STEM · Full roadmap · ~105 min read · 33 steps

📊Data Science (Aotearoa NZ)

Read NZ data, spot real patterns, respect the rules, and build a first model

Activities in this path

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Learning path

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What data science actually is

The workflow, start to finish

Why the question comes first

Types of data, numerical and categorical

Structured versus unstructured data

The middle of the data, mean, median, mode

How spread out is the data, range, variance, standard deviation

Percentiles and the median's friends

The normal distribution

Skew, when the bell tips over

Data cleaning is most of the job

Missing values

Outliers

Exploratory data analysis

Choosing the right chart

Reading a chart without being fooled

Correlation versus causation

Sampling and bias

A little probability intuition

Hypotheses and p-values, honestly

What machine learning is

Train, test, and overfitting

Linear regression intuition

Classification by nearest neighbours

Judging a model, accuracy can lie

NZ data sources to practise with

The Privacy Act 2020 and notifiable breaches

Māori data sovereignty and the CARE principles

The Algorithm Charter and the IDI

The NZ data and tech job scene

The tools, and your first project

Where to go next

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