Build a Data Science Business
At some point you have dedicated yourself to accumulating knowledge and helping others in the field of data science. You may not have realized it, but you are laying the foundation to have your own business. In addition to a traditional corporate gig you want to build a data science business. This can be profitable, educational, and believe it or not, fun. Think about it. You can pursue professional relationships that are actually meaningful to you.
The Ideal
You can solve problems and help people. Not just any people, but the people you choose to work with. You get to boldly display all of these technical skills that you have been building for all these years.
If you are diligent, you will at least generate enough cash flow to have some flexibility in your life. If you are careful with your budget and stash some cash away in investments, you can actually get to financial independence more quickly than your peers.
The Reality
When you build a business, it means to take a risk in terms of time and money to create something of value. It is not a hobby. Hobbies don’t create a profit. At some point, others need to recognize this value and present you with money (society’s IOU for work done) that exceeds the value of the amount of time and money you risked in the first place. When your value creation process is readily reproducible, you have a business. It means an independent source of income based upon ownership of the means of production. It could mean freelancing on Fivver and/or Upwork.
I think developing a small business based on technical skills is the smart way to hustle. This requires the curiosity to purposefully learn new things. Not everyone has that, and you have to do a gut check to ensure that you do. My gut check was when I attempted side hustles such as Uber Eats and even Air BnB with varying degrees of success. What I didn’t like about those apps is that almost anyone can do the tasks assigned. That sounds great from an egalitarian standpoint but not from a capitalist standpoint. That means that competition is fundamentally stiff and the wages are literally a “race to the bottom.” Who is going to do the simple task at the lowest price? OK, you, desperate person, are the lucky recipient of less than minimum wage then. There are some success stories, and I can totally understand even a handyman with some practical skills could do passably well on something like Task Rabbit.
However, let’s say you don’t mind being in front of a screen for a few hours each day and actually enjoy solving puzzles to the point of distraction. You constantly want to learn the right way to do things. This is the essence of the grit that it takes.
Data science is particularly fascinating because of its component disciplines. These include data collection, data wrangling, data visualization, statistics, calculus, linear algebra, computer science. If any of these fields hold any interest for you at all then you are squarely in the right place.
Where Do We Go Now?
OK, so we thought about why we are pursuing data science, we named a lot of what data science consists of, but what is data science as a whole? Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains.
That’s it. It is not even restrictive or exclusive to a particular domain, such as nursing or cattle ranching. It could be both or neither of those. That is the beauty of building these analytical skills. The skills can be applied anywhere and almost every field of study, work or dreams which benefit from data. We see it over and over in entertainment, construction, medicine, and even gambling.
A friend asked me recently how the odds could be placed so precisely and game scores could be so accurately predicted. My answer was that data automates and makes accessible what experts in the subject, like Ace Rothstein in the movie ‘Casino’ used to do. It enables prediction, categorization and regression and can improve any endeavor if used properly. What this means is if you are already savvy about woodworking, international sales, small business operations or whatever then you can combine data science skills with knowledge you already have in order to have your own niche.
Here is a rough outline for what a modern, branded data science content creator could do:
-make a bad video for YouTube
-make more videos for YouTube, getting better each time
-blog for Medium, using that to cross promote your YouTube channel
-get to 10,000 subscribers
-set up a Patreon account
-monetize with advertisers and get affiliate links
-follow up with Google adsense
-write an eBook related to learning data science and cross promote it on YouTube and Medium
-start and improve a dedicated, branded Facebook page
-start and improve an Instagram page
-cross blog and write content for others to promote your brand
-teach data science online and improve your teaching profile on specific educational and freelancing sites and use it to promote your channel
-get a logo done and promote merchandise with dropshipping
-build a great course and sell it, acquiring the first 100 customers
-work like hell to impress those 100 people and one out of ten will buy your stuff and market your services for free through reviews, social media posts, etc.
Final Thoughts
You don’t have to do these in order. However, just keep in mind that these things take time. Five years is not an unreasonable time horizon. If you hit your business goals before that, consider yourself blessed.