Chapter outline · Utopiajoy

Learn data science, plainly.

Thirty concept cards for beginners, arranged into five chapters. Each one explains the idea, why it matters for analysis, and the mistake people usually make — in plain language, with no invented numbers.

01Getting Started

6 entries →

The first steps into data science: what it really is, how a project runs, and the order to learn things in.

#	Concept	Level	Area
01	What Data Science Actually Is	Beginner	Basics
06	Your First Data Project	Beginner	Basics
11	Learning the Right Order	Beginner	Basics
16	Tools You Actually Need	Beginner	Tools
21	The Data Science Workflow	Beginner	Basics
26	Finding Practice Datasets	Beginner	Basics

02Data Basics

6 entries →

Working with raw data — what data means, how tables are shaped, and how to clean what you are given.

#	Concept	Level	Area
02	What 'Data' Really Means	Beginner	Data
07	Cleaning Messy Data	Intermediate	Data
12	Missing Values and Outliers	Intermediate	Data
17	Rows, Columns, and Types	Beginner	Data
22	Joining and Reshaping Data	Intermediate	Data
27	A Checklist Before Analysis	Intermediate	Data

03Analysis & Stats

6 entries →

Making sense of numbers: descriptive statistics, averages, correlation, sampling, and reading results honestly.

#	Concept	Level	Area
03	Descriptive Statistics, Simply	Beginner	Stats
08	Averages Can Mislead	Intermediate	Stats
13	Correlation vs Causation	Intermediate	Stats
18	Sampling and Bias	Intermediate	Stats
23	Probability Basics	Beginner	Stats
28	Interpreting Results Honestly	Intermediate	Stats

04Visualization

6 entries →

Turning numbers into pictures people understand — choosing the right chart and avoiding the misleading ones.

#	Concept	Level	Area
04	Choosing the Right Chart	Beginner	Viz
09	Making Charts People Understand	Intermediate	Viz
14	Telling a Story With Data	Intermediate	Viz
19	Avoiding Misleading Charts	Intermediate	Viz
24	Exploring Data Visually	Beginner	Viz
29	A Dashboard People Use	Intermediate	Viz

05Machine Learning Intro

6 entries →

A gentle introduction to machine learning: the core idea, the two flavours, and how to evaluate a model fairly.

#	Concept	Level	Area
05	Machine Learning, Plainly	Beginner	ML
10	Supervised vs Unsupervised	Beginner	ML
15	Training and Testing	Intermediate	ML
20	Overfitting and Generalization	Intermediate	ML
25	Features That Matter	Intermediate	ML
30	When ML Is (Not) Needed	Intermediate	ML