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Data Literacy Certification Course Overview

Data Literacy Certification Course Overview

Unlock your potential with Koenig Solutions' Data Literacy Certification course. Spanning 16 hours, this comprehensive program is designed for anyone who works with data and wants to enhance their data literacy skills. Throughout the course, you will learn to interpret business requirements, transform data, design and build visualizations, and effectively analyze and share results.

In addition, you'll gain expertise in employing basic statistics, ensuring data security, and choosing appropriate visualizations. By the end of the course, you'll confidently make data-driven decisions, recommend actions, and communicate insights to various audience types. Whether you're an analyst, manager, or aspiring to be one, this course equips you with the critical skills needed to leverage data effectively for business success.

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Course Prerequisites

Minimum Required Prerequisites for Data Literacy Certification Course:


  • Basic understanding of data concepts and familiarity with data types (e.g., numbers, texts, dates).
  • Familiarity with common business terminologies and objectives.
  • Fundamental knowledge of basic statistics and mathematics.
  • Proficiency in using basic computer tools (e.g., spreadsheets, word processors).
  • Interest in analyzing and interpreting data to make informed decisions.

These prerequisites ensure that you have a foundational understanding, enabling you to grasp the course content more effectively and gain the most from the Data Literacy Certification training.


Target Audience for Data Literacy Certification

1. Introduction:


The Data Literacy Certification course is designed for professionals handling data, enhancing their ability to interpret, visualize, and act upon data insights effectively.


2. Job Roles and Audience:


• Data Analysts
• Business Analysts
• Data Scientists
• Business Intelligence (BI) Developers
• Statisticians
• Data Engineers
• Financial Analysts
• Marketing Analysts
• Operations Managers
• IT Professionals
• Project Managers
• Product Managers
• Supply Chain Analysts
• HR Analysts
• Consultants (Management and IT)
• Academic Researchers
• Healthcare Analysts
• Sales Analysts
• Risk Managers
• Quality Assurance Specialists
• Manufacturing Analysts




Learning Objectives - What you will Learn in this Data Literacy Certification?

Introduction: The Data Literacy Certification course equips participants with the skills to interpret business requirements, understand and transform data, design and interpret visualizations, analyze, act on, and share results effectively.

Learning Objectives and Outcomes:

  • Interpret Business Requirements

    • Discuss business requirements for feasibility to implement.
    • Transform a business question into an analytical question.
    • Explain data sources and refresh frequency needed to implement requirements.
    • Discuss KPIs, dimensions, and measures for analysis.

  • Understand and Transform Data

    • Explain various data types and their implications for analysis.
    • Compare various classifications of data.
    • Explain data structure and its implications for analysis.
    • Contrast data schemas and describe their impact on analyses.
    • Apply basic statistics and explain needed aggregations.

  • Design and Build Visualizations

    • Use appropriate visualizations and complexity levels for different audiences.
    • Utilize correct measurements and properties of visualizations.
    • Arrange visualizations to engage the audience and enable analysis.

  • Interpret Visualizations

    • Validate visualizations and determine if they answer analytical questions.
    • Interpret visualizations to derive observations.
    • Identify outliers, trends, and relationships between data elements.
    • Recommend and validate theories based on visual data.

Target Audience for Data Literacy Certification

1. Introduction:


The Data Literacy Certification course is designed for professionals handling data, enhancing their ability to interpret, visualize, and act upon data insights effectively.


2. Job Roles and Audience:


• Data Analysts
• Business Analysts
• Data Scientists
• Business Intelligence (BI) Developers
• Statisticians
• Data Engineers
• Financial Analysts
• Marketing Analysts
• Operations Managers
• IT Professionals
• Project Managers
• Product Managers
• Supply Chain Analysts
• HR Analysts
• Consultants (Management and IT)
• Academic Researchers
• Healthcare Analysts
• Sales Analysts
• Risk Managers
• Quality Assurance Specialists
• Manufacturing Analysts




Learning Objectives - What you will Learn in this Data Literacy Certification?

Introduction: The Data Literacy Certification course equips participants with the skills to interpret business requirements, understand and transform data, design and interpret visualizations, analyze, act on, and share results effectively.

Learning Objectives and Outcomes:

  • Interpret Business Requirements

    • Discuss business requirements for feasibility to implement.
    • Transform a business question into an analytical question.
    • Explain data sources and refresh frequency needed to implement requirements.
    • Discuss KPIs, dimensions, and measures for analysis.

  • Understand and Transform Data

    • Explain various data types and their implications for analysis.
    • Compare various classifications of data.
    • Explain data structure and its implications for analysis.
    • Contrast data schemas and describe their impact on analyses.
    • Apply basic statistics and explain needed aggregations.

  • Design and Build Visualizations

    • Use appropriate visualizations and complexity levels for different audiences.
    • Utilize correct measurements and properties of visualizations.
    • Arrange visualizations to engage the audience and enable analysis.

  • Interpret Visualizations

    • Validate visualizations and determine if they answer analytical questions.
    • Interpret visualizations to derive observations.
    • Identify outliers, trends, and relationships between data elements.
    • Recommend and validate theories based on visual data.