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Description
| SESSION | JAN-FEB 2026 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | IV |
| COURSE CODE & NAME | DADS404 DATA SCRAPING |
Assignment Set – 1
Q.1. What factors should you consider when identifying a source for data scraping? (10 Marks)
Ans 1.
Finding the correct information source can be the vital first step in every data scraping endeavor. Unskillfully chosen sources can produce inaccurate data, legal complications as well as technical hurdles, which could lead to eventually unusable information. Certain key aspects must be carefully evaluated before making a decision on a source of automated data extraction.
Data Relevance and Quality
The main consideration is whether the source has the data fields that are specific to it as well as the granularity and coverage needed for the analysis objective. The accuracy, completeness and the speed of updating
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Q.2. Why are Wikipedia pages preferred source for data scraping? Write steps to scrape data from Wikipedia page using python library BeautifulSoup. (5+5 = 10 Marks)
Ans 2.
Why Wikipedia is a Preferred Source for Data Scraping
Wikipedia is widely considered among the top and widely accessible sites for scraping data science and research for a variety of compelling motives. For one, Wikipedia offers an enormous and varied collection of subjects that cover science, history, technology, geography, culture, sports and almost every other domain of human expertise, making it an ideal repository for the creation of datasets about almost any subject. Furthermore, Wikipedia pages are publicly accessible with no authentication
Q.3. What are the advantages and disadvantages of API based Scraping? (5+5 = 10 Marks)
Ans 3.
Advantages of API-Based Scraping
API-based scraping is the collection of data through an application or site’s official Application Programming Interface rather than simply parsing raw HTML content. APIs are structured endpoints supplied by service providers exclusively for programsmatic access to data, and offer a number of advantages over
Assignment Set – 2
Q.4. Why is scraping tweets useful for data analysis? Explain the process of collecting tweets using an API from X. (5+5 = 10 Marks)
Ans 4.
Why Scraping Tweets is Useful for Data Analysis
Twitter is now being rebranded to X Twitter, is among the most popular social media networks where millions of users express opinions and share information, talk about brands, engage with celebrities and post updates to current developments in real time. The constant stream of tweets that are
Q.5. Explain how data wrangling improves the quality of data with examples. (10 Marks)
Ans 5.
Data wrangling, also called data munging or data preprocessing, is the process of cleaning, structuring, transforming to enrich raw data into a format which is accurate, consistent, and suitable for analysis and machine-learning. Raw data collected from web scraping sensors, databases APIs or manually entered data entry usually has issues, inconsistencies or even missing value, formatting and other issues that can lead to misleading or untrue analytical results If not properly
Q.6. Discuss the importance of using dplyr for preprocessing raw data. (10 Marks)
Ans 6.
dplyr is a powerful and widely used program for manipulating data in the R programming language. It was developed by Hadley Wickham as part of the tidyverse community. It is a unified user-friendly, easy to understand, and understandable grammar of manipulating data that allows preprocessing raw datasets more efficient, appealing, and less susceptible to error in comparison to the basic R