Nayanta University · Term 4, 2026

AI and Data Analytics

Everything for the course lives here. Reading pages go up as we cover each topic, so this page will keep growing. Bookmark it.

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What the course covers, and how it is graded
Outline

Course outline

The full outline: what we do each week, the readings, how the assessment works, and the policy on using AI in your submissions. Read the assessment section before the first quiz.

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Assignment results

Final · 18 September 2026

Try it yourself

Explore and experiment

Topics we have covered

One page per topic. More each week
Week 4

Data project ideas

Starting points for choosing a question and a workable dataset, with links to official sources, public mirrors, and data explorers. Also available as a printable PDF.

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Week 2

Summary statistics, and when they lie

Mean, median, mode, range, IQR and standard deviation. What each one means, when it is the right one to report, and the five ways a correctly computed summary can still tell a false story.

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Week 2

Could this just be luck?

The null hypothesis and the p-value, worked out from the bus-company question in the first quiz. What "statistically significant" does and does not mean, and why sample size changes everything.

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Week 2

Principles of data visualization

What makes a chart good: starting from a question, making the right comparison easy to see, graphical integrity, and showing uncertainty instead of hiding it.

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Also here

Not part of the weekly sequence

Regression activities

Straight-line regression, classification, logistic regression, and gradient descent, done by hand before any library.

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Archive

GenWise 2026 material

The resource index from the first run of this course, at GenWise in June 2026. We cover much of the same ground in a different order, so treat it as extra reading rather than as the syllabus.

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