Mission
Build a solid Python workflow for analyzing data, starting with core language skills and moving into the libraries and techniques used in real analysis work. This sequence goes deep enough to cover practical tools, data cleaning, exploration, and communication-ready outputs.
Python for data analysis
~14 hours · 5 courses
Course 1 · ~150 min
Write small Python programs, use variables and functions confidently, and read code needed for analysis examples
A strong Python foundation makes it much easier to learn analysis libraries and understand examples without getting stuck on syntax.
Course 2 · ~180 min
Load datasets into pandas, inspect structure, filter and transform data, and produce clean summary tables
Pandas is the core tool for most Python data analysis workflows, so this step turns Python basics into usable analysis skills.
Course 3 · ~170 min
Clean messy datasets, identify data quality problems, and apply repeatable preprocessing steps before analysis
Analysis quality depends on data quality, and most real datasets need cleanup before any meaningful conclusions can be drawn.
Course 4 · ~190 min
Explore datasets with summary statistics and create clear charts that reveal trends, patterns, and relationships
Exploratory analysis helps you understand what the data is really saying before moving into interpretation or reporting.
Course 5 · ~160 min
Turn a business or research question into an analysis plan, interpret results cautiously, and summarize findings clearly
This final step connects technical analysis to decision-making, which is the real purpose of learning data analysis with Python.