DADS404 DATA SCRAPPING

198.00

Scroll down for Match your  questions with Sample

Note- Students need to make Changes before uploading for Avoid similarity issue in turnitin.

Another Option

UNIQUE ASSIGNMENT

0-20% Similarity in turnitin

Price is 700 per assignment

Unique assignment buy via WhatsApp   8755555879

Quick Checkout

Description

SESSION JUL – AUG 2024
PROGRAM MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER IV
COURSE CODE & NAME DADS404 DATA SCRAPPING
   
   

 

 

Assignment Set – 1

 

  1. Write short notes on different sources of data available for data scraping and factors to choose the right data.

Ans 1.

Different Sources of Data Available for Data Scraping and Factors to Choose the Right Data

Data scraping is a method for extracting structured information from various online and offline sources. Identifying the right data sources is crucial for ensuring the quality and relevance of the extracted data. Below are the primary sources of data available for scraping:

  1. Websites Websites are the most common and accessible sources of data scraping. Publicly available web pages, e-commerce platforms, blogs, and news websites often provide valuable structured or semi-structured dat

 

 

Its Half solved only

Buy Complete from our online store

 

https://smuassignment.in/online-store/

 

MUJ Fully solved assignment available for session July-Aug 2024.

 

Lowest price guarantee with quality.

Charges INR 198 only per assignment. For more information you can get via mail or Whats app also

Mail id is aapkieducation@gmail.com

 

Our website www.smuassignment.in

After mail, we will reply you instant or maximum

1 hour.

Otherwise you can also contact on our

whatsapp no 8791490301.

 

 

  1. What are the challenges in scraping data manually? Which R packages could help in manual scraping the data?

Ans 2.

Challenges in Scraping Data Manually and R Packages for Manual Data Scraping

Scraping data manually involves extracting information without the use of automated tools. While it may be necessary in certain cases, it comes with significant challenges that can impact efficiency and accuracy.

Challenges in Scraping Data Manually

  1. a) Time-Consuming

 

 

  1. Write the steps to scrap the data from any job portal. How python libraries can help in this?

Ans 3.

Steps to Scrape Data from a Job Portal and the Role of Python Libraries

Data scraping from job portals involves systematically extracting information such as job titles, company names, locations, and other relevant details. Python, with its versatile libraries, simplifies the entire process. Below are the steps to scrape data from any job portal and an explanation of how Python

 

Assignment Set – 2

 

 

  1. Write short notes on API based scrapers. Write benefits and drawback of API based scrapers.

Ans 4.

API-Based Scrapers: Benefits and Drawbacks

APIs (Application Programming Interfaces) provide a structured way to access and interact with data hosted on web servers. API-based scrapers utilize these interfaces to retrieve data directly, bypassing the need to scrape web pages manually. This method is widely used for extracting information from platforms like

 

 

  1. What do you understand by data wrangling? What steps or actions come into data wrangling in the industry?

Ans 5.

Understanding Data Wrangling and Its Steps in the Industry

What is Data Wrangling?

Data wrangling, also known as data munging, is the process of cleaning, organizing, and transforming raw data into a usable format for analysis. It is a critical step in the data pipeline, as raw data collected from various sources often contains inconsistencies, errors, and irrelevant information. Data wrangling ensures that the data is accurate, consistent, and structured, enabling meaningful

 

  1. What is the importance of data quality in making decisions? What measures can be taken to improve the quality of data?

Ans 6.

Importance of Data Quality in Decision-Making

Any industry needs high-quality data for decision-making. Using erroneous, incomplete, or inconsistent data can cause errors, inefficiencies, and financial losses. For educated judgments, trend prediction, and business process optimization, high-quality data is essential.

Healthcare diagnosis and treatment require precise patient data. In finance, reliable data ensures accurate risk