When the need for other skills come up along with the technical skills such as data science, a number of people do not seem to be in favor of it. This is a wrong conception that people do have and this can lead to a number of problems ahead when the data scientist has already joined some organization. Data science is not just about handling and analysis of the data that has been collected. It is also about understanding the analysis of the data that you have done and also making others understand what you have analyzed.
This is the reason, communication is also one of the important data scientist skills that are required for the position of a data scientist. The data scientist should be good in communication in order to explain the analysis of the data done and make understand it in the same way how he or she has understood it. A data scientist will be experienced and well-skilled in the areas of communication is able to perform well as a data science expert and hence companies prefer to look for such candidates in comparison to those who are experts as data scientists but are not skilled in the communicative area.
There are different types of communicative skills that a data scientist should have in order to be a perfect one in handling and analyzing data for the organization. Here are some of the types of communication that the data scientists need to know about and should be experts in so that they can explain the analysis of the data in a much better way to the other people in the team or other employees of the organization.
The very first communication option that you can have is that of presentation. If you are dealing in data, you need to understand that the other team members of employees will look towards you to understand the information extracted from the data. Hence, there can be a number of times when the data scientists may have to get involved in some kind of presentations. There are different types of presentations that the data scientists may have to go for such as mentioned below.
One-in-one presentation is the communication process in which the data scientist deals with one particular person and explains the outcomes of the data analysis and answers the questions of the person in detail.
The second type of small groups where the data scientist is representing a small group of people in either a board meeting or a general meeting etc.
Classroom presentation is the next presentation type where there will be a room filled with people such as at least 40- 50 people. This type of presentation is normally not done in a conversational tone as there are a large number of people and hence it is important to make your presentation relevant and also interesting so that the candidates in the presentation can feel that engagement in whatever you are saying.
Large audiences can be a big challenge for the data scientists even than the classroom presentations. In large audiences, it will be difficult to reach out to almost everyone and hence it is important that whatever is spoken, is spoken with proper understanding and weight. If imagery is to be shown, you need to make sure that the message conveyed by you can reach to all the entity present in the audience and that too in exactly the way that you wanted to make them understand. There are a number of times when you wish to say something else while the person in front of you understands something. Make sure that your sentences are not tricky and hence are properly understandable.
Storytelling is one of the difficult things that need a lot of precision and a whole lot of creativity. A storyteller should be able to narrate everything in such a way that the people listen to the story should be able to visualize everything that the storyteller is saying. This should be the power of words that the storyteller is conveying. The data scientist should have such a talent of storytelling so that the people listening to him or she can actually see what the data scientist is seeing and feel what the data scientist is feeling.
Unlike presentations, storytelling is not about discussing a problem area and finding a solution for it. It is about describing something and providing a view of it. Hence, it should be said in such a way with the help of the insights created that the people are able to understand it carefully and are also able to gather each and every point of the story narrated by the data scientist.
When the data scientist or anyone else is getting into storytelling, it has to be considered that it should have a very attractive start that can hold up the audience and the flow should be such that can keep the audience grabbed throughout the story till the ending of the story.
The story can be told through verbal communication and also through written ways such as reports, magazine articles, and so on. Whether it is verbal or written, the basic structure of the story will be the same.
Data visualization is one of the most eminent communication options that is being used in case of data science these days. It is basically the illustration of the data in the form of pictures or even pictorial graphs. The data visualization always go hand in hand with a story. The visualization can be best understood only when the story is also laid beside it for the audiences to gain interest from the illustrations and then getting anxious enough to go through the story that actually describes the data analysis in a better way.
Other Skill Options
Apart from these main communications options explained above, there are many more other options that are used in case of data science to make people understand the data and the analysis in detail. Some of the other communication options that one can make use of are business insights, publishing skills, social media skills, and so on.
While the data scientist is trying his or her best in rendering a proper presentation of the data through different communication means, it is also important to make use of the other communication skills such as listening and thinking. If you are just speaking up your own things and are not listening to what the others have to say, you will be not able to know whether the audience has understood your point of not. Also, you need to stop and think about various points so that you can understand what you are talking and whether it is reaching out to the audience in the right way or not.
There is no such field today where communication is not needed. Data science Python may be driven by data but communication is one of the basic necessities that it has. What will be the data scientist do by analyzing the data when he or she will be not able to convey the message or the final results of the analysis to the other team members of the organization?
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