Data Science Class 10 Questions and Answers

These Class 10 AI Important Questions Chapter 4 Data Science Class 10 Important Questions and Answers NCERT Solutions Pdf help in building a strong foundation in artificial intelligence.

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

Data Science Class 10 Subjective Questions

Question 1.
Fill the all stage of AI Project cycle.

Answer:

Question 2.
What is Mean? How is it calculated?
Answer:
The mean, often referred to as the average, is calculated by adding up all the values in a data set and then dividing by the nu mber of values. This is probably the most familiar one – it’s the average. You calculate it by adding up all the values in your data set and then dividing by the nuimber of values.expand_more So, if you have a data set {2,4,6,8}, the mean would be (2+ 4+6+8) / 4=5.expand_more

Question 3.
What is Mediarı? How is it calculated?
Answer:
This is the “micidle” number when you order your data set from least to greatest.expand_more If you have an even number: of values, the median is the average of the two middle numbers.expand_more So, for the data set {2,4,6,8}, the median is 5. But if you have an odd number of values, like {2,4,5,6,8}, the median is just the middle number, which is 5 again.

Question 4.
What is Mode? How is it calculated?
Answer:
This is the most frequent number in your data set.expa nd_more So, for 2,4,6,6,8, the mode is 6 because it appears more than any other number. If you have a data set where no number repeats, or if multiple numbers tie for the most frequent, then there is no mode.

Question 5.
What is Standard Deviation? How is it calculated?
Answer:
The spread of statistical data is measured by the standard deviation. Distribution measures the deviation of data from its mean or average position. The degree of dispersion is computed by the method of estimating the deviation of data points. It is denoted by the symbol, ‘ s ‘. The sample standard deviation formula is:

Here, s = Sample standard deviation

Question 6.
What is Variance? How is it calculated?
Answer:
According to layman’s words, the variance is a measure of how far a set of data are dispersed out from their mean or average value. It is denoted as ‘ s ‘.
The sample variance formula is given as:

Here, s² = Sample variance
n = Number of observations in sample
x_i = i th observation in the sample
\(/bar{x}\) = Sample mean

Data Science Class 10 Very Short Answer Type Questions

Question 1.
Which type of data KNN is use?
Answer:
Structured

Question 2.
We can create scatter plots in Python by calling the which function?
Answer:
scatter()

Question 3.
It is commonly collected for natural language processing (NLP) tasks such as sentiment analysis, text classification, and named entity recognition. What is it?
Answer:
Text data

Question 4.
How to marketers analyze customer demographics and behaviour to create personalized?
Answer:
advertising campaigns

Question 5.
It is a versatile Python library for creating static, interactive, and publication-quality visualizations. What is it?
Answer:
Matplotlib

Question 6.
It is a fundamental data structure used in programming to store a collection of elements of the same data type. What is it?
Answer:
array

Question 7.
For which tasks, once the ‘ K ‘ nearest neighbors are identified, the algorithm takes a majority vote to determine the class label of the new data point?
Answer:
classification

Question 8.
What is data collection?
Answer:
Data collection is the process of gathering and storing information from various sources for analysis and decision-making.

Question 9.
How is data science applied in transportation?
Answer:
Data science is used in transportation for optimizing routes, predicting traffic patterns, and improving logistics efficiency.

Question 10.
What is the application of data science in internet search?
Answer:
Data science enhances internet search by personalizing results, improving relevance, and optimizing search algorithms.

Data Science Class 10 Short Answer Type Questions

Question 1.
Ajay wants to access data from various data sources. Suggest him any two points that he should keep in mind while accessing data from any data source.
Answer:
When accessing data from various sources, Ajay should consider two important points.

• He should ensure that he has the necessary permissions or rights to access the data, respecting privacy regulations and legal requirements.
• He should verify the quality and reliability of the data

Question 2.
Give two advantages of using KNN.
Answer:
Two advantages of using the KNN (K-Nearest Neighbors) algorithm are

• Simple to Understand: Easy to grasp and implement, making it suitable for beginners.
• No Training Phase: Doesn’t require a training phase since it memorizes the entire dataset.

Question 3.
Differentiate between data acquisition and data exploration.
Answer:

Question 4
How are NumPy arrays better than Python lists?
Answer:
NumPy arrays offer several advantages over Python lists.
NumPy arrays are more efficient in terms of memory usage and computational performance, especially for large datasets. They are implemented in C and optimized for numerical operations, making them faster and more suitable for numerical computing tasks.

NumPy arrays support vectorized operations, allowing for element-wise operations and broadcasting, which can lead to concise and efficient code compared to iterating over lists.

Question 5.
What is a pie plot?
Answer:
A pie plot, is a circular statistical graphic that is divided into slices to illustrate numerical proportions. Each slice represents a proportion of the whole, typically shown as a percentage or fraction of the total. Pie plots are commonly used to visualize the distribution of categorical data and to highlight the relative sizes of different categories within a dataset.

Question 6.
Write the difference between unstructured and semi-structured data.
Answer:

Question 7.
AI can capture data for personality prediction from various sources using certain techniques. Write about any two such techniques.
Answer:
Al can capture data for personality prediction from various sources using techniques such as social media analysis and natural language processing (NLP). Social media analysis involves gathering data from social media platforms, such as Facebook, Twitter, or LinkedIn, to analyze user-generated content, interactions, and behaviours. NLP techniques can be applied to extract insights from text data, such as blog posts, emails, or forum discussions, to identify linguistic patterns, sentiments, or personality traits.

Data Science Class 10 Long Answer Type Questions

Question 1.
Explain in details the different measures of central tendency.
Answer:
These statistical measures are used to understand the distribution, central tendency, and variability of data, which in turn helps in making informed decisions and drawing meaningful insights from the dataset.

Mean The mean is the average value of a dataset. It is calculated by adding up all the values in the dataset and then dividing by the number of values. The mean provides a measure of central tendency, indicating the typical value in the dataset.

Median The median is the middle value of a dataset when it is sorted in ascending order. If the dataset has an odd number of values, the median is the middle value. If the dataset has an even number of values, the median is the average of the two middle values. The median is also a measure of central tendency and is less affected by extreme values (outliers) compared to the mean.

Mode The mode is the value that appears most frequently in a dataset. A dataset can have one mode (unimodal), two modes (bimodal), or more than two modes (multimodal). The mode provides insight into the most common value or category in the dataset.

Variance Variance measures the spread or dispersion of the values in a dataset. It quantifies how much the values in the dataset differ from the mean. A higher variance indicates that the values are more spread out from the mean, while a lower variance indicates that the values are closer to the mean.

Standard Deviation Standard deviation is another measure of the spread or dispersion of the values in a dataset. It is the square root of the variance. Like variance, standard deviation indicates how much the values deviate from the mean. A higher standard deviation suggests greater variability in the dataset, while a lower standard deviation suggests less variability.

Question 2.
What is data collection? Also, write the type of data.
Answer:
Refer to text on pages 119 and 120 (Data collection, Types of data)

Question 3.
Differentiate between NumPy Arrays and List.
Answer:
Refer to text on page 121 (Difference between NumPy Array and List)

Question 4.
What is K-NN model? How does K-NN work?
Answer:
K-Nearest Neighbour Model

• K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique.
• K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories.
• K-NN algorithm can be used for Regression as well as for Classification but mostly it is used for the Classification problems.
• K-NN is a non-parametric algorithm, which means it does not make any assumption on underlying data.
• It is also called a lazy learner algorithm because it does not learn from the training set immediately instead it stores the dataset and at the time of classification, it performs an action on the dataset.

How does K-NN Work?
The K-NN working can be explained on the basis of the below algorithm:

• Step-1 Select the number K of the neighbors
• Step-2 Calculate the Euclidean distance of K number of neighbors
• Step- 3 Take the K nearest neighbors as per the calculated Euclidean distance.
• Step-4 Among these k neighbors, count the number of the data points in each category.
• Step-5 Assign the new data points to that category for which the number of the neighbor is maximum.
• Step-6 Our model is ready.

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