Tag: Startup

6 Top Startup Incubators in London that You Should Know

Lockdown is the perfect time to launch a start-up

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If you’re looking to launch a new idea, then a Startup Incubator is one of the best ways of ensuring that your idea has a realistic chance of turning into a viable business. Not only do incubators offer you basic support in terms of office space and facilities, but they also advise on direction and link with reputable investors, customers and suppliers.

However it’s not just that clear. Each Incubator has a different direction and it’s imperative for the vision of the startup to be aligned with the vision of the incubator to ensure success. Some incubators are great for deep-tech moon-shot startups, whilst others may be great for B2C style businesses.

Below, I briefly take you through 6 top incubators in London and what the general feel of what life is like there. If you think I’m missing any (or want to know more), please comment!

Photo by Austin Distel on Unsplash

Note: The deal terms, alumni and focus for incubators below were taken from fi.com

Entrepreneur First

EF is the best place in the world to find your co-founder and build a technology startup from scratch. It is a highly charged environment designed to bring people together and help them realise their potential. We pioneered this model, and over the past seven years, we’ve built more startups this way than anyone else.”

  • Website: https://www.joinef.com/
  • Sectors / Markets of Focus: Deep technology or science-based focus that can have a big impact
  • Entrepreneur First Requirements: People are chosen based on their, ambition, potential and willingness to make a significant impact. Often, EF will select people that have advanced degrees in these relevant fields (think Masters or PhD).
  • Entrepreneur First Deal Terms: Assuming you pass the first 3 months of the program, EF will invest about £80,000 for an equity of 10%. From here, EF will help to build cohesive teams and to develop ideas. Plus, the teams work closely with experienced leaders and have the opportunity to pitch to investors during Demo day.
  • Notable Entrepreneur First Alumni: Affable, Beyond, CodeREG, Creditmint, Echobox, Hydroleap, Marble, Represent, Sentient Machines.

Techstars

Techstars is the worldwide network that helps entrepreneurs succeed. Home to a thriving international community of tech, founders, innovators and investors, London is an epicentre of startup activity. Ranked as the #6 strongest startup ecosystem in the world, London is known as a powerful location for startups. Founders are flocking to London to start their business and to learn from the incredible mentor community there.

  • Website: https://www.techstars.com/
  • Sectors / Markets of Focus: All tech or tech-enabled businesses.
  • Techstars Requirements: Like EF, Techstars don’t specialise in any one field but they look for strong and balanced teams who possess a wide array of skill sets.
  • Techstars Deal Terms: Startups are offered a $100,000 convertible note; plus, Techstars contributes $20,000 USD to support living expenses during the accelerator program. Techstars receives a 6% equity until the startup raises a priced equity financing of $250,000 USD or more.
  • Notable Techstars Alumni: Coconut, EnjoyHQ, Kalo, Lifebit, Lingvist, Memgraph, Quantemplate, SwiftComply, Tenzo, TeskaLabs, TVbeat, Unmade.
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Seedcamp

We back world-class entrepreneurs before their success is known to others. We don’t focus on any one sector and we look to our founders and network to help identify Europe’s next big talent. With some of the largest raises in Europe, acquisitions by major companies and $1bn valuations, our startups go on to achieve great things.

  • Website: https://seedcamp.com/
  • Sectors / Markets of Focus: All tech or tech-enabled businesses.
  • Seedcamp Requirements: They are looking for innovative founders who are solving problems in large markets.
  • Seedcamp Deal Terms: An initial investment of £100,000 for an equity of 7.5%; plus, the possibility of co-investing in seed rounds of up to £2M. Seedcamp offers a community of fellow founders to support your startup; also, access to a network of mentors and investors.
  • Notable Seedcamp Alumni: Bloomsbury AI, Monese, Pleo, Revolut, TransferWise, Trussle, UiPath, wefox.

Bethnal Green Ventures

At Bethnal Green Ventures we help talented teams launch and scale tech for good ventures that will significantly improve millions of lives.

Founders Factory

We partner with the world’s best founders and corporates to build, fund and scale ambitious startups worldwide.

  • Website: https://foundersfactory.com/
  • Sectors / Markets of Focus: All tech or tech-enabled businesses.
  • Founders Factory Requirements: Being an entrepreneur who wants to start their business or scale their startup. “Typically, we’re looking for founders with a product in market and early traction. If you are at that stage and are ready to leverage our network and expert team, now’s the right time.”
  • Founders Factory Deal Terms: For the incubation program, Founders Factory receives a minority stake in exchange for product IP, £150,000 and 12 months of operational support; plus, they help validate ideas and build the right team. For the accelerator program, Founders Factory takes 4–8% equity for £30,000 in cash; plus, participants get access to corporate partners, VCs and an angel investment network.
  • Notable Founders Factory Alumni: ChargedUp, Kukua, LuckyTrip, Luther Systems, Sampler, Straight Teeth Direct, Vidsy.

Level39

We support fast-growth tech companies in three clear ways — giving access to world-class customers, talent and infrastructure. Through expert mentors, access to Canary Wharf’s dynamic workspace, a packed events calendar and best-in-class facilities we help businesses achieve scale.

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The start-up incubators listed above are some of the top in London and having friends who have passed through some of these, I can safely say that a place in any of these will definitely help in launching your business in the right direction.

However, there are a few things that you should bear in mind:

  1. Do you actually need investment? What are the disadvantages to getting external investors?
  2. Would you prefer to grow your company organically? What are the benefits to this?
  3. Do you have any personal contacts (or ways to hustle an introduction) that could lead to an investment without the encroachment/impact of broader investors?

Those are just a few points that you should ask yourself when approaching an Incubator. They need you just as much as you need them, so it’s important to understand the bargaining chips. However, if you are in need of the following:

  1. Contacts in the field that you wish to launch a company
  2. Financing to help develop a product or to grow into a new market
  3. Advice and industry insight that a particular incubator has within a particular domain

Then for sure, you definitely should approach an incubator.

Thanks for reading again!! Let me know if you have any questions and I’ll be happy to help.

Keep up to date with my latest work here!

PyCharm vs VSCode

Opinion

Is it time to change your IDE?

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Maybe I’m a bit behind the curve, or maybe because JetBrains have such a big hold on the Python IDE market, it became clear to me in a previous post that a lot more Python coders are using VSCode than I was expecting.

Now I’ve used a combination of PyCharm and Notebooks for a while and I’m super happy with it. I love that if I have some data I want to explore then Notebooks is pretty easy to navigate, keep track of my work and also visualise data. On the other hand, PyCharm is just a pure machine when it comes to production: it’s never let me down and helps me churn through most tasks.

I also like the fact that the makers of PyCharm (JetBrains) are not some big American Goliath (like Microsoft), but comes from a much more humble region.

Either way, Visual Studio Code (or VSCode for short) is Microsofts open-source IDE. Its initial release was in 2015 and since then (according to Stack Overflow) it’s become the most in-demand IDE.

Given the fact that I’ve never really spent much time using VSCode and what it offers, I’ve decided to put it next to PyCharm try to figure out which is better, and which should I use?

PyCharm > VSCode

One would expect that developing code would feel more natural in a purpose built IDE and as PyCharm was created with the sole purpose of coding in Python. Does that make a difference?

Let’s take the example of autocomplete support. VSCode struggles at times with autocomplete support whereas when using PyCharm, it works nearly perfectly in every instance. My personal experience of VSCode was that the autocomplete can at times work great and other times not. It’s not just me though, people on reddit complain about the same thing: it’s oddly temperamental.

Further, VSCode struggles to load extensions at times and I thought it may have been me, however, this seems to be a bit of a recurring theme as its been reported multiple times: here, and here, and here, and here, and here, and here, and the issue is still present.

Now at first, you’re thinking “Oh awesome, I can customise my VSCode to be exactly how I want” but in reality, it never works that well and you end up having to spend a lot more time trying to fix the bug and less time developing, which is something you just don’t need to worry about in PyCharm.

So for those reasons, PyCharm being native to Python and built to really capitalise on that gives it a huge edge over VSCode. However, VSCode has a lot to offer as well.

Photo by i yunmai on Unsplash

VSCode > PyCharm

First and most importantly, VSCode is free. Yup, completely. The pure editor is pretty simple and you can expand its capabilities by installing plugins. PyCharm Professional, on the other hand, isn’t exactly cheap.

There is a free version of PyCharm (called the Community Edition) but it has fewer functionalities: it doesn’t include tools for developing databases or web related things, nor does it include advanced features such as performance profiling and remote debugging. VSCode has way more functionality than the free PyCharm Community edition, so let’s keep our focus on PyCharm Professional.

Now, something that PyCharm users are aware of is how big its memory footprint is. At the upper limit, it can take up to 1.5gb in disk space and that does have a knock on effect on your coding experience. If your computer can’t handle that then it’ll take ages to load up and sometimes it’ll take a bit longer to get through basic tasks: no one likes that!

Visual Studio Code has a much smaller footprint for memory consumption and physical disk space, about 30% that of PyCharm. So as VSCode is relatively light weight, it’s a particularly good editor for smaller projects or applications, and when performing quick edits to one or more files.

Finally, people generally seem OK with having to build a custom IDE in VSCode, as compared to PyCharm which works great out of the box and you don’t really need to do much more to it. However with VSCode, you have to build it from the beginning with plugins to even get Python working on it, so users are already comfortable with upgrading its functionality with plugins. This means that these users are also thinking about further enhancements which over time, leads to more development and a better coding experience, whereas with PyCharm, it’s mostly left to JetBrains.

Which is best?

Both PyCharm and VSCode allow the community to create plugins to enhance their user experience. Both have full-blown IDE’s and really do tick all the boxes in terms of what you need and want, although, neither are entirely perfect. Both have a strong community behind them and despite VSCode not being around for as long as PyCharm, both do have fairly mature systems in terms of technical capability.

I think it ultimately comes down to you. Do you want to pay for PyCharm professional and have a more specialised experience, or, would you rather have the free VSCode experience with a little bit less specialism, but, potentially more extensibility?

So what does my gut say?

Stick to PyCharm if you only code in Python. If not, VSCode.

The decision is ultimately up to you but the IDE you use can really alter your perception and experience in a coding language. I would expect advanced programmers to be using a variety of IDE’s depending on the project in hand (not to mention to the number of languages coders jump between) so being flexible with your tools definitely makes life easier.

Despite all that: I’ll probably stick to my Jupyter Notebooks and PyCharm combination, but I’d be interested to hear from any full-time VSCode users as to why they won’t be switching any time soon!

Thanks for reading again!! Let me know if you have any questions and I’ll be happy to help.

Keep up to date with my latest work here!

The Sampling Distribution of OLS Estimators

OLS Regression on sample data [source]

Details, details: it’s all about the details!

Ordinary Least Squares (OLS) is usually the first method every student learns as they embark on a journey of statistical euphoria. It’s a method that quite simply finds the line of best fit within a two dimensional dataset. Now the assumptions behind the model, along with the derivations are widely covered online, but what isn’t actively covered is the sampling distribution of the estimator itself.

The sampling distribution is important because it informs the researcher how accurate the estimator is for a given sample size, and more so, it allows us to determine how the estimator behaves as the number of data points increase.

To determine the behaviour of the sampling distribution, let’s first derive the expectation of the estimator itself.

Expectation of OLS Estimator

Remember that the OLS Coefficient is traditionally calculated as follows:

Closed form derivation of OLS regression coefficient [source]

Where Y = XB + e. Substitute the equation of Y into the formulae above, and continue the derivation below:

The expectation of the Beta coefficient is Beta, thereby also being unbiased [source]

Again, we know that an estimate of beta has a closed form solution, where if we replace y with xb+e, you start at the first line. Deriving out as we do, and remembering that E[e]=0, then we derive that our OLS estimator Beta is unbiased.

Variance of your OLS Estimator

Now that we have an understanding of the expectation of our estimator, let’s look at the variance of our estimator.

The expectation of the beta estimator actually goes to 0 as n goes to infinity. [source]

To get to the first line you have to remember that your sample estimator (beta hat) can be expanded and simplified as follows:

where e~N(0, σ²). From this, we can also determine that E[e’e]=σ², which is a constant and can therefore move out of the equation to leave the X’s which are all multiplied together, cancel each other out to just leave the inverse of the squared X.

Ultimately, this leaves σ²/(X’X) which is asymptotically 0 as if n increases substantially, then the variance of your OLS estimator goes to 0 as σ² remains the same but (X’X) would grow exponentially.

Sampling Distribution

Now that we’ve characterised the mean and the variance of our sample estimator, we’re two-thirds of the way on determining the distribution of our OLS coefficient.

Remember that as part of the fundamental OLS assumptions, the errors in our regression equation should have a mean of zero, be stationary, and also be normally distributed: e~N(0, σ²). Remember that the OLS coefficient is simply a linear combination of these ‘disturbances’ and therefore, our OLS coefficient is therefore driven by these normal disturbances. Therefore:

Distribution of the OLS Coefficient: [source]

And there we have it! I’ve (a) derived the expectation of the OLS estimator and shown how it is also unbiased. (b) I’ve derived the variance of the sample estimator and shown how it’s asymptotically actually 0. And (c), we use the intuition behind the distribution of the error term to infer the sampling distribution of our estimator. (Note that for sample sizes greater than around 30, the sampling distribution would be approximately normal anyways because of the Central Limit Theorem).

On the whole, I hope that the reader has a much deeper awareness and understanding of their beta coefficient. The information above can be used in a powerful way to make robust estimates of relationships: moreover showing the importance of increasing the number of samples to decrease the variance of your sample estimator.

Ultimately, the insights you gain from understanding fundamental details will shape the way you think when experimenting!

Thanks for reading and hope I helped! Please message me if you need any help!

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