Data Science with Python: Stats & Visualization
Practical stats, plotting, and simple models in Python using real-world datasets.
What you’ll learn
- Build end-to-end data analysis workflows in Python
- Clean messy datasets and handle missing values confidently
- Create publication-ready visualizations with matplotlib & seaborn
- Run hypothesis tests and interpret statistical results
- Build and diagnose simple regression models
Curriculum
M1
Data, variables & sampling — 2 lessons
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Python data stack: NumPy, pandas, Jupyter essentials16m
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Data types, structures & memory efficiency14m
M2
Visualizing distributions — 3 lessons
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Loading & inspecting real datasets14m
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Cleaning: missing values, duplicates & outliers16m
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Reshaping: pivot, melt, merge & joins16m
M3
Summaries & data cleaning — 3 lessons
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Matplotlib foundations & figure anatomy14m
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Seaborn: statistical plots made simple16m
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Customizing plots for presentations14m
M4
Probability & discrete models — 2 lessons
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Distributions: histograms, KDE & boxplots16m
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Relationships: scatter, correlation heatmaps16m
M5
Normal model & CLT — 2 lessons
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Descriptive stats review: center, spread & shape14m
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Probability refresher & sampling distributions16m
M6
Estimation & intervals — 2 lessons
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Hypothesis testing workflow with Python16m
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t-tests, chi-square & ANOVA in practice18m
M7
Tests & p-values — 2 lessons
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Simple linear regression from scratch16m
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Model diagnostics & residual analysis16m
M8
Comparisons & counts — 2 lessons
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Feature engineering & transformation16m
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Train/test splits & cross-validation intro14m
M9
Correlation & regression — 2 lessons
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Time series basics: trends & seasonality16m
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Rolling windows & simple forecasting14m
M10
Review & practice — 2 lessons
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Interactive viz with Plotly (intro)14m
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Building a data dashboard template16m
M11
Module 11 — 2 lessons
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Case study I: Sales analysis end-to-end18m
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Case study II: A/B test interpretation18m
M12
Module 12 — 2 lessons
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Publishing reports with Jupyter/Markdown14m
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Best practices: reproducibility & documentation14m
Projects
- Analyze a real sales dataset and deliver an executive summary
- Design an A/B test and interpret the results with proper stats
- Build an interactive dashboard for a business use case
Prerequisites
- Basic Python syntax (variables, loops, functions)
- High-school algebra and intro statistics helpful but not required
- Jupyter Notebook or similar environment installed
Who is this for?
- Students learning data science for the first time
- Analysts moving from Excel to Python
- Developers adding data analysis to their toolkit
Outcomes
- Confident cleaning and visualizing real datasets
- Able to run and interpret common statistical tests
- Ready for machine learning or advanced analytics courses
Resources
- Starter project / template (ZIP)
- Setup & study checklists (PDF)
- Core formulas / syntax cheatsheet (PDF)
Tip: Right-click → “Save link as…” if your browser opens the file.
FAQ
Is this beginner-friendly?
Yes. We start from mental models and build up with guided practice and plenty of worked examples.
Will I need extra software?
We stick to free or standard tools. Any optional extras are clearly marked and have alternatives.
Do I get updates?
Yes—lifetime access with updates as the field, tools, and exam expectations evolve.