Introduction to Data Literacy

Overview

Understanding how to work with data is an increasingly important skill. Businesses collect huge volumes of data and they expect their workforce to be able to use that data to inform decision making and to justify new strategies, products, and business processes.

Fortunately, if you have basic math skills (arithmetic and some exposure to algebra) then you have the skills needed to be data literate.

Syllabus

• Course Overview and Objectives
Introduction to Data Literacy
Importance of Data in Decision Making
Course Goals and Learning Outcomes
• Understanding Data
Types of Data: Qualitative vs Quantitative
Structured vs Unstructured Data
Basics of Data Collection Methods
• Data Management and Storage
Introduction to Databases and Spreadsheets
Understanding Data Storage Systems
Basics of Data Privacy and Security
• Data Cleaning and Preparation
Importance of Data Quality
Common Data Cleaning Techniques
Handling Missing or Incomplete Data
• Data Analysis Fundamentals
Descriptive Statistics
Introduction to Data Visualization
Basic Tools for Data Analysis (Excel, Google Sheets)
• Interpreting and Presenting Data
Understanding Trends and Patterns
Creating Effective Data Visualizations
Communicating Insights and Findings
• Making Data-Driven Decisions
Introduction to Data-Driven Decision Making
Case Studies: Using Data to Drive Business Strategy
Ethical Considerations in Data Usage
• Introduction to Advanced Topics
Introduction to Big Data and Analytics
Overview of Machine Learning and AI
Opportunities for Further Learning in Data Science
• Course Review and Project
Recap of Key Concepts and Skills
Final Project: Applying Data Literacy Concepts to a Real-World Scenario
Course Evaluation and Feedback

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Taught by

Dan Sullivan

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Subjects

Data Science