The Largest Technology Firms in The World
The GitHub link for the code I used for my web scraping project can be found here: https://github.com/psharma128/scraping
INTRODUCTION:
For my web scraping project, I scraped a wikipedia article regarding the biggest technology companies in the world. I wanted to examine the tech firms that dominate the global market to gain a better idea of how they operate. Wikipedia has listed their revenue, number of employees, and what country they are headquartered in. I used python to scrape the wikipedia page and to do some data visualization.
WHAT WAS LEARNED?
• The 16 largest tech firms in the world have an annual revenue totaling more than $1.6 trillion US dollars per year and employ more than 3.8 million people worldwide
• These are all multinational firms, of which 8 are based in the USA: (Apple, Amazon, Alphabet, Microsoft, IBM, Dell, Intel, and HP)
• 3 are based in Japan (Hitachi, Sony, Panasonic).
• 2 are based in China (Huawei and JD.com.)
• 2 are based in South Korea (Samsung and LG Electronics).
• 1 is based in Taiwan (Foxconn).
A DIVERSE RANGE OF BUSINESS AREAS WITHIN TECHNOLOGY:
• While firms like HP and Dell primarily manufacture computer hardware, Microsoft primarily manufactures computer software.
• Apple manufactures software and hardware for primarily computers and cell phones.
• IBM provides consulting services to corporations in areas such as Analytics, Blockchain, Cloud Computing, etc.
• Intel is a semiconductor chip manufacturer. Amazon is an e-retailer and Alphabet is a search engine.
• Panasonic and Sony manufacture hardware primarily outside of the computer industry
IMPROVEMENTS TO THE PROJECT:
• If given more time, I would like to examine how these firms are utilizing data science to improve their business model and how many data scientists they expect to hire in the coming years.
• I would like to plot graphs highlighting the strengths of particular firms
• I would like to be able to use more complex tools and code comfortably to scrape data from bigger data sets and use what I need selectively for my presentation.
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