Train Your Employees in Data Analytics Using E-Learning Platforms

Train Your Employees in Data Analytics Using E Learning Platforms | Jigsaw

As information and computer technologies have proliferated, so has the need for experts who can optimise and operate these technologies. Nowhere is this more apparent than in the field of data analytics. A recent jobs report published by the World Economic Forum stated that over a 5-year period (2015-2020) technological disruptions world-wide would result in the loss of some white-collar jobs (like office administration), and a spike in others (like data science). These predictions are borne out by a meteoric surge in demand for data scientists that has far outstripped the current supply.


As S Anand, Gramener’s CEO and Chief Data Scientist says, there is a persistent need” for trained people in the field of Data Science. Hiring from colleges is not feasible, because not many colleges have an analytics course to begin with. Hiring laterally from the industry has its challenges as well since it is not easy to find people with the unique combination of skills that is needed in business today. Professionals who wish to learn analytics or augment analytics skill-sets, are forced to put their careers on hold to enrol in one of a handful of courses being offered by universities or degree programs.


Luckily, a viable alternative for analytics-hungry graduates, professionals, and companies has emerged: online learning platforms that specialize in analytics training.


The world of e-learning

Digital learning platforms, and e-learning more generally, are part of a long-standing tradition of ‘student-led’, computer-centred learning. A critical moment in the history of contemporary e-learning was the introduction of a computer-based training program at the University of Illinois in 1960 named PLATO (Programmed Logic for Automatic Teaching Operations). By “automating individual instruction”, PLATO facilitated individual-led learning, a mode of instruction that emphasizes, “[S]elf-pacing, mastery before advancement, high-quality materials, tutoring help, prompt feedback, and practice testing”. PLATO innovated interactive features that have become characteristic of e-learning as well as of IT more broadly, such as e-mails, message boards, online-testing, and gaming programs.


Today’s e-learning platforms have successfully operationalised these features, particularly with respect to data analytics training.


E-learning in analytics

In light of the steep and growing ‘supply-demand gap’ mentioned earlier, it is evident that solutions need to be long-term, medium-term, and short-term. Tomorrow’s analysts must be trained today, to ensure long-term supply.


But what about today’s professionals with backgrounds in ‘data science-adjacent’ disciplines like statistics and computer science?  Wouldn’t it make sense to build on and upgrade their existing skillsets to ensure medium and short-term supply of data scientists? E-learning platforms are doing just that.


Here are some reasons why online platforms are an effective, viable and time-sensitive way to train future data scientists and analysts.


  • Anytime, anywhere learning: One of the biggest challenges of classroom training programs in companies lies in taking people away from their work and their deliverables, and insisting that they spend days at a stretch in the classroom. Much like how ATMs revolutionised cash withdrawal, e-learning platforms have transformed how students access course content. As long as they have internet connectivity they can ‘take classes’ anytime they want, anywhere in the world.


  • Self-paced learning: In a typical classroom environment, the pace of learning is determined by the faculty and based on the average class capabilities. E-learning allows learners to set their own pace and create a study schedule that takes into account their current knowledge base and learning capabilities.


  • Microlearning: This refers to the process of consuming course content in the form of compact, concise bundles of information. Lessons are condensed or reduced to their key takeaways and packaged as videos or as presentations. Learners can decide to review the lesson in depth at a later time, or not at all, while still getting the gist of what was covered.


  • Gamification: Applying game design and principles to non-game contexts so as to maximise the learner’s enjoyment of and engagement with the content, is known as gamification. Gamification capitalises on the idea of positive reinforcement. When the learner gets an answer right, they might win a reward of some kind. This gives them the incentive to keep trying, especially when the subject matter—data analytics in this case—is inherently challenging. Features like avatars, points and badges make the learning process immersive such that course work feel less like work, and more like an adventure. Along with leader boards, gamification introduces engagement through the element of competition.


  • Content customization: Part of the charm of a learning management platform is its ability to personalise the syllabus for a learner. The platform ‘learns’ his/ her interests and inclinations and recommends relevant course material. This might render the learning process non-linear, but what this process does is to reinforce and solidify a learner’s understanding of a concept and its cross-topical applicability.


  • Social learning: Peer-teaching through social collaboration is another advantage of a shared learning platform. Just as gamification transforms the learning process into a reward-based ‘quest’, the incorporation of localised tools like forums give students the chance to tackle complex assignments like case studies together. They can mentor and guide each other, thus making the learning process less solitary, and more interactive/collaborative.


  • Cohort programs: Learner-generated content like completed assignments or solutions to analytics problems can be uploaded to the learning platform. Course-takers turn into cohorts who can assess and evaluate each other’s approaches to problem-solving, and share interesting tips and suggestions that can be collated for future reference. This teaches students to crowd-source ideas, in the process training them to work in teams.


  • Connecting students to experts: E-learning platforms serve as hubs that connect experts with students. Inputs from varied analytics professionals can give learners real-world insights that they might not otherwise have. It also gives them a chance to cultivate long-term—and long-distance, as the case may be—mentor-mentee relationships that would otherwise be geographically impossible to have.


Today’s fast-paced world is in a constant state of technological disruption; one in which big data reigns supreme. Rapid upskilling in data analytics can give individuals and companies the competitive edge they need to succeed. E-learning platforms provide overextended employees and professionals with the most flexible and specialised instruction available on the market, at a pace of their choosing, and regardless of where they’re located.

To learn more about how to build a data-centric team, CLICK HERE.