Data Science Basics – SS2 Digital Technologies Lesson Note (Second Term)

Data science is the practice of studying information (data) from the past to make better decisions for the future. It involves four stages: collecting the data, storing it safely, cleaning out mistakes, and analysing it to find patterns. This note covers each stage, the ethics of handling people’s data, and a class activity.

Behavioural objectives

By the end of the lesson, students should be able to:

• explain what data science is;
• describe four methods of collecting data;
• state three places where data can be stored;
• identify common problems in “dirty” data and say how to clean them;
• describe the stages of the data life cycle;
• explain why consent, security and anonymity matter when handling people’s data.

What is data science?

Imagine you are the owner of a small shop. If you know that every Friday, students buy more “Zobo” than “Coke,” you will make sure to prepare more Zobo on Friday mornings.

That is data science! It is the art of looking at information (data) from the past to make better decisions for the future. It involves four main stages: collecting the information, keeping it safe, making sure it is correct, and analysing it.

What is data collection?

Data collection is the process of gathering information from different sources. If you don’t have good data, you can’t make good decisions.

Methods of collection

• Surveys/questionnaires: Asking people questions directly (e.g., Google Forms).
• Observation: Counting things as they happen (e.g., counting how many cars pass the school gate).
• Sensors: Automatic machines that record data (e.g., a thermometer recording the temperature every hour).
• Web scraping: Using a computer to “copy” information from websites automatically.

Where is data stored?

Once you have collected your data, you need a place to put it. We organise it instead of throwing it all into one big pile.

Types of storage

1. Spreadsheets (Excel/Google Sheets): Great for small amounts of data that look like a table.
2. Databases (SQL): Used for giant amounts of data (like all the bank accounts in Nigeria). A database is like a very fast, digital filing cabinet.
3. The cloud: Storing data on the internet (like Google Drive) so you can access it from any computer in the world.

Teacher’s tip: Always have a backup! Data storage can fail (hard drives crash), so professional data scientists always keep copies in different places.

What is data cleaning?

Real-world data is usually “dirty.” This doesn’t mean it has sand on it! It means it has mistakes, missing parts, or duplicates. If you analyse dirty data, you will get the wrong answers.

Common “dirty” data problems

• Missing values: A student forgot to write their age on a form.
• Duplicates: The same person signed the attendance sheet twice.
• Inconsistent formatting: One person wrote “June 1st,” another wrote “01/06/2026,” and another wrote “1-Jun.”
• Outliers: Mistakes that don’t make sense (e.g., a student’s age recorded as 200 years old).

How we “clean” data

1. Removing duplicates: Deleting the extra copies.
2. Handling missing data: Either deleting the row or filling it with an average.
3. Standardising: Making sure all dates and names follow the same format.

What is the data life cycle?

Why does privacy matter in data science?

When we collect data, we are often collecting information about people. Three rules protect them:

• Consent: Always ask permission before taking someone’s data.
• Security: Keep personal data (like home addresses) locked so bad people can’t find it.
• Anonymity: When showing results, don’t show names. Say “50% of students,” not “Bisi and Obi failed.”

Class activity: the “Snack Survey”

1. Collection: Each student should ask 5 classmates what their favourite snack is. Write it down on a piece of paper.
2. Storage: Create a small table on your paper with two columns: “Name” and “Snack.”
3. Cleaning: Look at your list. Did anyone spell “Biscuits” wrong? Did anyone leave the snack column blank? Fix those errors now.
4. Analysis: Which snack is the most popular in your group?

Evaluation

1. What is data science? Give one example from daily life.
2. Name four methods of collecting data.
3. List three places where data can be stored.
4. What are “dirty” data problems? Name two and say how each can be fixed.
5. Why should you ask for consent before collecting someone’s data?

Assignment

Using the farm analogy, write a short paragraph explaining the four stages of the data life cycle. Then state one way to protect the privacy of the people whose data you collect.