Soft Skills and Hard Skills in Data Science Image: Intellignos

When I began working in data, I used to think that it was all about the tools and algorithms. Now, I see the data science role as including some management consultancy. Recently, few authors have written some articles and books on the significance of training data scientists. And, if you are a data scientist, the significance of training yourself in asking the correct questions. These skills are cultivated rarely by an education on mathematics or statistics. So, I am going to discuss a few methods that will help you in developing these “soft” skills:

1. Business Skills Training

Few universities have business skills training in their programs, and they are a mixed bag according to my experience. These also enable you to ask the right questions and practice the customer facing and stakeholder management skills that you need whether you are a data scientist at PwC, Sony or a startup.

2. Consultancy clubs

Few universities have consultancy clubs too - usually where you go through some sort of training on the leadership or you discuss on the business cases. The training for case interviews, training in communication skills, networking and elevator pitch training are also included in these events. A good example for this is McGill Consulting club

3. A business case book

I would have laughed at this as a good advice for the data scientist but I think of reading one of these books now. Discussing with others is useful for developing the ‘consulting’ skillset of the data science. I feel that it is extremely important to be able to talk with the senior management in their language which is not often statistics. This means understanding a lot of things including their strategic objectives, how they develop a mental model of their organizations and their fears and hopes. Practicing the business cases is extremely beneficial for this.

4. Finance for non-Finance types

Any good introduction on accountancy or an online course would be a useful training. The reason for mentioning this is that there is an unfortunate stereotype in the business against the technical people and accountancy is the language of business. You must understand the language to succeed in the business.

5. Innovative Marketing

Depending on the team in which you are working, you should learn some of their specialist vocabulary like understanding what metrics the marketing team utilizes and how these are calculated

6. Business development

Another aspect that the data scientists at rapidly growing companies need to understand is how to combine data-driven with the development and growth of the organization. I suggest the “Lean Startup” books for these topics.

The significance of acquiring these soft skills cannot be underestimated. Many people around the world are striving to gain high-level expertise in data science and taking Data Science Training for building a career in this field. This is because of the salary and job trends of this area and the latest innovations coming up day-by-day. This field thus promises a lucrative career for the data science professionals and the job seekers who are taking up data science as a career.