{"id":20434777,"url":"https://github.com/netcodez/analysing-unicorn-companies---sql","last_synced_at":"2025-09-12T06:44:43.442Z","repository":{"id":206889637,"uuid":"717917301","full_name":"Netcodez/Analysing-Unicorn-Companies---SQL","owner":"Netcodez","description":"Analysing Unicorn Companies using SQL","archived":false,"fork":false,"pushed_at":"2023-11-13T01:10:04.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-07-12T05:25:24.043Z","etag":null,"topics":["data-analysis","data-structures","database","postresql","sql"],"latest_commit_sha":null,"homepage":"https://netcodez.github.io/Analysing-Unicorn-Companies---SQL/","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Netcodez.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-11-13T00:57:36.000Z","updated_at":"2023-11-13T01:12:03.000Z","dependencies_parsed_at":"2023-11-13T02:25:55.819Z","dependency_job_id":"8d5c53b6-f03e-4c0a-8e6e-60dbf5f604be","html_url":"https://github.com/Netcodez/Analysing-Unicorn-Companies---SQL","commit_stats":null,"previous_names":["netcodez/analysing-unicorn-companies---sql"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Netcodez/Analysing-Unicorn-Companies---SQL","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Netcodez%2FAnalysing-Unicorn-Companies---SQL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Netcodez%2FAnalysing-Unicorn-Companies---SQL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Netcodez%2FAnalysing-Unicorn-Companies---SQL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Netcodez%2FAnalysing-Unicorn-Companies---SQL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Netcodez","download_url":"https://codeload.github.com/Netcodez/Analysing-Unicorn-Companies---SQL/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Netcodez%2FAnalysing-Unicorn-Companies---SQL/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274770538,"owners_count":25346211,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-12T02:00:09.324Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-structures","database","postresql","sql"],"created_at":"2024-11-15T08:28:53.610Z","updated_at":"2025-09-12T06:44:43.392Z","avatar_url":"https://github.com/Netcodez.png","language":"Jupyter Notebook","readme":"# Analysing-Unicorn-Companies---SQL\n\n## Analysing Unicorn Companies using SQL\n\n### Investment Trends Analysis\nInvestment firms are constantly seeking insights into trends among high-growth companies to make informed decisions about their portfolios. Investment firms can leverage this information to gain a competitive edge in understanding industry trends and structuring their portfolios strategically. This project involves analyzing a PostgreSQL database containing information about unicorn companies, their valuations, funding, industries, and other relevant details.\n\n### Database Structure\nThe PostgreSQL database consists of the following tables:\n\n- **dates**\n  - company_id: A unique ID for the company.\n  - date_joined: The date the company became a unicorn.\n  - year_founded: The year the company was founded.\n\n- **funding**\n  - company_id: A unique ID for the company.\n  - valuation: Company value in US dollars.\n  - funding: The amount of funding raised in US dollars.\n  - select_investors: A list of key investors in the company.\n\n- **industries**\n  - company_id: A unique ID for the company.\n  - industry: The industry in which the company operates.\n\n- **companies**\n  - company_id: A unique ID for the company.\n  - company: The name of the company.\n  - city: The city where the company is headquartered.\n  - country: The country where the company is headquartered.\n  - continent: The continent where the company is headquartered.\n\n### Results\nThe SQL query results provide valuable insights into the trends among high-growth companies (unicorns) across different industries for the years 2019, 2020, and 2021. Here's an interpretation of the key information:\n\n#### Industries with the Highest Number of Unicorns:\n**E-commerce \u0026 Direct-to-Consumer:**\n- In 2019, there were 12 unicorn companies in this industry.\n- In 2020, the number increased to 16.\n- By 2021, there are 47 unicorn companies.\n\n**Fintech:**\n- 2019 saw 20 unicorn companies in the fintech sector.\n- In 2020, the number remained high at 15.\n- However, in 2021, there is a significant surge to 138 unicorn companies, indicating substantial growth in this industry.\n\n**Internet Software \u0026 Services:**\n- In 2019, there were 13 unicorn companies in this industry.\n- The number increased to 20 in 2020.\n- In 2021, it remains a strong industry with 119 unicorn companies.\n\n#### Average Valuation (in Billions) of Unicorn Companies:\n**Fintech:**\n- Fintech companies had the highest average valuation of $6.80 billion in 2019.\n- In 2020, the average valuation was $4.33 billion.\n- In 2021, it decreased to $2.75 billion, but it remains a lucrative industry.\n\n**Internet Software \u0026 Services:**\n- This industry had a notable average valuation of $4.23 billion in 2019.\n- In 2020, the average increased to $4.35 billion.\n- By 2021, the average valuation decreased to $2.15 billion, but it continues to be a significant player.\n\n**E-commerce \u0026 Direct-to-Consumer:**\n- E-commerce had an average valuation of $2.58 billion in 2019.\n- In 2020, the average increased to $4.00 billion.\n- By 2021, the average valuation slightly decreased to $2.47 billion.\n\n#### Key Observations:\n- **Fintech Dominance:** Fintech emerged as a dominant industry in terms of the number of unicorn companies and high average valuations. The significant increase in the number of fintech unicorns in 2021 suggests a robust and growing sector.\n\n- **Internet Software \u0026 Services Stability:** While the number of companies in this industry increased, the average valuation remained relatively stable. It indicates a consistent presence and valuation within this sector.\n\n- **E-commerce \u0026 Direct-to-Consumer Growth:** The e-commerce industry witnessed substantial growth in the number of unicorn companies, particularly in 2021. Despite a slight decrease in average valuation, the industry remains dynamic.\n\nThese insights are crucial for investment firms, providing a comprehensive understanding of industry trends and assisting in strategic decision-making for portfolio structuring.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetcodez%2Fanalysing-unicorn-companies---sql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnetcodez%2Fanalysing-unicorn-companies---sql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnetcodez%2Fanalysing-unicorn-companies---sql/lists"}