Experts have designed these Class 10 AI Important Questions Chapter 4 Data Science Class 10 Important Questions and Answers NCERT Solutions Pdf for effective learning.
Data Science Class 10 Important Questions
Class 10 AI Data Science Important Questions
Important Questions of Data Science Class 10 – Class 10 Data Science Important Questions
Very Short Answer Type Questions (1 mark each)
Question 1.
Why KNN is lazy learner?
Answer:
KNN algorithm does not build a model during the training phase. The algorithm memories the entire training dataset and performs action on the dataset at the time of classification.
Question 2.
What is Data Science?
Answer:
Data science is an interdisciplinary field that involves extracting insights and knowledge from structured and unstructured data using various techniques, algorithms, and tools.
Question 3.
What are Pandas use for?
Answer:
Pandas is used for data manipulation and analysis in Python.
Question 4.
Name the five stages of AI Project?
Answer:
- Stage 1: Problem Scoping
- Stage 2: Data Acquisition
- Stage 3: Data Exploration
- Stage 4: Data Modelling
- Stage 5: Data Evaluation
Question 5.
What is mean?
Answer:
Mean refers to the average value of a set of numbers. It is calculated by summing all the values in the dataset and dividing by the total number of values.
Question 6.
What is median?
Answer:
The median is the middle value of a sorted list of numbers. If the list has an odd number of observations, the median is the middle value. If the list has an even number of observations, the median is the average of the two middle values.
Question 7.
What is mode?
Answer:
The mode is the value that appears most frequently in a dataset. It represents the most common observation or category within the data.
Short Answer Type Questions (2 marks each)
Question 1.
How is society helping entrepreneurs in their business?
Answer:
Society is helping entrepreneurs by
(i) Creating needs
(ii) Providing raw material
(iii) buying/selling of items
(iv) making money
Question 2.
What is data with respect to the AI domain of Data Science?
Answer:
Every AI system heavily relies on data. Data is the core of almost every AI system. AI systems need data for functioning, learning and growing. It is basically input in the system.
Question 3.
What type of data can be used by Pandas?
Answer:
Tabular data: Data organised in rows and columns, such as CSV files, Excel spreadsheets, SQL tables, etc.
- Time series data: Data indexed by time or date, commonly used in financial, economic, and scientific applications.
- Heterogeneous data: Data with mixed types, such as numeric, string, boolean, etc.
- Missing data: Data with missing or NaN (Not a Number) values, which Pandas can handle efficiently.
Question 4.
What are the applications of data science?
Answer:
Recommender Systems: Providing personalised recommendations to users based on their past behaviours and preferences, commonly seen in e-commerce, streaming services, and social media platforms.
Fraud Detection: Identifying fraudulent activities and transactions by analysing patterns and anomalies in data, utilised in banking, insurance, and cybersecurity industries.
Image and Video Analysis: Analysing and interpreting visual data, used in medical imaging, surveillance, autonomous vehicles, and facial recognition systems.
Health Informatics: Analysing medical data to improve patient care, disease diagnosis, treatment outcomes, and public health interventions.
Social Network Analysis: Studying relationships and interactions within social networks to understand social behaviour, influence, and network dynamics.
Energy Forecasting: Predicting energy consumption and optimising energy production and distribution, contributing to renewable energy integration, demand-side management, and energy efficiency initiatives.
Question 5.
What is the use of histogram?
Answer:
Histograms are used for visualising the distribution of a dataset. They display the frequency or count of data points within predefined intervals, called bins, along a continuous axis. Histograms provide insights into the central tendency, dispersion, and shape of the data distribution, allowing users to identify patterns, outliers, and trends in the data. They are commonly used in statistical analysis, exploratory data analysis, and data visualisation to understand the underlying characteristics of a dataset and make informed decisions based on the data distribution.
Long Answer Type Questions (4 marks each)
Question 1.
What do you mean by data science? Illustrate your answer with an example.
Answer:
Data science is one of the domains of Al . It processes the data for the AI systems. It collects the data input, maintains them into accurate datasets, and prepares the output in the proper and appropriate format. The final output or information extracted through data science can be used to make a decision.
For example, price comparison websites like PriceGrabber, PriceRunner, Junglee, Shopzilla, and DealTime are totally driven by data, compares the price of a product from different vendors and then the user can make a suitable decision.
The digital marketing spectrum is also a good example of data science. It shows the targeted ads to the audience according to their data.
Question 2.
Explain the concepts of mean, median, mode, and standard deviation.
Answer:
Mean: The mean, often referred to as the average, is calculated by summing up all the values in a dataset and then dividing by the total number of values.
Median: When data are sorted in either ascending or descending order, the median is the value in the middle of the dataset. The median is the average of the two middle values when the number of data points is even.
In comparison to the mean, the median is less impacted by extreme numbers, making it a more reliable indicator of central tendency.
Mode: The value that appears most frequently in a dataset is the mode. One mode (unimodal), several modes (multimodal), or no mode (if all values occur with the same frequency) can all exist in a dataset.
Standard deviation: The spread or dispersion of data points in a dataset is measured by the standard deviation. It quantifies the variance between different data points.
Question 3.
Explain the k-nearest neighbours (KNN) algorithm.
Answer:
The k-Nearest Neighbors (KNN) algorithm is asimple and versatile supervised machine learning algorithm used for both classification and regression tasks. KNN makes predictions by memorising the data points rather than building a model about it. This is why it is also called “lazy learner” or “memory based” model too.
KNN relies on the principle that similar data points tend to belong to the same class or have similar target values. This means that, In the training phase, KNN stores the entire dataset consisting of feature vectors and their corresponding class labels (for classification) or target values (for regression). It then calculates the distances between that point and all the points in the training dataset. (commonly used distance metrics are Euclidean distance and Manhattan distance).
Note: Choosing an appropriate value for k is crucial. A small k may result in noisy predictions, while a large k can smooth out the decision boundaries. The choice of distance metric and feature scaling also impact KNN’s performance.
Case Based Subjective Question:
I. Read the following text and answer the following questions based on the same:
The given pie chart shows the subject of interest of each student in a class.
Answer the following question concerning the given pie diagram.
(a) If 30 students’ subjects of interest are Hindi, how many total students were surveyed?
(b) Which subject is liked the most?
(c) Which subject is disliked the most?
(d) What is sum of the percentage of maths and hindi?
Answer:
(a) According to the given question, 8 % of the total number of students is 30 . ie. (8 / 100) times Total = 30
Therefore, Total = 30 times(100 / 8) = 375
Hence 375 students were surveyed.
(b) According to the given pie chart, maths is liked the most.
(c) According to the given pie chart, hindi is disliked the most.
(d) According to the given pie chart,
Maths % is: 34 % and
Hindi % is: 8 %
Hence the total of maths and hindi is: 34 + 8 = 42 %