Unit 4: Data and Analysis

10th Computer Science Unit 4 Data and Analysis

Write answers of the following short response questions.

Q.1.Describe how data science helps businesses make informed decisions and provide two industry examples.

Ans: Data science helps businesses make informed decisions by transforming raw data into meaningful insights. By applying statistical methods, machine learning, and data visualisation, companies can identify trends, forecast outcomes, and improve their strategies. We can briefly learn about its scope in some common fields of life as follows:

Healthcare: To predict the possibilities of some disease, based on X-rays, Ultrasounds or other analytics of the patient.

Sports: If we want to predict who is going to win a cricket match, based on the previous performance of teams, data science will help us.

Q2. Identify three ways data science contributes to machine learning and artificial intelligence.

Ans: Data science plays a crucial role in the development of machine learning and artificial intelligence in several ways.

(i) It helps analyse data to discover useful patterns and insights, which are essential for building intelligent models.

(ii) Data science supports the development and selection of appropriate algorithms by experimenting with various approaches and measuring their effectiveness on real-world data.

(iii) It ensures continuous improvement by monitoring the performance of ML and AI systems over time and updating them with new data to keep them accurate and relevant.

Q3. Differentiate between supervised learning and unsupervised learning.

Ans:

Supervised learning	Unsupervised learning
(i) Uses labelled data (input and output are known)	(i) Uses unlabelled data (only input is known)
(ii) Predict outcomes or classify data based on known labels.	(ii) Discover hidden patterns or groupings in data.
(iii) Email spam detection, price prediction, disease diagnosis.	(iii) Customer segmentation, market basket analysis.
(iv) Predicts specific values or categories.	(iv) Identifies clusters, associations, or structures.
(v) Learns from the correct answers provided during training.	(v) Learns without explicit guidance or correct answers.

Q4. Describe an everyday example that illustrates reinforcement learning.

Ans: An everyday example of reinforcement learning is teaching a pet dog new tricks. When you train your dog, you give it a command, and if the dog performs the trick correctly (like sitting or fetching), you reward it with a treat or praise. This reward encourages the dog to repeat the behaviour in the future. If the dog doesn’t follow the command, it doesn’t get a reward, so it learns to avoid that behaviour. Over time, through trial and error and receiving feedback (rewards or no rewards), the dog learns which actions lead to positive outcomes.

Q5. Write the appropriate machine learning model for each of the following scenarios.

Sr. No.	Scenario	Suitable Machine Learning Model
1	You have a basket of mixed fruits (apple and banana),lo0 and you want a robot/machine to sort them.	Supervised learning
2	You are given a task to find the similarity in various flavours of ice cream.	Reinforcement learning
3	You have a book with pictures, and you want to teach your sibling to recognise them.	Unsupervised learning
4	You want to train a toy robot to find its way out of a maze.	Supervised learning
5	You have a set of shapes (square, triangle, circle and you want to teach a computer to recognise them.	Reinforcement learning
6	Your parents want you to clean your messy room if you want to attend the birthday party of your friend	Reinforcement learning
7	You are given a task to learn how to ride a bicycle to participate in a sports event.	Supervised learning
8	You have a book collection without specific categories, and you want your sibling to arrange them according to size, choice or ease of access.	Unsupervised learning
9	You have to unlock some rewards in your favourite video game.	Unsupervised learning
10	You have to unlock some rewards in your favorite video game.	Reinforcement learning

Write answers of the following extended response questions.

Q.1. Analyse the interrelationship between Data Science, Machine Learning, and Artificial Intelligence.

Ans: Data Science, Artificial Intelligence and Machine Learning Skills:

Artificial Intelligence and Machine Learning skills refer to develop systems that can learn and perform decision-making. Data Science skill refers to extracting insights from data and making informed decisions. There are many skills to achieve excellence in artificial intelligence. These skills include programming language, machine learning, and acquiring domain-specific knowledge. In programming languages, Python and R are the most used languages in Al. In machine learning algorithms, the knowledge of the TensorFlow framework, deep learning, neural networks, and NLP is very important.

(i) Data Science and its scope:

Data Science has a wide scope in many disciplines of life, and it is continuously expanding. We can briefly learn about its scope in some common fields of life a follows:

• Healthcare: To predict the possibilities of some disease, based on X-rays, Ultrasounds or other analytics of the patient.
• Sports: If we want to predict who is going to win a cricket match, based on the previous performance of teams, data science will help us.
• Finance: In the banking and finance sector, data science is helpful in fraud detection. It is also helpful in developing trading and investment strategies.
• Goods transportation: It helps in route optimisation and minimises delivery times. Data-driven approach helps to reduce cost. It also helps in real-time tracking of the transportation of goods.
• Airline: It helps in efficient route planning to reduce fuel consumption. It helps to improve services based on customer experience and feedback
• Energy and utilities: It helps to optimise energy production and distribution. Real-time monitoring is useful for load distribution and facilitates immediate response to demand fluctuations.
• Education: It is useful to enhance personalised learning based on the performance of learners. It also helps to predict students’ performance based on previous data.
• Communication Media: It is used for content creation and distribution. Various approaches. Like sentiment analysis, social media monitoring, and recommender systems are a few examples of the scope of data science in the media and communication sector.
• Entertainment: Data science plays an important role when you get suggestions about your favourite songs while playing online computer games.
• Business: Data science helps in intelligent decision-making about the business to get optimal profits. Customer insight and sentiments are also analysed to improve the business.

(ii) Artificial Intelligence and its scope:

The term artificial intelligence is not new. In 1950, a British mathematician, Alan Turing, proposed a Turing Test, which measured the ability of a machine to exhibit intelligent behaviour. Nowadays the modern robots are supposed to be smarter if they can pass the Turing Test. The term Artificial Intelligence refers to the ability of a machine to exhibit human behaviour like problem solving, understanding natural language & interacting with the environment intelligently. Like data science, the scope of artificial intelligence is wide.

• Decision-making: Artificial intelligence is very useful in optimal decision-making, based on a data-driven approach.
• Personalised recommendations: It helps to provide personalised feeds to customers, based on their past usage of machines.
• Automation industry: It has played a vital role in automating tedious jobs like car manufacturing and image & video analysis. The most important utilisation of artificial intelligence is the integration of the internet in devices of daily use. The entire industry of IT and smart devices is based on automation.
• Natural Language Processing (NLP): AI enabled the machines to understand and respond to natural human languages. ChatGPT and chatbots are examples of it. Now you can switch ON your air conditioner with a voice command rather than picking up the remote controller.
• Robotics: Whether you watch a latest robot, talking and cooking like a human being, or you get a small robot to clean your house, all this technology is due to significant advancement of artificial intelligence.
• Healthcare: Like data science, artificial intelligence has played equally well in the field of healthcare. The examples are personalised treatment recommendations based on medical imaging.
• Computer vision: It is a specialized branch of AI that teaches computers to draw meaningful results from digital images, videos and other visuals.
• Smart cities: Artificial Intelligence enables to development of efficient infrastructure and services, which can be used to manage energy consumption, traffic management and other necessary tasks for smart cities.
• AI Agents: Artificial Intelligence provides the basis to develop modern AI agents like Siri, Alexa, Google Assistant, ChatGPT and Cortana.

(iii) Machine Learning and its scope:

Machine learning is used in many fields of life, like healthcare, the automation industry, to develop recommender systems, finance and banking, pattern recognition, NLP, computer vision, research and innovation. An example of machine learning is Automated fraud detection. It helps to identify fraudulent activity by finding anomalies such as sudden large transactions or unusual network traffic. In short, the entire scope of Data Science and Artificial Intelligence is based on the algorithms developed in the field of Machine Learning

Q2. Identify any three types of data visualisation, and give their applications as well.

Ans: Types of Data Visualisation:

There are different ways to represent data graphically to make it easier to understand. Some common types of data visualization are as follows:

Quantitative Visualization

Quantitative visualization is used to represent numerical data. It focuses on quantities or numbers to show measurable data. It is used to display data that can be measured or counted. For example, a bar chart showing sales figures of a company over several months effectively communicates quantitative information.

Categorical Visualization

Categorical visualization is used to represent data that falls into distinct categories. It helps to show proportions or parts of a whole. This type is ideal for displaying nominal or ordinal data. For example, a pie chart showing the market share of different companies in a specific industry.

Temporal Visualization

Temporal visualization is used to display data that changes over time. It is used for time-series data. Line graphs are usually used to represent temporal data. For example, a line graph.

Spatial Visualization

Spatial visualization is used to represent data related to physical locations or spaces, visualizing geographic or spatial data. For example, a heat map showing population density across different regions.

Multivariate Visualization

Multivariate visualization is used to represent data involving more than two variables or dimensions. Scatter plots and heat maps are effective tools to represent such data. For example, a scatter plot matrix showing relationships between income, age, and spending habits.

Interactive Visualization

Interactive visualization allows users to interact with the data through digital platforms. These digital platforms can be dashboards and filters. A dashboard helps the user to filter and manipulate data visualizations and explore different trends and insights.

Statistical Visualization

Statistical visualization is used to present data to show statistical properties, such as distribution or correlation. Histograms, box plots, and scatter plots are popular choices.

Information Visualization

Information visualization is used to present complex data sets in an easier way, often for abstract conceptual data. Network diagrams, tree maps, and word clouds are effective tools. For example, a network diagram showing relationships between entities, such as a social network.

Q3. Discuss the way data visualization can be used to communicate data uncertainty, and provide two specific examples.

Ans: Data visualization is not only used to display exact data values but also to communicate the uncertainty or variability in data. Uncertainty means that the data may have errors, or predictions may vary due to changing conditions or incomplete information. Visualizations can show this uncertainty using special elements like error bars, shaded confidence intervals, or multiple possible outcome lines.

Two specific examples are:

(i) Weather Forecast Graph:

When meteorologists predict temperatures, they often show a line graph t the expected temperature each day. Around this line, a shade area or band shows the possible temperature range (for example, the highest and lowest likely temperatures). This shaded area is called a confidence interval. It communicates that while the forecast predicts a certain temperature, actual temperatures may fall within this range due to natural weather variability.

(ii) COVID-19 Infection Model:

During a pandemic, scientists use models to predict how many people might get infected in the future. These models include different scenarios based on factors such as social distancing or vaccination rates. A chart may show several lines or a Shaded area to represent best-case and worst-case predictions. This visualization communicates uncertainty by showing that the actual number of cases could vary widely depending on future events and behavior.

Q4. Describe the key considerations in selecting appropriate visualizations for different types of data and analyses?

Ans: Choosing appropriate visualizations involves several key considerations to ensure effective communication and analysis of data. The visualization must match the type of data and the goal of the analysis.

Key Considerations:

Below are the key considerations:

Type of Data:

• Categorical Data (e.g., colors, brands) is best displayed with bar charts or pie charts to compare categories.
• Numerical or Continuous Data (e.g., temperature, sales) is well-suited for line graphs, histograms, or scatter plots to show trends and distributions.
• Time Series Data (e.g., monthly revenue) is effectively shown with line graphs to display changes over time.

Purpose of the Analysis:

• If the goal is to compare values across categories → use bar charts or column charts.
• To show trends over time → use line graphs.
• To analyze relationships or patterns → use scatter plots.
• To show parts of a whole → use pie charts or stacked bar charts.
• For distribution analysis → use histograms or box plots.

Audience:

• Use simple and clear charts for general audiences who may not be familiar with data.
• For technical viewers, more detailed charts like heat maps, bubble charts, or box plots can be used
• Always include legends, labels, and titles to ensure understanding

Amount and Complexity of Data:

• For small datasets, simple visuals like bar charts are enough.
• For large or complex datasets, use interactive dashboards or heat maps to summarize data effectively.

Clarity and Design:

• Avoid cluttered visuals. Use clear labels, proper scales, and appropriate color schemes.
• The visualization should highlight the key message without overwhelming the viewer.

Q5. Explain the uses of data visualization in detail.

Ans: Uses of Data Visualization:

Like Artificial Intelligence, data science and machine learning, data visualization is useful in almost all fields of life.

Some of them are as follows:

Business Intelligence: Data visualization helps to make data-driven, well-informed decisions. It is used to find market trends and helps to track and improve performance.

Healthcare: It helps to visualize the impact of various diseases affecting the patient. It is helpful to track disease and visualize the spread of disease.

Education: Data visualization is very helpful to teach data literacy skills, concept building, creative thinking, and critical thinking

Science and Research: It is useful to visualize complex findings, very huge and complex data, such as complex scientific data received from satellites in the form of photographs.

Sports and gamming: It is useful to visualize performance of players, whether they are playing football on a ground or chess players playing in online tournaments. It is also helpful in sports broadcasting and other Predictions.

Finance: It is helpful to analyse market trends, to track portfolio performance and to identify investment opportunities.

Entertainment: It helps the entertainment industry to visualize movie performance data to predict future trends. It helps in content optimization by visualizing audience insights and trends.

Q6. What are the potential consequences of poor data quality on model performance?

Poor data quality can significantly affect the performance of a data model, especially in fields like Machine Learning and Data Science. When the input data is incorrect, incomplete, inconsistent, or outdated, it leads to unreliable outputs. Below are some key consequences:

Inaccurate Predictions:

Models trained on poor-quality data may produce wrong or misleading results.

For example, a disease prediction model may give false results if the training data has missing or incorrect patient records.

Overfitting or Underfitting:

Overfitting happens when the model learns too much noise from the data, while underfitting occurs when it cannot capture patterns at all.

Poor data quality causes both issues, reducing the model’s ability to generalize to new data.

Increased Errors and Bias:

If the dataset is biased or unbalanced (e.g., too many examples of one category), the model may give unfair or incorrect results.

For example, a face recognition system might fail if it were trained mostly on one ethnicity.

Waste of Resources:

Time, computing power, and storage are wasted when models are trained on low-quality data.

Analysts may also spend extra time cleaning and reprocessing data instead of focusing on analysis.

Poor Decision-Making:

In real-world applications like business, healthcare, or finance, bad data can lead to wrong decisions, financial loss, or even safety risks.

Select the best answer for the following MCQs.

1. Which of the following is the primary benefit of integrating Mathematics and Statistics with Computer Science in Data Science?

A) Improved data visualization

B) Better forecasting

C) Increased accuracy

D) Better decision making

2. Which of the following best describes the relationship between Data Science and Artificial Intelligence?

A. Data Science is a subset of Artificial Intelligence

B. Artificial Intelligence is a tool used in Data Science

C. Data Science and Artificial Intelligence are unrelated

D. Data Science enables Artificial Intelligence

3. The Turing Test, proposed by Alan Turing in 1950, measures a machine’s ability to exhibit intelligent behaviour. Which of the following is the fundamental assumption that underlies this test?

A. Humans are better

B. Machines are equal

C. Intelligence levels vary

D. Machines copy humans

4. Which of the following should be considered critically while developing AI-powered chatbots and virtual assistants?

A. User experience

B. Data security

C. Contextual awareness

D. Emotional intelligence

5. What ethical consideration arises from the integration of Artificial Intelligence (AI) into daily life devices?

A. Job displacement due to automation.

B. Increased energy consumption.

C. Improved customer service.

D. Enhanced data security.

6. Which of the following fields of Artificial Intelligence (AI) enables smartphones to recognize faces and unlock devices?

A. NLP

B. Computer vision

C. Deep learning

D Neural networks

7. A company wants to develop a system that categorizes customer feedback into positive, negative, or neutral. Which learning model would be most suitable?

A. Supervised learning

B. Unsupervised learning

C. Reinforcement learning

D. Deep learning

8. In a Reinforcement Learning model, what is the primary function of rewards and penalties provided as feedback to the agent?

A. Labelling data

B. Evaluating performance

C. Improving action choices

D. Classifying outcomes

9. Which stage of the data science life cycle ensures the model’s accuracy, reliability, and compliance with privacy rules?

A. Model Deployment

B. Model Evaluation

C. Data Analysis

D. Maintenance and Monitoring

10. Which of the following is the key characteristic of the “Data Cleaning” stage in the data science life cycle?

A. Data collection

B. Error removal and data organization

C. Pattern identification

D. Model deployment