Learn Statistics With Python
Compute and communicate summary statistics with Python
Learn Statistics With Python is a beginner-friendly course that teaches descriptive statistics using Python and the NumPy library. It covers core techniques—mean, median, mode, variance, and standard deviation—and practical visualizations such as histograms, quartiles, and boxplots.
The course emphasizes hands-on data analysis through guided projects and quizzes to help learners interpret datasets and communicate insights effectively. Interactive exercises and AI-assisted coding feedback reinforce measurable skills so learners can compute, analyze, and present summary statistics with confidence.
At a Glance
Learn Statistics with Python is a beginner-focused course offered by Codecademy that teaches descriptive statistics using Python and NumPy.
It emphasizes practical calculation and interpretation of central tendency, spread, and basic visualizations so learners can describe and analyze real datasets.
| Level | Beginner |
| Rating | 4.6 out of 5 |
| Duration | 4+ hours |
| Languages | English |
| Learners | 10K+ learners |
| Certificate | Certificate of completion (available with Plus or Pro) |
| Access | Access for as long as your subscription is active |
| Course includes |
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| Price | Included with subscription (monthly or annual plans) |
What This Course Teaches
The course frames outcomes as concrete, measurable competencies you can demonstrate with Python and NumPy in real-data contexts.
Learners are expected to compute and interpret core summary statistics, assess distributional spread, and communicate findings using concise metrics and simple visualizations.
How the Course Is Structured
The course is organized into 11 short lessons with a compact, module-like flow and includes integrated projects and quizzes; the overall estimated completion time is about 4 hours.
This setup favors short, focused lessons with applied projects to reinforce learning through practice rather than long lectures.
Curriculum overview
01Mean, Median, and Mode▾
Learn to use Python packages or manually calculate the mean, median, and mode of real-world datasets.
02Variance and Standard Deviation▾
Learn how to quantify dataset spread by calculating variance and standard deviation using Python.
03Histograms▾
Learn how to visualize and interpret a dataset using histograms to reveal distributional features.
04Describe a Histogram▾
Practice describing a distribution by considering its center, shape, spread, and outliers.
05Quartiles, Quantiles, and Interquartile Range▾
Calculate quartiles, quantiles, and the interquartile range to summarize data spread robustly.
06Boxplots▾
Create and interpret boxplots across different datasets to identify central tendency and outliers.
Audience & Requirements
Learn Statistics With Python is intended for beginners and early-career learners who want hands-on competence in summarizing and visualizing data using Python.
Ideal learners include aspiring data analysts, students needing practical statistics for coursework, professionals who must summarize datasets for decisions, and hobbyists who work with real-world data such as weather or travel logs.
- Aspiring data analysts seeking foundational, applied statistics skills.
- Students who need practical tools to summarize coursework datasets.
- Professionals who must analyze and report on business or operational data.
- Hobbyists interested in analyzing real-world datasets (e.g., weather, travel).
- No special prerequisites — the course is labeled for beginners.
Final Verdict
Learn Statistics with Python delivers a concise, practice-focused introduction to descriptive statistics using Python and NumPy.
Given its strong platform rating and wide learner adoption, alongside hands-on projects and AI-assisted guidance, the course is a high-value, low-friction option for beginners who want to gain practical competency quickly.
Recommend this course for students, aspiring data analysts, and professionals who need reliable, applied summary-statistics skills; those seeking deeper inferential statistics or extensive theory should plan a follow-up, more advanced course.
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