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Top 6 Priorities Data Science Startup Founders Shouldn’t Ignore

Top 6 Priorities Data Science Startup Founders Shouldn’t Ignore

The number of startups in analytics and data science space has increased exponentially over the years. There are many experienced professionals and fresh graduates opting for the entrepreneurial road and have come up with innovative startups in this space. The numbers of startups that we have spoken to in the past is a testimony to the fact that there is a new data science startup stealing the limelight every other day. Based on these past interactions with the startup founders we are writing this article which goes through the essential requirements that every data science entrepreneur must consider before starting their business.


Especially in cases of analytics and data science startups, there is a lot of data, data models, data analysis and more that we are talking about. Data science entrepreneurship, therefore, is more than just having an idea, planning and executing it. 

In this article, we list six crucial toolkits that data science entrepreneurs must-have before starting their entrepreneurial journey. And by toolkit we do not necessarily mean data science tools but the essential requirements inside and outside of analytics hardware and software. 

  1. Sufficient funding and backup: This is probably one of the most crucial requirements for any entrepreneur to start their business. Being financially prepared is the key and to have sufficient emergency fund is a must-have. It is not only required to survive in the difficult times but to sustain through the initial phases as when the product needs to be kept low cost, there are little or no profits,  among others. To apply for VC funding or seed funding can also only follow if the data science product has stood the test of time. 
  2. Work experience in data science domain: It can be said that you can best understand customer problems and come up with products and services if you have looked through the problems keenly. It always helps to have an industry exposure to understand what can be best made to suit the commonly faced problems. 
  3. Starting with small but efficient data science team: The headcount doesn’t really matter in the initial stages of a startup — what matters is to have an efficient team. Even if the number of data scientists initially is 2 or 3, they should be efficient enough in the roles and responsibilities given. They should also be able to do multitasking, as the initial days of the entrepreneurial journey is much more than defined roles for the employees.  
  4. Access to open-source tools and free tools: Since the initial days of startups has a tight budget, to have open and free tools is the best thing to do. Most of the important tools and libraries in data science are open source and free such as NumPy, Pandas, Jupyter Notebook, and others. Instead of going for paid tools in the initial days, these should definitely be on the list. Having free visualisation tools can also be a good addition. 
  5. Cloud services: Having access to the cloud since the beginning can help in the long run as it makes the workings much more efficient. Though it might be a little bit of an investment as most industry-famous cloud services come with a charge, it is a good-to-have thing in the list nevertheless. It can make the database storage, content delivery, and functionalities much more flexible. 
  6. Access to huge datasets: Based on the nature of the startup, for most analytics and data science startup to work up in the best form, datasets are the most crucial requirement. For developing models or ML and NLP tools or improving new algorithms can require massive amounts of data to work with. There should also be enough tools to crunch the data in the most efficient way. 


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