Council Post: Experiential Learning—An Essence To Address The Skill Gap In The Field Of Analytics And Data Science

Experiential Learning: An Essence To Address The Skill Gap In The Field Of Analytics And Data Science

Design by Experiential Learning: An Essence To Address The Skill Gap In The Field Of Analytics And Data Science

Method of teaching has been a subject of discussion and debate for a long time. The effectiveness of training and skilling has been questioned and deliberated time and again globally, irrespective of the field, stream, sector, or specialisation.

The education sector, of late, has been witnessing a move away from traditional teaching techniques. Rote learning is slowly becoming expendable, especially in practical fields like analytics and data science.

The pace at which these fields are evolving coupled with the rapidly increasing demand globally across all industry verticals has created a significant gap in the right talent supply with the skillset to apply themselves for a given business context and create impact.

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While the academic institutes, MOOCs, and the likes are doing a tremendous job in creating awareness and equipping the talent with theoretical concepts and knowledge, there is a gap that is widening around enabling the talent with the right experience to be impactful on-the-job quickly. The rising attrition of experienced talent is adding to the pressure on the system.

There is no doubt that theoretical learning is foundational for analysts and data scientists, but the work entails individuals to critically understand business problems and create innovative solutions. This demands them to be continuous prolific learners, creative thinkers, and quick problem solvers. The way to achieve these desired qualities is through learning by experience.

The learning-by-doing method allows learners to be engaged and actively participate in the learning process by working and reflecting on the projects done. This form of learning is proving to be the most effective in becoming successful in the analytics and data science landscape. We will discuss how one can and should upskill oneself through experiential learning in analytics and data science.

The Foundations

Before delving into the essence of experiential learning, there are two fundamental concepts that every aspiring analyst and data scientist must ingrain themselves with to become successful in the field.

1. Do not believe data without reasoning 

Data is the basis for your trendsetting, analysis, prediction, and business solutions. If the data is faulty, the entire project will fail. One must question the existence of the data and reason with the data to ensure its validity and quality before moving on to any other step. For instance, last year, Italy had the highest number of COVID-19 deaths at one point. But a part of this situation owed itself to every death in an Italian COVID-19 hospital being counted as a COVID-19 death, regardless of the real reason. If one were to base one’s predictions and trends on just the former statement, the results would be faulty.

 2. Do not arrive at conclusions without critically examining the data 

Complementing data reasoning this step entails examining the data and its correlation to causation. Go a step further into ensuring that the claims made by the data are backed by facts and information. For instance, citizens in the UK shop more during winter than summer. At face value, this proves seasonal consumer preferences, but in reality, winter coincides with Christmas and New Year sales, pushing customers to go on shopping sprees. Basing your analysis on the first statement would lead to an incorrect business solution.

The most fundamental aspect at which all three streams of analytics, “descriptive”, “predictive”, or “prescriptive” are built on, is the clarity around “correlation” v/s “causation”. Several of the analytics and data science applications fail to address business problems due to a lack of critical examination leading to the faulty judgment of interchanging correlation with causation and vice versa. 

Forms of Learning

The methods of teaching and learning are undergoing a significant change in the modern era. The traditional classroom approach, based on the foundations of listening to lectures and reading out of textbooks, is not proving to be successful in readying professionals for today’s workplaces. An increasing number of researchers with empirical pieces of evidence is proving the advantage of experiential learning on learners over conventional methods.

Setting the foundation for today’s classrooms, Edgar Dale’s Cone of Experience, or his Learning Pyramid (1940), illustrates how the depth of a person’s understanding depends on the medium leveraged and the senses involved in the learning process. Dale’s research identifies that direct, purposeful, or on-field experiences are the most effective method, resulting in 90 per cent retention of the information. In contrast, it revealed the least effective learning method through presented information like verbal and visual symbols.

As Dale explains, people learn best when they are present in action and learn from their experience. In the world of data science, opening up the learner’s sensory channels to interact with the information at hand is bound to produce better results. Moreover, analytics and data science are practical fields, entailing practitioners to work on models, deal with data, and make engineering decisions. For instance, a data scientist cannot learn a hackathon solution without brainstorming the possibilities or building an intelligent model right from the textbook.

Building Hard and Soft Skills

Experiential learning methodologies and their effectiveness can be illustrated through the essential skills under the hard-skill and soft-skill umbrella in the analytics and data science space. While hard skills provide a foundation for all solutions, soft skills help in creating innovative ideas and communicating them. A nurtured combination of the two is what sets apart a data scientist from their peers.

Hard Skills

The need for practitioners to be skilled in the textbook technical concepts to ensure that the best possible analytical approach and models are built, while is necessary, is not sufficient. They need to be seasoned in applying the concepts in real-life problem situations.

The way to develop application-oriented hard skills is to focus on three essential components.

1.  Applied knowledge of algorithms

While one may have mastered algorithms, it is essential to know how and when to apply them. There may be instances when one comes across a problem where conventional algorithms don’t work. One will need to be fluent in writing a new/heuristic algorithm or creative in tweaking the old ones. Applied knowledge is learned from experience, so one must practice applying oneself in the right way.

2. Translation for business context

Data scientists often work with non-tech-based business professionals to find solutions to business problems or to create incremental business impact. It is paramount for them to understand the business context and translate those to data analytics problems, followed by building the right solution to map the context for timely implementation. This process also requires translating back the solution to business stakeholders in a language that they can comprehend. This is critical not only for a successful implementation of analytical solutions but to also set the stage for continuous improvement for incremental impact. Contextualisation leads to the adoption and growth of data-driven culture within organisations.  The skills acquired by one through the experiential learning approach can help with the above endeavour.

3.  Programming skills in Python or R

Python or R can handle applications from data mining and ML algorithms to running embedded applications under one unified language. Data scientists need to be skilled in one or both programming languages to be successful in the field. The application-oriented case study-based approaches enabled through experiential learning methodologies enable one towards industry readiness with this skill. 

Soft Skills

LinkedIn’s “Future of Skills” report from 2019 that studied behavioural insights based on millions of data points from member engagement identified soft skills to have increased value in enterprises. This, they reported, is given the expanding application of new technology that is broadening the job expectations for data scientists. The data science industry focuses majorly on hard skills, but it is time we lay enough importance on developing soft skills as well. There are three soft skills that are most important for a data scientist to nurture.

 1. Critical thinking & problem-solving

Critical thinking and problem-solving skills assist data scientists in clarifying vague and broad problems. If the dataset has errors or is not understood correctly, the solution will be unsuccessful. Under the experiential learning framework, one can build these skills by participating in hackathons, building models for experimentation, or engaging with data.

 2. Effective communication

Once one has solved the problem, it is important to communicate it to the stakeholders effectively. Data scientists’ inability to communicate with stakeholders is a pressing concern within the industry. If the receiver does not understand the solution, it will not be implemented. Individuals can hone this skill by putting themselves out there, explaining solutions to non-technical people, receiving feedback on it, and working on enhancing the skill with more practice.

3. Agility & flexibility

Agility and flexibility are two skills that are increasingly becoming more important. The agile approach to working empowers data scientists to prioritise and create roadmaps based on business needs and adapt to different goals. Agile individuals are always learning and growing from new practical experiences.  


In summary, experiential learning is learning by doing with application orientation and contextualisation. The framework is poised to get wide adoption in the field of analytics and data science globally across enterprises, functions, and academia. The aspirants and practitioners in the field should benefit from the framework to be continuous and prolific learners to upskill themselves in the most effective way and be future-ready.

This article is written by a member of the AIM Leaders Council. AIM Leaders Council is an invitation-only forum of senior executives in the Data Science and Analytics industry. To check if you are eligible for a membership, please fill the form here.

Satyamoy Chatterjee
Satyamoy is a seasoned analytics professional and a hands-on leader. He has 18+ years of global experience with a deep focus on the Banking and Financial Services industry. He has spent a significant part of his career in a variety of roles in companies such as Citigroup, and GE, enabling business impact through the application of analytics and data science.

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