. Did post industrial society move only towards the service sector? Discuss. 

(a) Explain different methods of visualising random forests. 
(b) Explain the use of the bivariate histogram and kernel density estimation with the help of a suitable
example.
(c) Explain the Trellis Paradigm. Why is it needed?
(d) Explain various regression visualisations with the help of an example

3:
(a) What is a scatter plot matrix? Why is it needed? Write a Python program to make a scatter plot matrix.
 (3 Marks)
(b) A weather department has recorded daily rainfall and temperature data for five years. Suggest suitable
graphical techniques for visualising this dataset. Justify your choices with reference to time series,
scatterplot matrices, and geographical maps. 
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(c) Using Python, create a dashboard consisting of multiple plots arranged in a single figure. Demonstrate
how titles, legends, annotations and customised plot parameters improve the readability of the
visualisation.
(d) Explain any four applications of graphs in data science with suitable examples. (4 Marks)
(e) A social networking website wants to analyse the relationships among users. Describe how graph
representations such as bipartite graphs, hierarchical trees and graph drawing techniques can be used for
analysing the network. Illustrate your answer with suitable diagrams.

Write a Python program with suitable comments using pandas that performs the following tasks after
reading the file.
 identify missing values,
 remove duplicate records,
 replace inconsistent values,
 fill missing salary values using an appropriate method, and
 rename the column headings.
(d) Explain different techniques used for handling missing data. Discuss situations where each technique is
appropriate. (3 Marks)
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(e) A dataset contains the ages of 500 customers ranging from 18 to 80 years. Explain different ways to
classify these customers into age groups using discretisation (binning). (4 Marks)
(f) Write Python code to detect outliers using the interquartile range method. Also, explain the strategies for
handling outliers in data with the help of suitable examples. (4 Marks)
Q2:
(a) Two data files contain customer details and purchase details having the same format. These files may
contain overlapped data. Write and explain the Python program to merge the two files. Make and state
assumptions, if any (3 Marks)
(b) Explain hierarchical indexing. Illustrate how hierarchical indexing can be used for reshaping datasets
with suitable examples. (3 Marks)
(c) A supermarket dataset contains the following fields: (3 Marks)
 Region
 Product Category
 Sales
 Profit
Write a Python program to perform the following tasks:
 group data by Region,
 calculate total sales,
 calculate average profit.
(d) Why is the need to create groups during data analysis? Explain with the help of an example. Explain the
working of the groupby() function in pandas. Discuss grouping using dictionaries, functions and Series
in Python with suitable examples. 
(e) Make a synthetic dataset containing monthly sales of three different products for one year. Write a
Python program using Matplotlib and/or Seaborn to generate a line plot and a scatter plot. Make suitable
assumptions. 
(f) Write a Python code to demonstrate the purpose of figures, axes, labels, legends, annotations and subplot
arrangements with suitable examples.
 

Make a CSV file that contains employee information. The file should have a few missing salary values, a
few duplicate employee records, a few inconsistent department names, and some incorrect city names.

 A retail company has collected customer transaction data containing the following attributes: (3 Marks)
 Customer ID
 Age
 Gender
 City
 Purchase Amount
 Payment Mode
Describe how you would perform data profiling on this dataset. What are the summary statistics that you
would perform on this data?

Explain data wrangling. Discuss the importance of data semantics, set-based profiling, summary
statistics, and data profiling in preparing raw data for data analysis. Illustrate your answer with a suitable
example

: Write a Python Program to implement Support Vector Machines for data classification , and choose a
dataset of your own choice.

Take a real-time example to implement the ID3 decision tree classification algorithm in Python

 Write a Python Program to implement Logistic Regression for data classification, and choose a dataset
of your own choice. 

Discuss the Naïve Bayes algorithm and write the Python code to demonstrate the execution of the
Naïve Bayes algorithm on the dataset of your choice. 

Write a Python Program to implement the AO* Algorithm 

 Write a Python Program to implement the Min-Max Algorithm. 

Implement the Water Jug problem in Python.

Write a Python Program to solve the N-Queen Problem without using Recursion. 

Describe Soviet culture and Soviet ideology.

 Write a note on the causes of collapse of Soviet Union. 

 Write a note on any Russian/Soviet writer and discuss any one
of his/her work.

 Write a short note on the given topics -
i) Red Square
ii) Peter the Great

Explain 1917 October Russian Revolution.