4 reasons to invest in open-source data science training

With the ever-changing technology landscape come lots of challenges for companies. Specially around data science, where we are witnessing a constant expansion in technologies and tools. Every company is becoming increasingly data-driven, and the best way to remain relevant in the next five years is to be prepared.

What concrete advantages does open-source data science bring to your organisation?

1. Develop your own use cases.

As the dust settles down and clear winners emerge (R & Python), organizations should get ready to jump into data science, identifying relevant use cases for their businesses. The focus is less and less on technologies and is finally moving onto bottom-line value. There is no one better prepared than your own, trusted and experienced employees to come up with the right use cases for data science in your business. You do not need an expensive consultancy, you have already the experts!

2. In-house customer support.

Although “open-source software” is understood as free, like in “free-beer”, it comes to a cost. Yes, you don’t have to pay the hefty yearly license, but you have to customize your software to your infrastructure and needs. A large chunk of the revenue for open source vendors comes from the ongoing support. The way out? Train your own technology experts.

3. Increased productivity and reduced employee turnover.

A happy employee is an employee that stays with you. Open source technologies are here to stay, and smart, valuable data scientists and data analysts want to work with those. Proprietary software limits their creativity and that would eventually make them leave. It is not pleasant to be locked down with the limited tooling that vendors offer, no matter how “easy” they make it sound.

4. Trained employees can be promoted.

Although there are signs that data science starts to be commoditized, with an data science and machine learning bootcamps and even master and PhD degrees everywhere, you can not really expect that any of such “instant experts” will be able to take a senior role. Investing in your employees now will save you money in the future when you are fully committed to embrace advanced analytics in your business, as the new joiners will find strong mentors and leaders within your team.


Interested in upgrading your analytics / open-source software in-house capabilities? Reach out!

Where is higher education going?

Having spent most of my twenties in academia to move on later into business, I have seen my fair share of both worlds, and the disconnect between them is worrying.

We are not teaching students the skills they really need. We send them unprepared to the workplace, much to the dismay of the employer, which needs to spend valuable time and resources in training them. Why are we teaching them Lisp, when the probability of using it is close to zero? I can not believe that there are that many self-branded machine learning / artificial intelligence programs without a single course in R or Python, for instance.

I’m obviously speaking from my trench (data science), but I see that happening in other fields too. For many specializations, including humanities, a decent course in spreadsheet software is long overdue. ¬†Instead, focus is lost in highly theoretical subjects, memorizing tons of stuff that are now easily available online.

We should focus on problem solving skills. Why is it taking so long for universities to catch on?