Learning data science can be complicated and intimidating. Let alone learning data science, starting a data science company can be very overwhelming. Before you can launch a data science startup, you need to be a data scientist; else you will be joining in as an investor. There are lots of questions you have to answer to get everything right.
Which tools and languages do you need to learn? Is it R or Python? What kind of techniques do you need to focus on and how much statistics must you learn? These are just some of the critical questions you have to get right from the onset. We have created this guide to help those who want to come up with a data science or data analytics startup.
In the data science world, there are many different roles, and the first thing you need is to choose the right role. These roles include a machine learning expert, a data visualization expert, a data engineer, data scientist among others. Your background and experience will help you fall into the right category. It would be difficult and costly to shift from one category to the next. If you are not sure about what to choose, talk to people in the industry who understand these roles well.
Services to Offer
If you want to start a data science company, you must provide analysis services to help solve your customer needs. You can sell your services to one customer, and if you deliver quality services, the next customer will come calling. You do not need to worry about getting other customers, and the kind of work you provide with the first customer determines how your success will roll out.
Get the Right Team
Running and delivering on the services required in a typical data science company is never an easy work. The market for data science is full and ripe but suffers skilled personnel. There are not enough qualified people to take up data science roles. You need to take time to assemble your ideal team that can deliver in an overwhelming environment. Take your team to task, to keep them motivated and always ready to learn new and emerging technologies.
The customer is king, and their satisfaction will play key between your success and failure. When starting out, you don’t need to worry about growing your customers from the offset. What you need is to pitch to one client, bring them on-board and work to solve the problems they are facing. And since many similar clients are facing same issues, words will spread like wildfire that you can deliver on your promises. Referrals are one of the best and effortless ways to grow clients.
You can hardly survive in the data science world without funding. You will need to invest a substantial amount of money in building your data infrastructure and centers. You will also spend heavily on product development and testing. Marketing budget equally deserves its share. You will also need to pay your employees, be it in monthly retainers or to have some equity in your company. To run all these issues comfortably, you need to look for investors ready to finance your business.