Skill your organization with AI

Data science represents a paradigm shift in how people live their lives and how they do business.

Data is everywhere and data science is trickling down into every aspect of business and life. Industries need to and are gearing up for disruption, some faster than the others as the AI wave hits them. Companies are keen on acquiring data science talent too, making data scientists one of the most in-demand jobs of the decade.

Is your company one of them? If so, it’s time you figure out if your company needs a new data science team, partner with a provider for data services, or train your existing team. In this article, we dive into training your existing team, weigh its pros and cons, and walk through the how of it.


Training on the job is one of the most understated and under-implemented but extensive types of learning. Most of the workforce is utilized 85% or more and thus skilling them while not undercutting their productivity is what we are working on. We design our corporate training in such a way as to integrate the new skill learning into their existing work plan.

Coming to our particular case at hand, corporate training in data science has its own set of merits and demerits. But the pros far outweigh the cons in the right industry. We’ll put forth the case for training the existing employees for data science adaption.


Data science is a multidisciplinary field with a wide range of applications that can be easily unlocked with the right amount of knowledge, experience, and innovation. The existing employees have knowledge about your organization, its internal processes, current clientele, and the vision. They also have industry exposure, relevant knowledge, and skills, the use cases, infrastructure, etc.

Leveraging the experience and the expertise of the existing employees can be more advantageous and give a certain edge to your organization. If your company is in the phase of procuring and implementing data science, it is crucial to have someone in the middle or upper levels of management to have a broad knowledge of data science to align your data team to the company’s requirements. If the organization picking momentum with data science, it is essential for your current employees to upskill to fit the new and evolved process flows and data-driven systems.

The way we see it, any data is pacing innovation and both organizations and individuals have to ride its wave to stay on top and up to date.


One important detail that most companies looking to recruit data scientists or for that fact, most people in general overlook is the implication of data science being an emerging field. While a lot of universities are offering data science degrees today, they started off as programs to keep up with the growing trend.

A recent study conducted by a data science community reveals that 59% of the employed data scientists have gained their skills through self-guided learning or open online courses. According to Kaggle (2017), 32% of full-time data scientists started learning machine learning or data science through a MOOC, while 27% were self-taught.

So here, if you ask us if a formal education required to be a good data scientist, we cannot help but say it is not the most essential criterion. Data scientists come from different fields and backgrounds, computer science, statistics or mathematics, data or business analysis, etc. Anyone with the relevant skillset, aptitude, and interest can find their footing in this field, even if they are not working towards becoming full-time data scientists.


“Data is everywhere, data scientists, not so much.”

IBM predicts that by 2020, there will be around 2.7 million job openings in the field of data. A job site noted a 39% increase in demand for data scientists whereas job seekers with relevant skills in data science grew about 14%. There is a tremendous gap in the demand and supply of data scientists, the stated metrics only supporting the fact. Partnering with a data science provider is both an elegant and efficient option.

Another facet of this demand-supply gap is the struggle for retention. A Financial Times article mentioned that a survey based on 64,000 developers revealed that machine learning experts topped the list of developers looking for a new job at 14.3%. Data scientists came a close second at 13.2%

The glamour of a data science job is dampened by lack of expectations and clear understanding of both the parties, the organizations lacking in data science knowledge and the data scientists lacking in the scope of the organization’s domain and landscape.


There is no case against training your existing employees in data science, just its limitations, and shortcomings.

  • For an industry that is traditionally not oriented towards technology, the employees may not be comfortable or confident at using data science even if they learn about it.
  • Your organization may still require a data team, in-house or outsource, in addition to your existing employees.

For the former, we will not insist that on data science training but to restructure your processes and teams to become data ready. For the latter, while at first glance, it looks like an additional investment, we promise you that it gives better results long term.

FIO Labs in collaboration with Algorithmica is extending corporate training to the world to help both individuals and organizations learn and adapt to data science. Here’s what we have to offer:

  • 10+ years of training experience
  • Emphasis on science more than the technologies
  • Beginning with the basics to ensure you are on par with all the math concepts
  • Interactive training so you can ask the questions right there
  • Searchable recordings and notes made available online


  • The big picture, that a data scientist alone cannot see.
  • Work disruption is low.
  • Skip the recruitment and on-boarding hassle.
  • Empowered the decision making of your employees.
  • Increase the data literacy of your organization.

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