Since the pandemic began, we launched 13 Fund to research high-leverage solutions to local issues, and new nonprofits led by under-represented groups to address them.
Family and immigrant-owned restaurants have been hit especially hard last year. So we focused our initial research on assessing how to help. We identified a range of causes — falling sales, limited access to capital, rent debt, and labor flight.
Labor flight was the least addressed challenge facing restaurants. 30% of restaurant workers are undocumented. On top of lack of access to healthcare and financial security, their status meant they did not qualify for federal…
One year ago, my startup was acquired by Amplitude. I received several offers from companies private and public, and had to navigate the strenuous process a day at a time.
After closing I realized the acquisition process actually follows a fairly consistent protocol. I documented it for other YC founders, and directly helped three of them through nine-figure acquisitions. I’m open-sourcing the protocol now for anyone who may be navigating an acquisition for the first time.
Most acquisitions happen due to an acquirer wanting one of three assets: your team, your product, or your revenue. …
Restaurants are an essential part of our cities.
From family outings to date nights, corporate outings to conventions — restaurants form an important component of our social lives. In San Francisco and New York, they provide livelihood for hundreds of thousands, often immigrants of Asian, Black, and Latinx descent.
When COVID hit in March 2020 and shelter in place (SIP) orders took effect, restaurants took a huge hit. One in three restaurants were estimated to close in the US last year. Revenue fell to 30% of the same levels last year. …
One of the most common analyses we perform is to look for patterns in data. What market segments can we divide our customers into? How do we find clusters of individuals in a network of users?
It’s possible to answer these questions with Machine Learning. Even when you don’t know which specific segments to look for, or have unstructured data, you can use a variety of techniques to algorithmically find emergent patterns in your data and properly segment or classify outcomes.
In this post, we’ll walk through one such algorithm called K-Means Clustering, how to measure its efficacy, and how…
There are a number of machine learning models to choose from. We can use Linear Regression to predict a value, Logistic Regression to classify distinct outcomes, and Neural Networks to model non-linear behaviors.
When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. But even if this model can accurately predict a value from historical data, how do we know it will work as well on new data?
Or more plainly, how do we evaluate whether…
These days we hear a lot about Artificial Neural Networks. Facebook uses them to classify different types of text in their posts. Zillow recently started using them to better predict house prices from images. Google even open sourced their technology to help any company build their own.
But what are Neural Networks? And when should you use one?
Often we want to predict discrete outcomes in our data. Can an email be designated as spam or not spam? Was a transaction fraudulent or valid?
Predicting such outcomes lends itself to a type of Supervised Machine Learning noted as Binary Classification, where you try to distinguish between two classes of outcomes.
One of the most common methods to solve for Binary Classification is called Logistic Regression. The goal of Logistic Regression is to evaluate the probability of a discrete outcome occurring, based on a set of past inputs and outcomes. …
One of the most common questions we have of our data is evaluating the value of something. How many items will we sell next month? How much does it cost to produce them? How much revenue will we make over the year?
You can often answer such questions with Machine Learning. As covered in our previous post on Supervised Machine Learning, if you have enough historical data on past outcomes, you can make such predictions on future outcomes.
Data Science is growing.
It’s been called the “sexiest job of the 21st century”, and is attracting a flood of new entrants.
Recent reports indicate that there are 11,400 data scientists who have held 60,200 data-related roles. And the overall count has grown 200% over the last 4 years, across Internet, Education, Financial Services, and Marketing industries.
And yet amidst a field growing so fast, you can observe a bit of confused exuberance. It’s not uncommon for a company to hire a data scientist just after product launch, or after Series A. …
Head of Product @ Amplitude. Founder @ 13 Fund. Alumnus of YC / Obama / Cambridge / Stanford.