DADS301 PROGRAMMING IN DATA SCIENCE

Description

ASSIGNMENT

SESSION	February – March 2024
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	III
course CODE & NAME	DADS301-Programming in data science
CREDITS	04
nUMBER OF ASSIGNMENTS & Marks	02 30 MARKS EACH

 

Assignment Set – 1^st

Questions

 

1. (a) What is Data wrangling? Name the package used for Data wrangling in R and describe some of its features.

Ans: Data wrangling, also known as data munging is the process of cleaning, transforming, and preparing raw data for analysis. This involves various steps such as dealing with missing values, correcting data types, filtering out unnecessary information, merging datasets, and reshaping data. The goal of data wrangling is to convert raw data into a more structured and usable format that can be easily analysed.

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(b) Explain with examples the built-in functions and methods available to manipulate a data frame.

Ans: In R, data frames are a common data structure used for storing datasets. There are several built-in functions and methods available to manipulate data frames effectively. Below are some of these functions and

2. a) Describe the steps to initialize a plot, specify aesthetics, create a simple plot, and add titles and labels to the plot.

Ans: Creating plots in R can be effectively done using the ggplot2 package, which is part of the tidy verse collection. Ggplot2 is a powerful and flexible system for creating graphics. Below are the steps to initialize a plot, specify aesthetics, create a simple plot, and add titles and labels to

 

(b) Explain the following functions with example.

1. spread ()
2. gather ()
3. filter ().

Ans: The spread() function is used to convert data from a long format to a wide format. It spreads a key-value pair across multiple columns.

Example:

 

# Load the

 

3. (a) Describe the method to visualize the covariance between two continuous features using the geom_point() method. What is the disadvantage of this method? What is the advantage of using the geom_hex()? illustrate with an example.
   Ans: Visualizing the covariance between two continuous features can be done effectively using scatter plots (geom_point() in ggplot2). Let’s go through the method to visualize covariance with geom_point(), discuss its disadvantages, and then explore the advantages of using geom_hex() with an example.

Visualizing

(b) Explain the ‘Simple If’ construct and the ‘Vectorised If’ with example. Elaborate the advantages of Vectorised If.

Ans:

In R, there are two main constructs for conditional execution: the simple if construct and the vectorized ifelse() function. Let’s explore each of them with examples and discuss the advantages of using the vectorized ifelse() function.

Simple if Construct

The simple if construct

 

 

Assignment Set – 2^nd

Questions

 

 

4. (a) Write a basic program in Python. How are the comments added? What is the significance of indentation?

Ans: Here’s a basic Python program that calculates the factorial of a number:

# Python program to calculate factorial of a number

 

# Function to calculate factorial

Def factorial(

 

5. (a) Explain file manipulation with context manager with an example. What are the advantages of using context manager?

Ans: File manipulation in Python, especially with context managers, allows for efficient and safe handling of files. Context managers ensure that resources (like files) are properly managed, even if exceptions occur during execution. Let’s explain file manipulation with a context manager using an example and discuss the advantages.

File Manipulation with

6. (a) How are donut charts created in Python? Explain with an example.
   Ans: Creating donut charts in Python can be done using libraries like matplotlib or plotly. However, matplotlib does not have a built-in function specifically for donut charts, but you can achieve it by creating two pie charts: one for the outer circle and another for the inner circle. Let me walk you through an example of creating a donut chart using matplotlib.

Example: Creating a Donut Chart with Matplotlib

First, you’ll need to install matplotlib if you haven’t already:

Pip install matplotlib

Here’s an example of creating a donut chart using matplotlib:

Import matplotlib