Entrepreneurship, Data Science, and Two Broken Bones

Otilia OTLACAN entrepreneurship, misc Leave a Comment

TL;DR Mixing entrepreneurship with data science studies is a great idea; broken bones – not so much.
Studying, quite like laughter, can be an excellent medicine!

The new school year is now well under way; unexpectedly, I do have a solid reason to celebrate school this year and run my very own – albeit unconventional – Back to School campaign.

Some background, first…

Six weeks ago, after a lovely day hiking, I managed to break a leg during a walk in the park. That’s right. Two broken bones and a long drive back home were followed by a surgery, a titanium plate with 11 screws, and a hard cast that will prevent me from walking for the next 3 months.

Seeing how my mobility has dramatically decreased, I had to make some significant changes in my schedule and had to adjust an otherwise hectic life. Having cut down on any projects that required me to be physically present somewhere, I thought long and hard how I could make the most out of these months.

The answer was rather obvious: study. Here’s what I’ve been up to!

Entrepreneurship: Launching an Innovative Business

University of Maryland, College Park (available on Coursera)

Our goal is to demystify the startup process, and to help you build the skills to identify
and act on innovative opportunities now, and in the future.

This is a specialization that I started a few weeks prior to my accident. As I completed the initial 3 courses, I decided to do the capstone project as soon as possible. The capstone project revolves around developing a comprehensive, customer-validated business model and creating an investor pitch for the concept. Selecting which idea I will use for this project was a difficult decision; I am happy to have opted for a real business idea that I’ve been toying with for several months, for two reasons:

  • at the end of the specialization, I will have built an actual business model, will have validated the idea through at least 20 interviews, will have developed a business plan and an investor pitch;
  • leading concepts are selected for viewing by experts and investors to include 500 Startups, a Silicon Valley seed fund and accelerator founded by PayPal and Google alumni.

So here I am, working on my very real, very serious project, looking forward to complete this entrepreneurship specialization (mid-November) before diving into all the work that an actual product launch involves. It feels quite daunting, bordering on scary sometimes. It’s also so very exciting and I cannot wait to announce what I am working on. If you guessed it’s something serving the digital advertising industry, you’re totally right!

Executive Data Science

Johns Hopkins University (available on Coursera)

This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content.

My doing a specialization in data science may come as a surprise but it’s something I’ve been wanting to do for a long time. It consists in 4 standalone courses before being eligible to enroll in the capstone project and, unlike those required for the entrepreneurship specialization, they are entirely self-paced.

I’ve had tons of fun and have learned immensely from the lectures ran by Roger D. Peng, Brian Caffo, and Jeff Leek – they’re also very engaged instructors who manage to put a smile on the faces of sometimes puzzled students.

Fun aside, I truly recommend data science courses to any new or aspiring entrepreneur, as even a most basic understanding of what data can do for your business can have huge benefits.

The next capstone project, required to complete the specialization, starts in January 2016. There’s still plenty of time to complete the 4 initial courses if you wish to join me!

Resources

I thought I’d share some of the books I’ve read while studying for both the entrepreneurship and the data science specialization. In no particular order, here they  are.


Predicting the success or failure of a new product

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