Ниже представлен обзор направлений в изучении основ работы с данными. Информация носит описательный характер.
Getting Data Ready
Raw data is messy. These materials walk through the typical cleanup steps and explain why skipping them tends to bite you later.
The focus stays on the logic behind each step, not on specific software commands. Read it as a mental checklist.
Statistics Without the Pain
Averages, spread, correlation, uncertainty. The intuition first, formulas kept out of the way.
Enough to read a report and spot when a number is doing more work than it should. That's the goal here.
Reading Results Carefully
A number rarely speaks for itself. Context matters - sample size, how the data was collected, what was left out.
These materials walk through common traps: confusing correlation with cause, overreading small samples, ignoring the messy footnotes.
Showing It Visually
Which chart fits which question. Bars, lines, scatter plots - each has a job, and picking the wrong one quietly misleads people.
We also cover honesty in visuals: truncated axes, cherry-picked ranges, misleading color scales. Small choices, big consequences.
Data Fundamentals
What counts as data. Where it comes from, why formats differ, and how people usually structure it before doing anything useful.
A plain-language intro. Nothing technical yet, just the shape of the field so the rest makes sense.
Tools Landscape
A map of what's out there. Spreadsheets, notebooks, dashboards, statistical environments - each category has a rough purpose.
No step-by-step for any specific product. Just enough context so you know which family of tools fits which kind of task.