Data Literacy & Digital Problem Solving

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About Course

Course Code: DF02

Program: Digital Future Skills Program

Course Overview: Data literacy enables students to understand information, question its quality, recognize patterns, and use evidence to make informed decisions. These skills are increasingly valuable in academic work, professional settings, and everyday problem-solving.

This course introduces data concepts, types, sources, quality, interpretation, visualization, structured problem-solving, and evidence-based decision-making while emphasizing clear, responsible communication of insights.

Learning Outcomes

  • Explain basic data-literacy concepts and identify reliable data sources
  • Interpret simple data patterns, trends, charts, and visualizations
  • Evaluate data quality, credibility, and potentially misleading information
  • Apply structured digital problem-solving and evidence-based decision-making methods
  • Communicate data insights clearly and responsibly

Target Audience: Students across disciplines who want to understand data, interpret evidence, and solve problems without requiring an advanced technical background.

Learning Experience: Students engage through video overviews, audio deep dives, study guides, practical examples, reflection prompts, workbook activities, applied tasks, knowledge checks, and end-of-module quizzes. Activities support data-source evaluation, quality checks, pattern and chart interpretation, structured problem-solving, evidence-based decisions, and responsible communication of insights.

Course Structure: 5 modules / 20 lessons.

Special Feature: Every module concludes with a Putting It All Together audio deep dive that connects the key lessons and supports reflection and practical application.

Pathway Focus: Understand data. Analyse evidence. Communicate informed conclusions.

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Course Content

Course Introduction

  • DF02 Data Literacy & Digital Problem Solving
    03:52

Foundations of Data Literacy
Module Overview: Data literacy is the ability to understand, interpret, question, and use data effectively. In university and the workplace, students increasingly need to make decisions based on evidence rather than assumptions. This module introduces the foundations of data literacy and shows why data matters for learning, problem-solving, and future employability. Learning Focus: What Is Data Literacy? Why Data Matters in Learning, Work & Society Basic Data Concepts Every Student Should Know Using Data Responsibly Lesson Sequence: What Is Data Literacy? Why Data Matters in Learning, Work & Society Basic Data Concepts Every Student Should Know Using Data Responsibly

Understanding Data Types, Sources & Quality
Module Overview: Not all data is equally reliable, accurate, or useful. Students must learn how to identify different types of data, evaluate data sources, recognize quality issues, and question misleading or incomplete information. This module helps students develop the ability to critically evaluate data before using it for analysis, decision-making, or communication. Learning Focus: Understanding Data Types Data Sources: Primary, Secondary & Digital Sources Data Quality, Reliability & Validity Identifying Misleading, Biased & Incomplete Data Lesson Sequence: Understanding Data Types Data Sources: Primary, Secondary & Digital Sources Data Quality, Reliability & Validity Identifying Misleading, Biased & Incomplete Data

Data Interpretation & Visualization
Module Overview: Data becomes more useful when people can interpret it clearly and communicate insights effectively. Charts, graphs, dashboards, and visual summaries help students understand patterns, compare information, and explain findings to others. This module helps students develop practical skills in reading, interpreting, and creating simple data visualizations responsibly. Learning Focus: Reading & Understanding Charts and Graphs Identifying Patterns, Trends & Insights Foundations of Effective Data Visualization Communicating Data Clearly & Avoiding Misleading Visuals Lesson Sequence: Reading & Understanding Charts and Graphs Identifying Patterns, Trends & Insights Foundations of Effective Data Visualization Communicating Data Clearly & Avoiding Misleading Visuals

Digital Problem Solving & Decision Making
Module Overview: Modern academic and workplace environments require students to solve problems using structured thinking, digital tools, evidence, and data-informed decision-making. Strong problem-solving skills help students identify challenges, evaluate solutions, and make better decisions. This module helps students develop practical digital problem-solving and analytical decision-making skills. Learning Focus: Foundations of Digital Problem Solving Analytical Thinking & Root Cause Analysis Evidence-Based Decision Making Evaluating Solutions, Risks & Outcomes Lesson Sequence: Foundations of Digital Problem Solving Analytical Thinking & Root Cause Analysis Evidence-Based Decision Making Evaluating Solutions, Risks & Outcomes

Applying Data Literacy in Academic & Workplace Contexts
Module Overview: Data literacy is valuable in both academic and professional environments. Universities, organizations, and industries use data to evaluate performance, solve problems, improve services, and support decision-making. This module helps students apply data literacy skills in real academic, workplace, and collaborative contexts. Learning Focus: Data Literacy in Academic Success & Research Workplace Data, Reporting & Performance Measurement Communicating Recommendations Using Data Responsible Data Use, Collaboration & Professional Practice Lesson Sequence: Data Literacy in Academic Success & Research Workplace Data, Reporting & Performance Measurement Communicating Recommendations Using Data Responsible Data Use, Collaboration & Professional Practice

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