Data Analytics Path
A route for learners who want to understand data, clean it, visualize it, query it, test claims, and write careful evidence-based reports.
Use this path when it matches the work you want to do
Choose this path if your goal is dashboards, reports, business analysis, statistics, SQL, or preparing data for AI work.
Read the skip rule
Do not jump into machine learning before you can explain the question, denominator, sample, missing data, and limits of a chart or table.
Read the path outcome
A reproducible evidence workflow with clean data, clear charts, cautious statistics, SQL checks, and a short decision-ready report.
Follow the route in this order
The order keeps prerequisites clear. If a course is already comfortable, use its detail page to confirm the syllabus before skipping it.
Basic Statistics for Data Analysis
Learn variables, bias, distributions, uncertainty, tests, effect sizes, correlation, and causation.
Python Foundations for AI or R Foundations for Data Analysis
Pick the language or tool route that matches your work. You do not need to complete both branches unless your goals require both.
Data Analysis and Visualization with Python
Clean, inspect, join, summarize, visualize, and report on datasets using the Python data stack.
Data Visualization and Dashboard Storytelling
Create readable charts, dashboard stories, captions, accessibility notes, and limitations.
SQL for Data Analysis and AI
Query, join, aggregate, validate, and prepare structured data for analytics and AI workflows.
Statistical Data Analytics with Python or Statistical Data Analytics with R
Pick the language or tool route that matches your work. You do not need to complete both branches unless your goals require both.
What this route includes
- Basic Statistics for Data Analysis
- Python Foundations for AI or R Foundations for Data Analysis
- Data Analysis and Visualization with Python
- Data Visualization and Dashboard Storytelling
- SQL for Data Analysis and AI
- Statistical Data Analytics with Python or Statistical Data Analytics with R
Start the first course, then continue through the path
A reproducible evidence workflow with clean data, clear charts, cautious statistics, SQL checks, and a short decision-ready report.