Key Objective:
Analyze 100 Series A and B hard tech startups to map investor networks and to improve HAX's lead sourcing efforts.
Project Description:
I analyzed 100 Series A and B startups to identify which universities and geographies to target, as well as investors that HAX should build relationships with.
Process and Techniques Used:
I compiled the dataset from PitchBook and Crunchbase by filtering and identifying high quality startups afer pre-seed rounds. I cleaned and analyzed this data in Jupyter Notebook to determine trends in HQ locations and founders' associated universities. To visualize and understand investor relationships, I created interactive network graphs using NetworkX.
I created a deck to summarize my findings and provide actionable recommendations.
Data Tools Used:
Python NumPy and Pandas, NetworkX graphs, PyVis, Pitchbook, CrunchBase
Lessons and Takeaways:
A sample size of 100 is too small to observe overarching patterns and relationships between investors. I can increase the sample size by scraping data from PitchBook and CrunchBase, but I'd need access to their API for this to be worthwhile. I'd use a simple random sample from the subset of startups in Series A and B.