- James Maningo
- August 20, 2018
Fill in your Teams’ Analytical Skills Gaps Through Data Science Training
The importance of data is growing every day, and we have already reached a stage where we face talent shortages when it comes to analyzing data. This analytical skills gap appears to be a global problem, and one logical way to fill the gap is by encouraging data science training.
Shortage of Trained and Qualified Data Scientists
To offer a reasonable idea of the situation, we must identify where the gaps exist. Talent shortages and mismatches between market requirements and a supply of qualified and skilled professionals are quite visible. These gaps cause economic slowdown, and businesses can’t scale their operations; launching new products and services as well as operational expansion remain stifled.
Governments and businesses share the same view; talent shortage directly impacts competitiveness and productivity, and the way forward in a data-driven and automated world, lies in workers becoming sufficiently skilled in order to harness the potential of new technologies.
Qualifications of Existing Data Scientists
According to estimates, just one in four data scientists in the US holds a PhD qualification. Business analysts express concern regarding how future challenges will be met if at the current stage demands are not being fulfilled. However, the counterargument is that 10 years ago, no one was even talking about the importance of data analysis. That has changed, and around 3 or 4 years ago, academics began turning their attention towards this area, which is more than encouraging.
So, for the time being we might have just 25% of data scientists holding a PhD, but in a few short years this figure is set to rise. While we now know that there are more people seeking pertinent qualifications and skills in data science, companies currently still can’t seem to find people who are ready to hit the ground running. However, some smart businesses have developed their own training models to fill their teams’ analytical skill gaps.
Here’s How Companies are Bridging the Analytical Skills Gaps
To cover skill gaps, there are two approaches that are being adopted; companies either prepare existing/potential resources through some form of data science training, or they have an external entity manage this for them. The concept of training individuals for data science roles is the same in both cases; the only difference is whether companies choose to have the training done externally or internally. One thing is certain though, and that is “companies are surely better off investing in data-science training.”
Training Your Own Data Science Team
There are several aspects to consider when a company chooses to develop its own data science training. It’s obvious that each company would develop its own approach to training based on its particular requirements and perspectives. In other words, a standard approach won’t be achieved that can be practiced across an industry any time soon, and a trainee from one company might not match the requirements of a competitor in the same industry. However, training your own team of data scientists seems to be the best approach primarily because you have the capacity to facilitate a customized approach to data science in terms of training and application.
Choose the Right People to Fill Existing Gaps
Training your team ideally should begin with selecting people from within your organization who know how to code from an analytical point of view. This would help to produce reliable results aligned with your commercial aims.
Your team of data scientists must be scientists in the truest sense if you want the best results out of your investment in data science training. Ideally, you need to have the nosiest ones on your team; people who are keen to get to the bottom of everything and understand the what, why and wherefore of everything.
These people may present themselves as highly annoying, inquisitive individuals, but they are precisely the sort of people you need. Data science training would benefit people who are by nature scientists. Choosing the right people is a step you must get right if you want your training to have intended results, which largely include filling the gaps in your analytical skills gap.
Partner with or Hire Data Science Training Experts
To ensure effective data science training, your team must go through the right process, one that is aligned with your particular business needs. While you may have a 100% grasp on your company data and how it is used, you may still need help inculcating the required process and approach in your team members’ minds. You may need data science trainers to setup the training process and conduct suitable data science courses as required. Investing in the right experts can save you time, energy and frustration, and you can help your team achieve results efficiently.
Organizations that offer data science courses may each have their own methods. Some conduct courses online and may have an individualized approach. Others may offer a physical environment with a collaborative approach, while some may offer online a collaborative environment with interactivity.
If you’re looking to build a team of data scientists, it’s advisable to have your trainees placed in a collaborative environment where knowledge can be shared and where brainstorming is a norm. This encourages accountability through the entire length and breadth of the data science courses conducted, which tends to have a long-lasting impact wherein a team learns to collaborate and challenge each team member’s analysis in a professional setting. A notable benefit of training through an entire process in a collaborative environment is that gaps can be identified and filled.
The analytical gaps in your data science team may primarily lie in the right selection of individuals. When you choose to mentor individuals, who possess the right demeanor and attitude toward data science, you will notice progress. And when you place these individuals in a structured training environment akin to a professional setting, you will end up with a team that works with no stone left unturned. That’s precisely the attitude toward data that you want from a reliable team of scientists.