principles of Data Analytics provides a comprehensive foundation for those looking to enhance their data analytics skills and pursue a career in data-driven roles.