Many of you who are starting out on Data Science and looking for materials on what to study, probably have come across Drew Conway’s Venn Diagram on the meaning of Data Science. I have a different version that is adapted from him and have written it in my post here.
After much thought and looking at the post from Andrew Ng with regards to setting up his Data Science team in Baidu Research (when he was still with them), I asked myself the question how can a GOOD data scientist move to being a GREAT data scientist.
One thing that came to my mind is that for anyone working in Data Science, they cannot run away from being part of a team. They have to learn how to be a good leader and a good follower as well, in order to play a good supporting role, especially when the data scientist has to communicate to both data engineers and business users. Everyone plays a part in making the team effective and efficient, the leader cannot be effective without the cooperation from the team members and team members cannot be effective without the leader to give the direction, stay focus, managing the timelines and motivating the team members.
Besides being a team player, communication skills is very important. Being able to communicate the relevant insights and in a manner that is digestible by management requires much thought to be put in. For instance, how should the presentation be structured so that the insights are easily understood.
The great data scientist would need to learn how people learn, what kind of communication medium is effective in bringing across the messages/insights, so that they are easily understood and can be used to make better decisions. That is why a lot of good data scientist actually move on to become a mentor and trainer so as to continuously sharpen their communication skills.
Looking at the above, being a good leader, a good follower and a good communicator, they all need a common “ingredient” and that is empathy, being able to put oneself in other people's shoes and think from their perspectives.
Having empathy allows one to understand which behavior are likely to be chosen, which perception are likely to be taken if the ‘story’ is presented in a certain way. This allows the data scientist to anticipate the possible outcomes and be prepared for it and also come up with successful presentation.
For instance, if a data scientist have to prepare an ad-hoc analysis, he/she should be able to anticipate the questions that the audience is likely to ask, and prepare the figures accordingly so that they are handy when the questions is asked. Being able to answer these questions from the audience, can increase the credibility of the data scientist.
So to be a good data scientist, I would recommend the person to be trained and have knowledge in the following:
And to move on from good to great data scientist, the fourth skill that is needed would be
4) People skills — Team Player, Communication Skills & Empathy
To conclude, the data scientist should not only have the ‘hard’ skills, knowledge and skills that can be picked up through books and other mediums but also need to have the ‘soft’ skills that can only be picked up through experience and practice.
Good news is both of them can be picked up in parallel. So go forth and pick them up! Start working on team projects and grab more opportunities to do presentation!
I hope the blog has been useful! If you are interested to understand what other soft skills are needed, do visit the second part here.