Data Analysis and Visualization with Python
Learn the practical data workflow from NumPy arrays and pandas tables through cleaning, joins, time analysis, Matplotlib, Seaborn, Plotly, and a final analysis portfolio.
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Review prerequisite
Start immediately without an account. Progress is browser-local; notebooks and synthetic datasets are in the downloadable course pack.
Learn analysis before modelling
This course fills the gap between knowing Python syntax and using data responsibly. You learn to ask questions, clean tables, summarize evidence, create charts, and write a reproducible report before moving into machine learning.
Use NumPy and pandas to load, inspect, clean, transform, join, and summarize synthetic datasets.
Create clear static, statistical, and interactive charts with Matplotlib, Seaborn, and Plotly.
Build a final analysis portfolio with a data dictionary, cleaning log, chart critique, report, and limitations.
What you need before starting
Python Foundations for AI or equivalent beginner Python skill: variables, lists, dictionaries, functions, files, and notebooks.
45-65 hours across twelve modules, practice notebooks, worked notebooks, and a capstone report.
Static lessons plus downloadable Jupyter notebooks, synthetic CSV datasets, templates, and local setup instructions.
Twelve-module course sequence
The order is deliberate: analysis mindset, NumPy, pandas, cleaning, summaries, joins, time, EDA, static charts, statistical charts, interactivity, and capstone story.
Data Analysis Mindset and Notebook Workflow
- Turn a vague topic into a clear data question and notebook plan.
- Practice with synthetic datasets and a worked notebook.
NumPy Foundations for Data Work
- Use arrays, masks, vectorized operations, aggregation, and simulation.
- Practice with synthetic datasets and a worked notebook.
Pandas Core Objects and Data Import
- Load, inspect, document, and safely export tabular data.
- Practice with synthetic datasets and a worked notebook.
Selecting, Filtering, Sorting, and Creating Columns
- Build analysis tables with visible rules and derived columns.
- Practice with synthetic datasets and a worked notebook.
Cleaning Messy Data
- Handle missing values, duplicates, dates, text, and outliers with a cleaning log.
- Practice with synthetic datasets and a worked notebook.
Grouping, Aggregation, Pivoting, and Reshaping
- Create summaries, pivots, and long-form tables with clear grain.
- Practice with synthetic datasets and a worked notebook.
Combining Datasets Safely
- Join datasets with key checks, missing-match review, and row-count reconciliation.
- Practice with synthetic datasets and a worked notebook.
Time Series and Rolling Analysis
- Parse dates, resample by period, and use rolling averages without forecasting claims.
- Practice with synthetic datasets and a worked notebook.
Exploratory Data Analysis and Data Quality
- Explore distributions, relationships, segments, missingness, and limits.
- Practice with synthetic datasets and a worked notebook.
Matplotlib and Visualization Foundations
- Create clear static charts with labels, layout, export, and text equivalents.
- Practice with synthetic datasets and a worked notebook.
Statistical and Presentation Visualization with Seaborn
- Use statistical visual patterns carefully across relationships, distributions, and groups.
- Practice with synthetic datasets and a worked notebook.
Interactive Visuals, Dashboards, and Capstone Story
- Use Plotly interactivity and package the final analysis portfolio.
- Practice with synthetic datasets and a worked notebook.
Start the course now
The first release is downloadable-first. The local pack includes all lessons, datasets, practice notebooks, worked notebooks, learner templates, and a runtime-check notebook.