Explore the 9th class new syllabus solved exercises on data science and data gathering. Understand key concepts, definitions, and examples with easy explanations. Ideal for students preparing for exams!
MCQ 1
Statement: What is data?
Options:
a) Processed information
b) Raw facts gathered about things
c) A collection of numbers only
d) A list of observed events
Answer: b) Raw facts gathered about things
Explanation:
Data refers to raw, unprocessed facts that are collected about objects, events, or people. These facts can later be processed to generate meaningful information.
Tip & Trick:
- Data is raw and unprocessed, while information is processed and meaningful.
- Numbers, text, symbols, and images can all be data.
MCQ 2
Statement: Which of the following is an example of qualitative data?
Options:
a) Temperature readings in degrees Celsius
b) Number of students in a class
c) Favourite ice cream flavours
d) Test scores out of 100
Answer: c) Favourite ice cream flavours
Explanation:
Qualitative data describes attributes, characteristics, or categories. It is non-numerical and usually represents opinions, preferences, or labels (e.g., colours, names, or flavours).
Tip & Trick:
- Qualitative = Quality (Descriptive)
- Quantitative = Quantity (Numerical)
MCQ 3
Statement: What type of data involves distinct, separate values that are countable?
Options:
a) Nominal Data
b) Ordinal Data
c) Discrete Data
d) Continuous Data
Answer: c) Discrete Data
Explanation:
Discrete data consists of distinct and separate values that can be counted (e.g., number of students, number of books). It cannot be divided into smaller parts meaningfully.
Tip & Trick:
- Discrete = Distinct (Fixed numbers like 1, 2, 3, …)
- Continuous = Can be divided (e.g., height, weight, time)
MCQ 4
Statement: What is an example of continuous data?
Options:
a) Number of cars in a parking lot
b) Height of students in centimetres
c) Types of fruits
d) Shirt sizes (small, medium, large)
Answer: b) Height of students in centimetres
Explanation:
Continuous data can take any value within a range and can be measured with precision. Heights, weights, and temperatures are examples of continuous data.
Tip & Trick:
- Continuous = Can be measured & has decimals
- Example: A person’s height can be 165.5 cm or 170.2 cm
MCQ 5
Statement: What type of data is used to categorize items without implying any order?
Options:
a) Ordinal Data
b) Discrete Data
c) Nominal Data
d) Continuous Data
Answer: c) Nominal Data
Explanation:
Nominal data categorizes things without any ranking or order (e.g., gender, blood groups, eye colour). Unlike ordinal data, it does not indicate a sequence.
Tip & Trick:
- Nominal = Names only, No order
- Example: Car brands (Toyota, Honda, Ford)
MCQ 6
Statement: How can you organise data to make it easier to analyse?
Options:
a) By writing it in long paragraphs
b) By creating tables, charts, and graphs
c) By storing it in random files
d) By keeping it in a messy notebook
Answer: b) By creating tables, charts, and graphs
Explanation:
Organizing data visually in tables, graphs, or charts helps in analysis, making patterns and trends easier to understand.
Tip & Trick:
- Use tables for structured data
- Use graphs for trends and comparisons
MCQ 7
Statement: Which tool can be used to create surveys online?
Options:
a) Microsoft Word
b) Google Forms
c) Excel Spreadsheets
d) Adobe Photoshop
Answer: b) Google Forms
Explanation:
Google Forms is a free tool by Google that allows users to create surveys, quizzes, and forms for data collection. It provides easy-to-use templates and automatic response collection.
Tip & Trick:
- Microsoft Word is for document creation.
- Excel is for data analysis, not direct survey creation.
- Adobe Photoshop is for image editing.
MCQ 8
Statement: What is the main purpose of data collection?
Options:
a) To create random numbers
b) To gather information to answer questions or make decisions
c) To delete old data
d) To format text documents
Answer: b) To gather information to answer questions or make decisions
Explanation:
Data collection helps in making informed decisions, conducting research, and solving problems by gathering relevant facts and figures.
Tip & Trick:
- Data collection is the foundation of research, business analysis, and decision-making.
MCQ 9
Statement: What is the primary purpose of data visualization?
Options:
a) To generate random numbers
b) To convert text into data
c) To make data easier to understand by turning it into pictures
d) To hide complex data
Answer: c) To make data easier to understand by turning it into pictures
Explanation:
Data visualization transforms raw data into charts, graphs, and infographics, making it easier to identify patterns and trends.
Tip & Trick:
- Examples: Bar charts, pie charts, histograms.
- Data visualization = Simplifying data with visuals
MCQ 10
Statement: Which tool is specifically designed for creating detailed and interactive visualizations?
Options:
a) Microsoft Excel
b) Google Sheets
c) Tableau
d) PowerPoint
Answer: c) Tableau
Explanation:
Tableau is a powerful data visualization tool used for creating interactive and insightful dashboards and reports.
Tip & Trick:
- Excel & Google Sheets offer charts but lack advanced interactivity.
- PowerPoint is for presentations, not data analysis.
MCQ 11
Statement: What is the first step in the data science process?
Options:
a) Data Cleaning
b) Data Analysis
c) Data Collection
d) Understanding the problem
Answer: d) Understanding the problem
Explanation:
Before collecting or analyzing data, it is crucial to understand the problem to determine what data is needed and how it should be used.
Tip & Trick:
- Know the goal first!
- The next steps: Data Collection → Cleaning → Analysis → Interpretation.
MCQ 12
Statement: What does the ‘Volume’ characteristic of Big Data refer to?
Options:
a) The speed at which data is generated
b) The different forms data can take
c) The sheer amount of data being collected
d) The way data is processed
Answer: c) The sheer amount of data being collected
Explanation:
Big Data is characterized by Volume (large amounts of data), Velocity (fast processing), and Variety (different data types).
Tip & Trick:
- Big Data = Too big for traditional processing!
- Example: Social media data, online transactions.
MCQ 13
Statement: What is an outlier in a dataset?
Options:
a) The most frequent value
b) The average of all values
c) An unusual or extreme value that doesn’t fit the pattern
d) The middle value when all values are arranged in order
Answer: c) An unusual or extreme value that doesn’t fit the pattern
Explanation:
Outliers are data points that are significantly different from others in a dataset. They can result from errors or unique variations.
Tip & Trick:
- Example: If most students score between 60-80 in a test, a score of 10 or 99 could be an outlier.
- Identify outliers using box plots or standard deviation analysis.
MCQ 14
Statement: What does data encryption do?
Options:
a) It converts data into a code to prevent unauthorized access.
b) It makes data available to everyone online.
c) It automatically deletes old data.
d) It speeds up internet connection.
Answer: a) It converts data into a code to prevent unauthorized access.
Explanation:
Data encryption secures information by converting it into an unreadable format, which can only be decoded with a key or password.
Tip & Trick:
- Encryption = Locking data with a key!
- Common encryption methods: AES, RSA.
Q1: What is the difference between qualitative and quantitative data?
Answer:
Qualitative data describes qualities or characteristics, while quantitative data consists of numerical values that can be measured or counted.
Explanation:
- Qualitative data includes categories, names, labels, or descriptions (e.g., eye color, favorite food).
- Quantitative data includes numerical values (e.g., height, weight, test scores).
Key Words: Qualitative = Descriptive, Quantitative = Numerical
Q2: Give an example of continuous data and explain why it is considered continuous.
Answer:
Example: Height of students in a class (e.g., 165.5 cm, 172.3 cm).
It is considered continuous because it can take any value within a range and can be measured with decimal precision.
Explanation:
Continuous data can be broken down into smaller parts and still retain meaning (e.g., temperature, time, speed).
Key Words: Measured, Decimal values, Range
Q3: Which method would you use to collect opinions from a large group of people about a new school policy?
Answer:
An online survey using Google Forms or paper-based questionnaires.
Explanation:
Surveys and questionnaires are efficient methods for collecting responses from a large group quickly and analyzing trends.
Key Words: Survey, Questionnaire, Large group, Data collection
Q4: What type of data is the number of students in your class?
Answer:
Discrete data
Explanation:
The number of students is a whole number (e.g., 25, 30). It cannot take decimal values and is countable.
Key Words: Discrete, Whole numbers, Countable
Q5: Why is it important to organize data into tables or charts before analyzing it?
Answer:
Organizing data in tables or charts makes it easier to identify patterns, trends, and relationships.
Explanation:
Raw data can be confusing, but when structured in charts or tables, it allows for better comparison and decision-making.
Key Words: Visualization, Patterns, Trends, Comparison
Q6: What is one advantage of using online tools like Google Forms for collecting survey data?
Answer:
Google Forms allows for automatic data collection and easy analysis.
Explanation:
Responses are stored digitally, reducing errors and saving time in organizing and analyzing results.
Key Words: Automatic, Digital, Time-saving, Error-free
Q7: Why might you need to integrate data from different sources when working on a project?
Answer:
To get a complete and accurate picture by combining information from multiple perspectives.
Explanation:
Different sources may provide complementary details, ensuring better decision-making and reducing biases.
Key Words: Integration, Accuracy, Multiple sources, Complete data
Q8: Describe a scenario where discrete data might be more useful than continuous data.
Answer:
Example: Counting the number of books in a library.
Explanation:
Discrete data is used when values are fixed and countable (e.g., number of students, tickets sold). It does not require measurements with decimals.
Key Words: Countable, Whole numbers, Fixed values
Q9: Explain why data visualization is important. How does it help in understanding complex information?
Answer:
Data visualization simplifies complex data by presenting it in an easy-to-read format.
Explanation:
Charts, graphs, and infographics allow people to quickly identify trends, patterns, and outliers that may not be obvious in raw data.
Key Words: Visualization, Patterns, Trends, Simplification
Q10: Describe what a line graph is used for and provide an example of data that could be displayed using a line graph.
Answer:
A line graph is used to show trends over time.
Example:
Tracking monthly sales growth in a business.
Explanation:
A line graph helps visualize changes over time and is useful in analyzing trends, such as population growth or temperature variation.
Key Words: Trends, Time-based, Growth, Decline
Q11: Explain the use of scatter plots in visualizing continuous data. Provide an example of a situation where a scatter plot would be useful.
Answer:
A scatter plot is used to show the relationship between two numerical variables.
Example:
Comparing study time vs. exam scores to see if more study hours improve performance.
Explanation:
Scatter plots help identify correlations between variables, such as positive, negative, or no correlation.
Key Words: Correlation, Relationship, Two variables, Trend analysis
Long Questions
Q1: Explain the differences between qualitative and quantitative data. Provide examples of each type.
Answer:
Qualitative data describes characteristics or categories, while quantitative data consists of numbers that can be measured or counted.
Example:
- Qualitative Data: Favorite color, type of pet, eye color.
- Quantitative Data: Age, height, number of students in a class.
Key Words: Qualitative = Descriptive, Quantitative = Numerical, Measurable
Q2: Describe the process of conducting a survey to gather data about students’ favorite extracurricular activities.
Answer:
- Decide the purpose – To find out students’ favorite activities.
- Design the survey – Create questions (e.g., multiple choice, ranking).
- Distribute the survey – Use Google Forms or paper forms.
- Collect responses – Gather data from students.
- Analyze results – Use tables or graphs to understand trends.
Key Words: Survey, Data Collection, Questionnaire, Analysis
Q3: Compare and contrast continuous and discrete data. Use examples in a school setting.
Answer:
- Continuous Data: Can take any value within a range. Example: Students’ heights in cm (e.g., 155.3 cm, 160.5 cm).
- Discrete Data: Only specific whole values. Example: Number of students in a class (e.g., 25, 30).
Comparison:
- Continuous data is measured; discrete data is counted.
- Continuous data can have decimal values; discrete data cannot.
Key Words: Measured, Counted, Whole Numbers, Decimal Values
Q4: Analyze the benefits and challenges of using digital tools like Google Forms for data collection.
Answer:
Benefits:
- Quick data collection.
- Automatic analysis using graphs and charts.
- Accessible from anywhere.
Challenges:
- Requires internet access.
- Not everyone may be comfortable using digital tools.
Key Words: Digital, Easy Analysis, Internet Access, Automated
Q5: Imagine you are organizing a school event. How would you collect data on student preferences?
Answer:
- Create a survey – Ask about preferred activities and refreshments.
- Distribute the survey – Use Google Forms or paper forms.
- Collect and analyze responses – Use tables or pie charts.
- Plan the event – Arrange activities and food based on the survey results.
Key Words: Survey, Preferences, Data Collection, Event Planning
Q6: Explain the role of tables and charts in data analysis. Provide an example.
Answer:
Tables and charts make data easy to understand by organizing information visually.
Example: A bar chart can show students’ grades in different subjects, making it easier to compare performance.
Key Words: Visualization, Easy Comparison, Tables, Charts, Graphs
Q7: Describe a situation where non-numeric data is essential. How would you collect, store, and analyze it?
Answer:
Situation: Collecting student feedback about school environment (e.g., “Do you feel safe at school?”).
Steps:
- Collect – Use open-ended survey questions.
- Store – Save responses in a document or spreadsheet.
- Analyze – Identify common themes (e.g., “many students feel safe”).
Key Words: Non-numeric, Feedback, Open-ended, Thematic Analysis
Q8: Explain data visualization. How does it help in understanding complex data?
Answer:
Data visualization converts numbers into graphs and charts, making trends and patterns easier to understand.
Examples:
- Line Graph: Temperature changes over time.
- Pie Chart: Favorite subjects among students.
Key Words: Graphs, Charts, Simplify Data, Trends, Patterns
Q9: Discuss the importance of data visualization for businesses and decision-makers.
Answer:
Data visualization helps businesses make better decisions by presenting information clearly.
Benefits:
- Easy decision-making: Sales trends shown in bar charts help plan future sales.
- Quick comparisons: Pie charts show customer preferences clearly.
Key Words: Business, Decision-Making, Trends, Easy Comparison
Q10: Differentiate between nominal, ordinal, discrete, and continuous data. Provide suitable visualizations for each.
Answer:
- Nominal Data (Categories, No Order)
- Example: Types of pets (dog, cat, fish).
- Best Visualization: Pie Chart (percentage of each pet type).
- Ordinal Data (Ordered Categories)
- Example: Student rankings (1st, 2nd, 3rd).
- Best Visualization: Bar Chart (students’ rankings).
- Discrete Data (Whole Numbers, Countable)
- Example: Number of students in each class.
- Best Visualization: Column Chart (class sizes).
- Continuous Data (Measurable, Decimal Values)
- Example: Heights of students (e.g., 155.4 cm, 162.5 cm).
- Best Visualization: Histogram (height distribution).
Key Words: Nominal = Categories, Ordinal = Order, Discrete = Countable, Continuous = Measurable
