The training programs, from adult learning perspective is facing a lot if challenges. on one hand there are plenty of graduates apparently trained in Technical topics and tools. But, when hired and deployed, they are not able to deliver on the job, either with in company projects or on client projects, due to lack of required advanced knowledge and skill sets.
As a result, most university graduates are not employable at the end of 4-year program. At the same time, companies are facing a lack of resources to hire, with the right set of knowledge, skills and tools.
To meet this gap, many of the hiring companies as well as training institutions are creating training programs in order to make these graduates, job ready, on day one, of their work life.
One of the trending areas in this is Data Science and Data Analytics. Selecting the right set of candidates to train, the training methodology, the different interventions used, all play a very crucial role, in order to ensure maximum conversion from ‘training to placement’ perspective.
What are we addressing?
The article brings out the salient points of selecting the right set of participants for a given training with requisite skills and use training methodologies that enhance the participants’ areas of strength and helps in working on areas of improvement.
Most institutions take all the candidates who opt for training, run sessions for all, in a combination of multiple modes like self-paced, E-Learning, live webinar and sometimes live classroom included.
The result: the participants think they have the required knowledge and skill but are not able to pass the litmus test of the interview.
Reasons are several. These participants do not have enough training in order to deal with real world problems. Their knowledge is limited, mostly to theory. They fail miserably while applying the knowledge to solve functional problems.
Recognising key competencies
The above problem can be addressed at two levels.
The time spent on concept lectures are minimal, not more than 20 minutes on each concept.
Then the participants will work on in-class hands-on assignments using applying their concept and tool knowledge
In the next step, case studies are given to train the participant in operational problem solving. Participant understands the business problem and does critical thinking to apply the relevant and the right concept. It could be a hit or miss first few times. Hence working on several different overarching, domain specific case studies, will help in fine tuning the process.
The student also guided to connect the dots across different topics (concepts, tools, data analysis, using the right algorithm) and be able to identify and apply all the different elements in an integrated manner to address the overall business problem. To achieve this, they will work on overarching case studies in relevant industry domains.
At this point they are almost ready to take part in hackathon/ Datathon, both internal challenges as well as external. Initial failures will guide them to prepare better and face the challenges in a more prepared manner and better mindset.
Since the participants are together in a classroom, a lot of social learning happens with several different solutions options being discussed.
We have also observed that sometimes, participants are not able to make appropriate connections between knowledge and the tools used. In this regard, one suggested experiment is to adopt a bottom-up learning approach.
We start the learning intervention with a real-world problem and challenge the participants to solve it. As different solutions emerge, in addition to related concepts, technology, tools and implementation methodologies, the facilitator helps to decide between the different options by focusing on the pros and cons. This forces the participants to critically think through the problem. They also now know the importance of the concepts and tools that they must learn as they are aware of the big picture and perceive the how and why of the different elements of a solution design.
Along with Technical and conceptual skills, articulation and story-telling skills in English language, is essential to successfully pass client interviews. For Indian students for whom English is not a mother tongue, they cannot express their thoughts with clarity, when questioned by interviewers.
Hence an intervention on English articulation is required, supported by mock interviews by experts.
It is important that the participants be trained to answer the different interviews and respond appropriately. For example, technical panel, functional business head and HR person. This comes by developing the capability to understand the interview panel portfolio, their intention in asking the question and then providing appropriate response.
It is important to use technical terms and tools in a technical interview. They should also show case a small Proof of concept (PoC) they have created for an Industry problem.
For a Business analyst panel, they should have the solutioning and storytelling ability.
For the HR panel, they should talk about positive attitude, integrity, learnability, punctuality, etc.
This awareness and capability to recognise who is on the panel and respond appropriately should be developed in the participants.
Incidentally, the above-mentioned model has worked well in the Postgraduate Diploma in Data Science program (PGD-DS) program at Manipal, along with Jigsaw Academy. It has helped as achieve up to 70% placement, across batches.