Data Analytics Foundations — Illustrated Study Guide

A tool-neutral, illustrated study guide for the Data Analytics Foundations certification. Four modules build a clear mental model from zero: what data analytics is and how the analytics lifecycle flows; the types and measurement scales of data; descriptive statistics and data quality; and the four analytics types, visualization, querying with SQL, correlation versus causation, KPIs, and data ethics. Rich with Mermaid diagrams (the analytics lifecycle, the data-type taxonomy, a data-cleaning pipeline, and the four-analytics ladder), comparison tables (measurement scales; a chart-selection guide), worked examples (computing mean, median, and standard deviation by hand), key takeaways, and self-checks.

Key facts

• Course price: $65 (one-time payment)
• Field: Data Engineering & Analytics
• Prepares for: Data Analytics Foundations
• 4 modules · 12 lessons · about 3 hours of lessons
• Self-paced, online, illustrated lessons

Course outline

1. What Data Analytics Is, and the Lifecycle — From a working definition of data analytics to the lifecycle that moves a project from question to data to insight to decision, and the kinds of data you will meet.
   • What Data Analytics Actually Is
   • The Analytics Lifecycle
   • Types of Data: Structured, Semi-structured, Unstructured
2. Measurement Scales and Descriptive Statistics — The four measurement scales and why they dictate valid statistics; central tendency (mean, median, mode); and spread (variance, standard deviation, percentiles) with a worked calculation.
   • Data Types and Measurement Scales
   • Central Tendency: Mean, Median, Mode
   • Spread: Variance, Standard Deviation, Percentiles
3. Data Collection, Cleaning, and Quality — Where data comes from, the dimensions of data quality, and a practical cleaning pipeline for missing values, outliers, and duplicates.
   • Data Sources and Collection
   • Data Quality Dimensions
   • Cleaning: Missing Values, Outliers, Duplicates
4. From Insight to Decision — The four analytics types, choosing the right chart, querying data with SQL, the correlation-versus-causation trap, KPIs, and the essentials of data ethics and privacy.
   • The Four Types of Analytics, and Choosing the Right Chart
   • Querying Data: Spreadsheets, Databases, and SQL
   • Correlation, Causation, KPIs, and Data Ethics