IK
Chapter 6 of 9
~17 min read
Last reviewed July 17, 2026

Data Science and Gathering Data

Explore qualitative vs quantitative data, collecting data through surveys, visualizing data, SQL, and applying Python to data science tasks.

Written and reviewed by the IK Learning team

  • Qualitative vs Quantitative Data
  • Methods of Collecting Data
  • Visualizing Data (Charts & Graphs)
  • SQL & Databases
  • Using Python for Data Science

This chapter introduces data science fundamentals, including the difference between qualitative and quantitative data, methods for collecting data like surveys, and how to organize and visualize it using charts, SQL, and Python. It sets up the deeper data science topics covered in Class 10.

Chapter Introduction

What this chapter is about, and why it matters

Data science is what happens when the amount of information available is far larger than any person could read. The subject is not really about computers — it is about asking a clear question, collecting honest data to answer it, and presenting the answer so that someone can act on it.

The most important idea in this chapter is that data quality decides everything downstream. A beautiful chart built on a badly worded survey question is still wrong; it is just wrong in a convincing-looking way. That is why the sections on collection methods and question design matter more than they first appear.

You will also meet SQL here for the first time. Treat it as a language for asking questions of a table, not as programming — most of it reads almost like English once you know the four keywords.

What You Will Learn

The skills this chapter is assessed on

  • Distinguish qualitative from quantitative data and classify examples correctly.
  • Compare methods of collecting data and identify the strengths and weaknesses of each.
  • Choose an appropriate chart type for a given data set and justify the choice.
  • Explain what a database and a DBMS are, and write a simple SQL SELECT query.
  • Describe how Python is used in data science tasks.

Key Concepts Explained

8 core ideas — each with its definition and a separate worked example

Data and information

Definition

Data consists of raw, unprocessed facts and figures with no meaning on their own. Information is data that has been processed, organised or given context so that it becomes meaningful and useful.

Example

The list 72, 65, 88, 91, 54 is data — it could be anything. Told that these are test marks out of 100 for one student across five subjects, and that the class average is 70, it becomes information: the student is above average in three subjects.

Detailed Explanation

The exam distinction is that processing adds the meaning. This is also why the same data can produce different information depending on the question asked of it.

Qualitative data

Definition

Qualitative data describes qualities or characteristics that are observed and expressed in words or categories rather than numbers, and cannot be meaningfully measured on a numeric scale.

Example

Eye colour, favourite subject, a customer's written review, and the answers to "how did you find the exam?" are all qualitative. You can count how many people said "difficult", but "difficult" itself is not a number.

Quantitative data

Definition

Quantitative data consists of values that can be counted or measured numerically and on which arithmetic can meaningfully be performed.

Example

Height in centimetres, number of siblings, marks scored, and time taken to complete a task are quantitative. Calculating the average of these values produces something meaningful — which is the practical test.

Detailed Explanation

Be careful with numbers that are really labels. A student roll number is written as a digit but averaging roll numbers is meaningless, so it behaves as qualitative data. Ask "does arithmetic on this produce sense?" rather than "does it look like a number?"

Survey

Definition

A survey is a data collection method in which a set of predetermined questions is put to a sample of people in order to gather information about their opinions, behaviours or characteristics.

Example

Asking 200 students "how many hours per day do you spend on your phone?" with fixed ranges to choose from gathers quantitative data quickly and cheaply from a large group.

Detailed Explanation

Surveys scale well but are only as good as their wording. "Don't you agree that phones waste time?" is a leading question — it pushes respondents towards one answer and makes the resulting data worthless no matter how many people answered.

Choosing a chart type

Definition

Data visualisation is the presentation of data in a graphical form so that patterns, comparisons and trends become easier to understand than they would be in a table of numbers.

Example

A bar chart compares marks across five subjects. A line graph shows how one student's marks changed across six monthly tests. A pie chart shows what proportion of a class chose each optional subject.

Detailed Explanation

Match the chart to the question. Bar = comparison between categories. Line = change over time. Pie = parts of a single whole. A pie chart with fifteen slices, or one whose slices do not add up to a meaningful whole, is the wrong tool no matter how colourful it looks.

Database and DBMS

Definition

A database is an organised collection of related data stored so that it can be efficiently retrieved and updated. A DBMS (Database Management System) is the software that creates, manages, secures and provides access to that database.

Example

A school's student records are the database. MySQL is the DBMS — the program that stores the records safely, controls who may read them, and answers questions about them.

Detailed Explanation

Students often use the two words interchangeably. The database is the data; the DBMS is the software managing it. The same distinction as a library's books versus the librarian.

SQL

Definition

SQL (Structured Query Language) is the standard language used to store, retrieve, update and delete data held in a relational database.

Example

Retrieving the names of all students who scored more than 80:

SELECT name
FROM students
WHERE marks > 80
ORDER BY marks DESC;

Detailed Explanation

Read a SELECT query as a sentence: SELECT what you want, FROM which table, WHERE this condition holds. Almost every basic query is that shape, which is why SQL is unusually quick to learn compared with a programming language.

Python in data science

Definition

Python is a general-purpose programming language widely used in data science because its readable syntax and specialised libraries make it efficient for loading, cleaning, analysing and visualising large data sets.

Example

A researcher with a spreadsheet of 50,000 survey responses can use Python to remove blank rows, calculate the average per district, and produce a bar chart — in a script that runs in seconds and can be re-run when new data arrives.

Detailed Explanation

The re-runnable part is the real advantage. Cleaning data by hand in a spreadsheet has to be redone every time the data updates; a script does the same work again for free and records exactly what was done.

Where This Is Used in Real Life

The same ideas, outside the syllabus

How weather forecasts are produced

Thousands of sensors report temperature, pressure and humidity continuously. That raw data is quantitative and useless to read directly; models process it into a forecast, and a visualisation turns the forecast into a map you can understand in two seconds.

Why online shops recommend products

Every click and purchase is a data point. Pattern recognition across millions of customers produces "people who bought this also bought…". The technique is straightforward; the accuracy comes entirely from the volume and quality of the collected data.

Misleading charts

A bar chart whose vertical axis starts at 90 instead of 0 makes a tiny difference look enormous. Learning to check the axis before believing a graph is one of the most useful practical skills in this chapter.

Common Mistakes to Avoid

Errors that cost marks in this chapter, and the correction for each

Mistake

Classifying any number as quantitative data.

Correct Approach

Test whether arithmetic on it is meaningful. Roll numbers, phone numbers and postcodes are numeric labels, not quantities.

Mistake

Using a pie chart to show change over time.

Correct Approach

A pie chart shows parts of one whole at one moment. Change over time needs a line graph.

Mistake

Saying "database" when you mean "DBMS".

Correct Approach

The database is the stored data; the DBMS is the software that manages it. Definition questions test exactly this distinction.

Mistake

Writing SQL keywords in the wrong order.

Correct Approach

The order is fixed: SELECT, then FROM, then WHERE, then ORDER BY. A query with WHERE before FROM will not run.

Mistake

Forgetting the semicolon at the end of an SQL statement.

Correct Approach

Most systems require it to mark the end of the statement. It is a single character and a frequently dropped mark.

Mistake

Assuming a large sample automatically means good data.

Correct Approach

A leading or ambiguous question produces bad data from 10,000 people just as reliably as from 10. Question design comes before sample size.

Exam Preparation Tips

Technique specific to this chapter

  • For "qualitative or quantitative?" questions, state the classification AND a one-line reason. The reason is usually worth its own mark.
  • When asked to choose a chart, always justify the choice — "a line graph, because the data shows change over time" is a complete answer.
  • Write SQL keywords in capitals. It is not required by the language, but it makes the structure obvious to the marker and to you.
  • Learn one advantage and one disadvantage for each collection method (survey, interview, observation, existing records). Comparison questions are near-certain.
  • If a question gives you a data set, look at it before answering — the answer to "which chart?" is usually visible in the shape of the data.

Quick Revision Summary

The whole chapter in one screen — read this the night before

  • Data = raw facts. Information = processed data with meaning.
  • Qualitative = described in words or categories. Quantitative = counted or measured.
  • Test for quantitative: does arithmetic on it make sense?
  • Bar chart = compare categories. Line graph = change over time. Pie chart = parts of a whole.
  • Database = the stored data. DBMS = the software managing it.
  • Basic SQL: SELECT columns FROM table WHERE condition ORDER BY column;
  • Leading questions ruin survey data regardless of sample size.
  • Python is used in data science for cleaning, analysing and visualising data repeatably.

Glossary of Terms

Words used in this chapter, defined plainly

Sample
The subset of a population actually surveyed or measured.
Bias
A systematic error that pushes results consistently in one direction.
Record
One complete entry in a database table — usually one row.
Field
One item of data within a record — usually one column.
Query
A request for specific data from a database.
Raw data
Data as originally collected, before any cleaning or processing.

Practice Questions

Now test yourself on the concepts above. Collapse the answers to make it a real practice run.

Multiple Choice Questions with Explanations

7 MCQs — pick an option to check yourself, then read why the answer is right

1What is data?

Correct answer: B — Raw facts gathered about things

Data refers to raw, unprocessed facts gathered about things.

2Which of the following is an example of qualitative data?

Correct answer: C — Favourite ice cream flavours

Favourite ice cream flavours are non-numeric, descriptive (qualitative) data.

3How can you organise data to make it easier to analyse?

Correct answer: B — By creating tables, charts, and graphs

Tables, charts, and graphs make data easier to compare and interpret.

4What is the main purpose of data collection?

Correct answer: B — To gather information to answer questions or make decisions

Data collection gathers the information needed to answer questions or support decisions.

5What is the main purpose of data visualization?

Correct answer: C — To make data easier to understand by turning it into pictures

Data visualization turns raw data into visual formats that are easier to understand.

6Which tool is specifically built for creating detailed and interactive visualizations?

Correct answer: C — Tableau

Tableau is a dedicated data-visualization tool for interactive dashboards and charts.

7What is the first step in the data science process?

Correct answer: D — Understanding the problem

The data science process begins with understanding the problem, so you know exactly what question you are trying to answer before any data is collected, cleaned, or analysed.

Short Questions with Answers

9 short-answer questions

Qualitative data is descriptive, non-numeric, and represents categories or qualities, like names or types of fruit. Quantitative data is numeric, measurable, and countable, such as heights, test scores, or number of students.
A survey or questionnaire works best for gathering opinions from a large group. These methods let people share their views in a structured, easy-to-analyse way.
The number of students counts as quantitative data because it is numeric, measurable, and can be counted to analyse patterns or totals.
Organising data into tables or charts makes it easier to understand, cuts down on errors, and allows quick comparison and interpretation of information.
Data visualization turns numbers and information into charts or graphs, making it easier to spot trends, patterns, and relationships that might not be obvious in raw data.
Line graphs show changes or trends over time, helping to visualize how values rise or fall in a sequence.
Microsoft Power BI helps build interactive charts, graphs, and dashboards, making complex data easy to understand and analyse for better decision-making.
Python is used in data science to manipulate, analyse, and visualize data using libraries such as Pandas for data handling and Matplotlib for creating charts and graphs.
SQL, or Structured Query Language, is used to manage and query databases, letting users extract, update, and analyse large sets of structured data efficiently.

Long Questions with Detailed Answers

6 in-depth answers

Qualitative Data: - Qualitative data refers to categories or labels used to describe the qualities or characteristics of something rather than its quantity. - This type of data offers a way to categorize and gives insight into opinions, behaviours, and experiences through descriptions rather than numbers. - Key characteristics include non-numeric, descriptive, and categorical attributes. - Qualitative data is represented by words, labels, or symbols instead of numbers. - It describes attributes rather than quantities. - It can be split into categories or classes based on their characteristics. Quantitative Data: - Quantitative data consists of numbers used to measure the quantity or amount of something. - These data types answer questions like "How much?" or "How long?" and are useful for mathematical calculations and statistical analyses. - Key characteristics include being numerical, measurable, countable, and arithmetical. - Quantitative data is expressed in numbers, representing a measurable quantity. - It can be measured using instruments or tools. - It can be counted or enumerated and used in arithmetic operations.
To conduct a survey about students' favourite extracurricular activities, start by deciding what you want to find out, such as which activities are most popular and how much time students spend on them. Next, write simple and clear questions, like "Which activity do you enjoy the most?" or "How many hours per week do you spend on it?" You can use online tools like Google Forms, Microsoft Forms, or SurveyMonkey, or make a paper survey. Before sending it to everyone, test the survey with a few students to make sure the questions are easy to understand. Then, share the survey with your classmates or a small group and collect their responses over a set time. After collecting the data, organise it in a spreadsheet or table and check for any mistakes or missing information. Finally, analyse the data to spot patterns and trends. You can create charts or graphs like bar charts or pie charts to show which activities are most popular. Present your findings clearly to the class, explaining what the data shows and any interesting observations.
Benefits - Allows quick creation of surveys with multiple question types (multiple choice, rating scales, checkboxes). - Responses are automatically collected and stored, cutting down manual errors. - Participants can respond from anywhere, making it easier to reach a large and diverse group. - Data can be exported to spreadsheets for further analysis. - Saves time and helps organise large datasets efficiently. Challenges - Requires internet access, so participants without it may be excluded. - Some tools have limited customization for complex surveys. - Response bias may occur if anonymity isn't guaranteed. - Large datasets may need additional tools for proper analysis.
Tables and charts play an important role in data analysis by organising and presenting data in a clear, understandable way. Tables - Organise data in rows and columns for easy comparison. - Help summarise information clearly. - Example: A table showing students' scores in Math, Science, and English. Charts/Graphs - Visual representations that make patterns and trends easier to see and understand. - Bar chart: Compare the number of students in each score range. - Pie chart: Show the percentage of students in different extracurricular activities. - Line graph: Track changes over time, e.g., performance trends across months. These visual tools help interpret data quickly and support decisions.
The process of turning unprocessed data into visual representations like charts, graphs, dashboards, and infographics is known as data visualization. It plays a key role in simplifying the interpretation and understanding of complex data. Importance - Turns raw data into visual formats like charts, graphs, dashboards, and infographics. - Makes large or complex datasets easier to understand. - Helps spot trends, patterns, relationships, and outliers. Benefits - Speeds up understanding of information. - Supports better decision-making. - Improves communication of insights to team members or stakeholders. - Example: Sales trends can be seen quickly using a line graph or bar chart.
Charts, graphs, and dashboards are essential tools for businesses and decision-makers because they help turn large, complex data into simple, visual formats that are easy to understand and interpret quickly. Why Businesses Use Them? - Turn complex data into clear, actionable insights that support a better understanding of business performance. - Track Key Performance Indicators (KPIs) in real time via dashboards, helping businesses monitor progress effectively. - Spot trends and patterns, such as seasonal sales changes, customer behavior, and market performance. - Compare datasets across teams, regions, products, or time periods for better evaluation and decision-making. - Improve internal communication, since visuals are easier to understand than long spreadsheets or raw data. - Support strategic planning, problem-solving, and resource allocation, making businesses more efficient, organized, and competitive. - Enable faster decision-making by presenting data in a simplified, summarized visual form.

Important Questions for Revision

5 high-priority questions

Qualitative data is descriptive and non-numeric (e.g., favourite colours); quantitative data is numeric and measurable (e.g., test scores).
Data visualization turns raw numbers into charts and graphs, making trends, patterns, and relationships easier to spot and understand.
SQL (Structured Query Language) is used to manage and query databases, letting users extract, update, and analyse structured data.
Bar charts compare quantities across categories; pie charts show proportions of a whole; line graphs track changes/trends over time.
Python is used to manipulate, analyse, and visualize data using libraries like Pandas (data handling) and Matplotlib (charts and graphs).

Frequently Asked Questions

6 quick answers to common questions about this chapter

Qualitative data describes qualities or categories using words, such as favorite colors or types of fruit. Quantitative data is numeric and measurable, such as test scores or heights. The key difference is that quantitative data can be counted and used in calculations, while qualitative data cannot.
Not quite. SQL is a specialized query language built specifically for retrieving, adding, updating, and deleting data in a database — it isn't designed for general-purpose tasks the way Python is. Many data science projects actually use both together: Python for analysis and SQL for pulling data out of a database.
A weather app showing a week's temperatures as a line graph instead of a long list of numbers is data visualization in action — it lets you instantly see whether the week is getting hotter or colder, something that would take much longer to notice from raw numbers alone.
Real-world data collected through surveys or sensors often has mistakes, missing values, or inconsistent formatting. If this messy data is analyzed as-is, the conclusions drawn from it can be wrong or misleading — cleaning it first ensures the analysis is based on accurate, trustworthy information.
Yes. Qualitative vs quantitative data, data collection methods, visualization tools, SQL basics, and using Python for data science are all part of the Class 9 syllabus and appear in exam questions.
A bar chart compares separate categories against each other using bars of different heights, making it easy to see which category has more or less. A pie chart shows how a whole is divided into parts as percentages, making it better suited to showing proportions rather than direct comparisons.

Chapter Test

7 questions with the answers hidden — check what you actually remember

You have just read the explanations above. This checks whether they stuck. The answers stay hidden until you finish, so it is closer to exam conditions than scrolling through the notes again.

  • 7 questions, one at a time — no time limit.
  • You can move back and change an answer before submitting.
  • Afterwards you get your score, every explanation, and what to re-read.

Your score is saved in this browser only. No account, nothing sent anywhere.

Related Topics in the Other Class

Class 10Data Science