By Chris Kay

Today it seems that everyone and their mother has a idea for a startup. It also seems that a ton of new startups are being built using data science and machine learning techniques. In fact, Mosaic Ventures estimates that one sixth of the winter 2015 Y Combinator accelerator cohort was data or machine learning driven. Not only is this a great time for startups, as the cost of getting a new company off the ground is at a record low. The accessibility of inexpensive storage and computation, as well as open source machine learning engines, makes data driven and machine learning startups their most viable.

If you have an idea for a startup project or have been thinking about joining one, here are four reasons to get connected with the local startup community:

Side Projects Are Healthy

Doing the regular 9-5 can get a little stale after a while. Having a side project or an experiment is a great way to keep things fresh and dynamic. If you have a day job you probably don’t need your side project to pay your bills, this is an awesome low risk, low pressure opportunity to experiment without fear of failure. Remember, Jeff Bezos developed the idea for Amazon while working at D.E. Shaw. Side projects also provide a diverse platform to develop new complementary skills that you can take back to the day job.

Data Scientist is One of Startups’ Most in Demand Jobs

In 2015, LinkedIn listed data mining as the second hottest job globally. In 2016, ComputerWorld named big data as the fourth hottest skill set in technology. Anecdotally from my perspective, it’s exciting to attend local data science events (listed below) and watch employers pitch the audience all of the open positions they are trying hard to fill.

Supply of graduates from the world’s best computational linguistics, machine learning and data science programmes cannot meet demand.

Machine Learning is Growing Fast & Data is it’s Life Blood

CB Insights reported that Q4 2015 was a record period for artificial intelligence (including machine learning) investment deals. As well, venture capital investment in artificial intelligence seemed to side step the slow down the rest of the industry is experiencing lately.
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When people think of machine learning they commonly think of complex algorithms. In reality, a massive quality data set is far more important. It’s been found that increasing your data set by an order of magnitude can cause top algorithms and mediocre algorithms to perform in a similar manner.

Machine learning theory states that with unlimited data, we could expect all algorithms to produce similar-quality results.”

In the machine learning world the quality, size, and granularity of the data set produce the competitive advantage.

Data is the key to AI because 1) it’s the missing ingredient — we have great algorithms and virtually endless computational resources now, and 2) it’s the proprietary ingredient—algorithms are mostly a shared resource created by the research community. Public data sets, on the other hands, are generally not very good. The good data sets either don’t exist or are privately owned.

AI is Seen as the Next Golden Age of Computing & Startup Business Models

There is no shortage of top US venture capitalists such as Matt Truck, Chris Dixon and Jim Breyer who agree that data driven, machine learning and AI startups will be the next golden age of computing and startup business models. Even Kevin Kelly, Co-Founder and first Editor of Wired said:

The business plans of the next 10,000 startups are easy to forecast: Take X and add AI. This is a big deal, and now it’s here.

Toronto Has Both Thriving Startup & Data Science Communities

So here’s (a non exhaustive list) where you can get connected to the data science and machine learning startup community in Toronto:

Meetups, Events & Hackathons

Places Hangout

Startups Doing Cool Stuff

Other Resources

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Read Chris’ previous articles

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Chris Kay is a Financial Analyst, Co-founder of Multiplicity. Follow him on Twitter – @ChrisJKay
By day, Chris is a Financial Analyst working with entrepreneurs and angel investors on their personal financial affairs.

By night, Chris is an active member of the Toronto tech start-up community. Chris is a two-time mentor at Techstars Startup Next Pre-Accelerator program, the #1 startup pre-acceleration program in the world. Prior to co-founding Multiplicity, Chris acted as the Group Manager of the Ryerson Angel Network.

Chris studied Finance at Ryerson University, and has obtained his Canadian Investment Manager (CIM) and Chartered Alternative Investment Analyst (CAIA) designations, he is also a CAIA Canada Chapter Executive based in Toronto. Chris has completed the Level 1 of the Chartered Financial Analyst program.