Unicorn Ventures

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Revision as of 19:32, 14 October 2018 by Yu.fu.2015 (talk | contribs)
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PROJECT GROUP

 

TEAM

 

PROPOSAL

 

POSTER

 

APPLICATION

 

RESEARCH PAPER

 

Version 1 | Version 2

INTRODUCTION


Blockchain, artificial intelligence, data science, edtech and internet-of-things are all buzzwords today in this new innovation era. More and more founders and investors begin to see the potential of innovation in Asia today. Meanwhile, local governments in the region have introduced new policies and initiatives to explore new technological innovation frontiers in order to boost the competitiveness of various knowledge-based industries. In recent years, both Hong Kong and Singapore government has pumped in resources including start-up clusters, grants and funding to boost its start-up Ecosystem.

The two Asian-Tigers, Singapore and Hong Kong, will be the main contexts for this project. They are two high-growth metropolitan city-states in Asia that share many characteristics in common in terms of GDP per capita and population density. Beyond that, both Hong Kong and Singapore offers a comprehensive financial and technical infrastructure and has attracted a considerable amount of foreign investment. It is commendable that both countries have achieved stellar economic performance despite the lack of natural resources and large land size. Unicorn Ventures strives to study the current start-up ecosystem in these two city-based regions and hope to generate new insights for policy-makers, founders and investors.

MOTIVATION


Our research is motivated by the lack of comprehensive comparison between these two countries’ startup ecosystem. Currently, there are many sources of fragmented datasets on different aspects of the startup ecosystem such as top-funded companies, industry breakdown and funding analysis. Our project aims to consolidate the data sources and study the two different startup ecosystems through a centralized interactive web application.

This project will focus on start-up companies and funding organizations in the tech ecosystem. The insights generated could help:

  • Hong Kong and Singapore policy makers improve its existing infrastructures or policies to cultivate a more robust startup ecosystem
  • Potential and current founders to understand the growing industries, competitor landscape and investors
  • Potential investors to identify the growing industries and dominant players


OBJECTIVES


Our project aims to explore and compare the following aspects for the startup ecosystem in Singapore and Hong Kong by considering the startups founded after 2000.

Ecosystem Index Analysis:

  • What is the difference of Hong Kong and Singapore in terms of Global Entrepreneurship Index and Global Competitiveness Index?

Startup Analysis:

  • Time-series analysis for startups that has formed/exited across the years by different industries
  • What is the current breakdown of startups by industries, key industries, team size, funding stage, age and gender of founders?

Funding Analysis:

  • What are top funded startups and their funding stages and industries?
  • Time-series analysis of the disclosed funding over the years by industries
  • Where does the investors originate from and what are the investor types?


SELECTED DATASET
Dataset

Global Entrepreneurship Index Score in Singapore and Hong Kong in 2018

  • Description: The GEI measures both the quality of entrepreneurship in a country and the extent and depth of the supporting entrepreneurial ecosystem. The GEI consistsof 3 sub-indices including entrepreneurial attitudes, entrepreneurial abilities and entrepreneurial aspirations.
  • Source: Global Entrepreneurship and Development Institute
  • Dateset
  • Components:
Sub-index Attributes
Attitudes Sub-index
  • Opportunity Perception
  • Startup Skills
  • Risk Acceptance
  • Networking
  • Cultural Support
Abilities Sub-index
  • Opportunity Perception
  • Technology Absorption
  • Human Capital
  • Competition
Aspiration Sub-index
  • Product Innovation
  • Process Innovation
  • High Growth
  • Internationalization
  • Risk Capital

Global Competitiveness Index 2017-2018 for Singapore and Hong Kong

  • Description: It measures national competitiveness which is defined as the set of institutions, policies and factors that determine the level of productivity.
  • Source: World Economic Forum
  • Dataset
  • Components:
Sub-index Attributes
Basic Requirements Sub-index
  • Institutions
  • Infrastructure
  • Macroeconomic environment
  • Health and primary education
Efficiency Enhancers Sub-index
  • Higher education and training
  • Goods market efficiency
  • Labour market efficiency
  • Financial market development
  • Technological readiness
  • Market Size
Innovation and Sophistication Factors Sub-index
  • Business sophistication
  • Innovation

Startup Information in Singapore and Hong Kong

  • Description: This dataset includes various key attributes on startups in Singapore and Hong Kong that was founded after 2000.
  • Source: Crunchbase
  • Dataset
  • Components:
Information Type Attributes
Basic Information
  • Organization Name
  • Categories
  • Category Groups
  • Headquarters Location
  • Operating Status
  • Founded Date
  • Exit Date
  • Closed Date
Team
  • Number of Founders
  • Female_Founder
  • Founders
  • Number of Employees
Funding
  • Number of Funding Rounds
  • Funding Status
  • Last Funding Date
  • Last Funding Amount Currency (in USD)
  • Last Funding Type
  • Last Equity Funding Amount Currency (in USD)
  • Last Equity Funding Type
  • Total Equity Funding Amount Currency (in USD)
  • Total Funding Amount Currency (in USD)
  • Number of Lead Investors
  • Number of Investors
  • Number of Acquisition
  • Acquisition Status
IPO and Stock Price
  • IPO Status
  • IPO Date
  • Stock Symbol
  • Stock Exchange

Investment and Funding Information in Singapore and Hong Kong

  • Description: This dataset details the individual disclosed funding transactions that are public and are published in crunchbase.
  • Source: Crunchbase
  • Dataset
  • Components:
Information Type Attributes
Deal Information
  • Transaction Name
  • startup Organization Name
  • Funding Type
  • Money Raised Currency (in USD)
  • Announced Date
  • Funding Stage
  • Equity Only Funding
Investors
  • Number of Investors
  • Number of Partner Investors

Investor Information in Singapore and Hong Kong

  • Description: This dataset shows the current breakdown of the profile of the investors that had invested in Singapore/Hong Kong startups. However, this data set does not disclose the individual transaction due to privacy policy .
Information Type Attributes
Basic Information
  • Organization/Person Name
  • Location
  • Region
  • Primary Job Title
  • Number of Organizations Founded
  • Primary Organization
Investment
  • Number of Investments
  • Number of Portfolio Organizations
  • Number of Partner Investments
  • Number of Lead Investments
  • Number of Exits
  • Number of Exits (IPO)
  • Investment Stage
  • Investor Type
  • Investor’s Category Group
  • Investor’s Category
DATA MODEL

BACKGROUND SURVEY OF RELATED WORKS
Related Works What We Can Learn


SKETCHES STORYBOARD
Sketches How Analyst Can Conduct Analysis

ARCHITECTURE DIAGRAM


Technology diagram VA unicorn.png


KEY TECHNICAL CHALLENGES


Domain Knowledge Understanding:

  • As the datasets involves many technical terms in the startup ecosystem, the group has to study more in-depth on the terminologies used in the ecosystem in order to draw meaningful insights. This includes the definition of different funding stages, types of fundings, types of investors, startup industries categories and etc.

Data Preprocessing:

  • Missing data: how to deal with missing values
  • Data integration and calculation: understanding the column attributes and perform meaningful summation or calculation
  • Multiple values for certain observations: how to deal with such attributes and ensure that the visualizations take into account of start-ups that has attributes with multiple values

Technological Expertise:

  • Learning relevant packages under R such as ggplot2, tidyverse, shiny and plotly
  • Learning how to integrate D3.js with R to achieve both advanced analytics functions as well as interactive visualization
  • Learning integration of different charts and enhance the interactivity and animation techniques of the storyboard


PROJECT TIMELINE


Project milestones unicorn.png


REFERENCES


*Global Entrepreneurship Index


COMMENTS

Feel free to comments, suggestions and feedbacks to help us improve our project!:D