Investment Opportunity Analysis

Project Objectives

⚡⚡Spark Funds⚡⚡

1. Project Brief

Spark Funds is an asset management company. Spark Funds wants to make investments in a few companies. The CEO of Spark Funds wants to understand the global trends in investments so that she can take the investment decisions effectively.

2. Business and Data Understanding

Spark Funds has two minor constraints for investments:

• It wants to invest between 5 to 15 million USD per round of investment.
• It wants to invest only in English-speaking countries because of the ease of communication with the companies it would invest in.

3. Strategy

Spark Funds wants to invest where most other investors are investing.

4. Business objective

Identify the best sectors, countries, and a suitable investment type for making investments. The overall strategy is to invest where others are investing, implying that the 'best' sectors and countries are the ones 'where most investors are investing'.

5. Data analysis objective

Divided into 3 sub-goals:

• Investment type analysis: Comparing the typical investment amounts in the venture, seed, angel, private equity etc. so that Spark Funds can choose the type that is best suited for their strategy.

• Country analysis: Identifying the countries which have been the most heavily invested in the past. These will be Spark Funds’ favourites as well.

• Sector analysis: Understanding the distribution of investments across the eight main sectors. (Note that we are interested in the eight 'main sectors' provided in the mapping file. The two files — 'companies' and 'rounds2' — have numerous sub-sector names; hence, you will need to map each sub-sector to its main sector.)

  Proposed approach

A - Overall approach:

I utilize descriptive analytics and alternative risk-return analysis with correlation to gain valuable insights.

B - About the dataset:

In this analysis, I use 3 dataset: 'companies', 'rounds2', 'mapping'. Please view the dataset here:

1. 'rounds2'

• This dataset contains information about individual funding rounds for companies. Each record represents a single investment round for a specific company.

• Data Dictionary:

Field Name	Description	Data Type	Example
company_permalink	Unique identifier for the company that received the funding	String	/organization/-fame
funding_round_permalink	Unique identifier for the specific funding round	String	/funding-round/9a01d05418af9f794eebff7ace91f638
funding_round_type	The type of funding round (e.g., seed, Series A, Series B, etc.)	String	venture
funding_round_code	A code representing the funding round type (may differ from funding_round_type)	String	B
funded_at	The date the funding round was completed	String (format might need conversion)	5/1/2015
raised_amount_usd	The amount of money raised in the funding round in USD	Integer	10000

2. 'mapping'

• This dataset contains a mapping between sub-sectors and their corresponding main sectors. It helps categorize companies based on their broader industry.

• Data dictionary: Binary Representation (0/1) (1 for belonging, 0 for not belonging)

3. 'companies'

-This dataset contains general information about the companies themselves.

• Data Dictionary:

Field Name	Description	Data Type	Example
permalink	Unique identifier for the company	String	same as company_permalink in 'rounds2'
name	The official name of the company	String	Acme Inc
homepage_url	The company's website URL	String	https://www.acmeinc.com
category_list	A list of categories the company belongs to	List of Strings	["Software", "Cloud Computing"]
status	The current operational status of the company	String	operating
country_code	The two-letter ISO code for the country where the company is headquartered	String	US
state_code	The state or province code for the company's location	String	CA
region	The broader region within the country	String	Bay Area
city	The city where the company is headquartered	String	San Francisco
founded_at	The date the company was founded	String (format might need conversion)	2010-01-01