Science behind Data

Children are handed numbers constantly — in news, in advertising, in school textbooks, in social media — and are almost never taught how to interrogate them. A percentage sounds authoritative. An average hides more than it shows. A truncated axis on a graph can make a flat line look like a crisis.

This course teaches data science from the ground up for Class 6 to 10: what data is, how it is collected and cleaned, how averages and spread actually behave, how to build honest charts, how to read probability, and how to spot the manipulations that get used on adults every day.

By the end, students run a small investigation of their own — ask a question, collect real data, analyse it in a spreadsheet, chart it and present the finding, including the parts that did not support their hypothesis.

1 What Data Is View

•Data, information and insight, and the difference between them

•Types of data: numbers, categories, text, images

•Where data comes from and who creates it

•Data in daily life: school records, cricket scores, weather, prices

2 Collecting Data View

•Asking a question that data can actually answer

•Surveys, observation and experiments

•Samples and why a sample can mislead

•Bias in how a question is worded

3 Cleaning and Organising Data View

•Recording data in a table, rows and columns

•Missing values, duplicates and wrong entries

•Outliers, and when to keep or remove them

•Sorting, filtering and grouping in a spreadsheet

4 Making Sense of Numbers View

•Mean, median and mode, and when each misleads

•Range and spread: why the average is not the whole story

•Percentages, ratios and rates

•Comparing two groups fairly

5 Charts and Visualisation View

•Choosing the right chart: bar, line, pie, scatter

•Building charts in a spreadsheet

•How axes, scales and colour change the story

•Spotting a misleading graph in the wild

6 Chance and Probability View

•Certain, likely, unlikely, impossible

•Simple probability with coins, dice and cards

•Why streaks and coincidences feel meaningful

•Correlation and causation, with real examples

7 Data, Computers and AI View

•How computers store and process data

•What a dataset looks like to a machine

•How AI systems learn from data, in plain terms

•Where AI goes wrong when the data is bad

8 Data and Fairness View

•Privacy: what your data says about you

•Who owns data collected about children

•Bias in data and unfair conclusions

•Being honest with your own findings

9 Capstone Investigation View

•Choosing a question worth investigating

•Collecting and cleaning real data

•Analysing and charting the result

•Presenting the conclusion, including what disproved the hypothesis

Google SheetsMicrosoft ExcelCharts and pivot tablesPublic open datasetsSimple probability simulations