Statistics with SciPy
Researchers who know which test they need and want it run, reported and defended in Python rather than clicked through a menu.
6 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- Compute and choose between measures of central tendency — mean, median, mode — and know when each lies
- Quantify spread — range, variance, standard deviation, IQR, MAD, coefficient of variation
- Describe shape — skewness and kurtosis — and connect them to a histogram
- Use percentiles, quantiles, and the five-number summary
- Standardise data with z-scores and explain what a z-score means
- Detect outliers with the IQR rule and the z-score rule
- Produce grouped summaries with groupby().agg() and read DataFrame.describe() critically
- Explain random variables and the difference between discrete and continuous distributions
Curriculum
6 sections · 6 lessons
Section 1
6 modules
Section 1
- Descriptive StatisticsSection 4 taught you to see data. Section 5 teaches you to measure it.~150 min
- Probability DistributionsDescriptive statistics summarise the data you have. Probability distributions are mathematical models for the data you might get — they describe how likely each possible value is.~150 min
- Hypothesis TestingYou now know how to summarise data (Notebook 38) and model randomness (Notebook 39). Hypothesis testing puts them together to answer the question every analyst is eventually asked: "Is this difference real, or could it just be chance?" We'll build the logic from scratch, run the workhorse tests with scipy.stats, and — just as importantly — learn the traps (p-hacking, misreading p-values, ignoring effect size) that make hypothesis testing the most misused tool in statistics.~180 min
- Correlation AnalysisHypothesis testing (Notebook 40) asked "do these groups differ?" Correlation analysis asks a different question: "do these two variables move together — and how strongly?" It is the quickest way to find relationships in a dataset, the backbone of feature selection, and the setup for regression (Section 6). It is also the single most misused idea in statistics, because a correlation is constantly mistaken for a cause.~150 min
- ANOVA (Analysis of Variance)A t-test compares two group means. But what if you have three or more?~180 min
- Non-parametric TestsThe t-tests and ANOVA of the last two notebooks assume your data is roughly normal with equal variances. Real data — skewed incomes, ordinal survey ratings, tiny samples, outlier-riddled measurements — often breaks those assumptions.~150 min
About this course
6 modules from the Python for Data Analysis curriculum.
This course covers Descriptive Statistics, Probability Distributions, Hypothesis Testing, Correlation Analysis, ANOVA (Analysis of Variance) and Non-parametric Tests.
About 16 hours of material across 6 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
- Practitioners who can already run the basics and want Statistics with SciPy to a standard they can defend
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- Comfort with the earlier tracks of Python for Data Analysis, or equivalent experience
- Your own dataset is welcome but not required — every module ships with the data it teaches from
Start Statistics with SciPy today
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